# Source: https://docs.skypoint.ai/docs/actions # Power Platform Connector ## Overview Power Platform Connector is a wrapper around the API of skyData that allows the services to interact with the Power Automate and click a custom flow based on actions and triggers. It provides a way for the users to connect and interact with these segments and to build dataflows and many other automated flow based functionality. ## Actions Actions are the parts of the flow that needs to be run in a systematic manner. They include operations that can help in attaining a list of tables from an instance or maybe it can produce a dynamic response based on requirement during creation of automatic flow. skyData's Power Platform Connector provides three fundamental actions. They are : 1. **Get Entities** : This operation gets entities from an instance. It returns a list of tables present in that instance. 2. **Get Item** : This operation gets an item from an entity. The output of this operation is dynamic and it entirely depends on the entity and the item that need to be extracted. 3. **Get Items From An Entity** : This operation gets items from an entity. The output of this operation is also dynamic and it entirely depends on the entity and the number of items present in that entity. # Source: https://docs.skypoint.ai/docs/adlsgen2export # Azure Data Lake Storage Gen2 Export lets you extract data from the Lakehouse and load it into Azure Data Lake Storage Gen2. Alt text ## Step 01 - Add export * Click on **Add export** on the left top corner of the screen. * Enter a Name. (Name starts with a letter and only **Letters** and **Numbers** are allowed. No Spaces) * Display name (which displays the name in the UI of the export saved). * Select credentials: * Select from existing * Create new ## Step 02 - Select from existing **In order to allow skyData access to your *Azure Data Lake Storage Gen2* account, you'll need the following details:** * **Select Dataflow** * **Account Name** (Check with Microsoft for Azure Data Lake Storage Gen2 credentials.) * **Account key** * **Container** * **TenantID** * **AppID** * **AppKey** * Click **Next** Button. ## Step 02 - Select from new **In order to allow skyData access to your *Azure Data Lake Storage Gen2* account, you'll need the following details:** * **Account Name** (Check with Microsoft for Azure Data Lake Storage Gen2 credentials.) * **Account key** * **Container** * **TenantID** * **AppID** * **AppKey** * Click **Next** Button. ## Step 03 - Entities to export Here all the Lakehouse data will be available Entity wise. * Select the entities from the list to be exported * Click **Create** and then click on three dot button and select **Run**. * The export will be started. ## Step 04 - Download Exported Data The data gets exported in the form of csv file/s that gets stored in the storage account under the specified container with the same name as the export name. To access those files, follow the steps. * Open Mircosoft Azure Storage Explorer and go the storage account selected in the second step during export. * Navigate to the selected container and click on it. * You will see a folder named **export-entity**. Double click to open the folder and you will see the exported csv file/s there. * Double click on the csv files you want to download and the download will start. # Source: https://docs.skypoint.ai/docs/agents # Using skyData Agents ## Overview skyData Studio now supports AI Agents Configuration, enabling users to create, customize, and manage AI agents directly within the platform. Users can work with multiple agents, track version history, and tailor agent behavior to their specific business needs. With the Agents feature in Studio, users can manage task-specific AI settings enhancing flexibility, control, and performance across workflows. skyData Studio provides following built-in agent templates: * General Knowledge Agent * Database Analyst (Jack) * Microsoft Copilot Studio Agent (MSA) ## How to Customize and Configure the Existing Agent templates? Navigate to the **Agents** tab within the **Applications** module on your skyData Studio dashboard. The **Manage Agents** page will appear and then select the **agent name** you'd like to configure from the panel. Alt image ### A. General Knowledge Agent The General Knowledge Agent delivers context-aware responses on a wide range of topics, providing flexible support to meet diverse business needs. From the panel, select **General Knowledge Agent**, detailed page for the agent will appear with following tabs. * Overview * Configuration Follow the steps below to configure the agent: Alt image 1. Enter the agent details, such as **Name** and **Description**. 2. Choose an **icon** from the drop-down menu to represent the agent. Alt image 3. Inside the Configuration tab, enter the **Agent instructions** in the provided space to define the agent’s behavior. The **Instructions** field defines the agent's behavior and tasks by providing clear directives for its responses and actions. 4. Click **Save Changes** to apply the changes. ### B. Database Analyst Agent (Jack) The Database Analyst (Jack) Agent specializes in querying and analyzing data, offering precise insights into structured datasets to support data-driven decision-making. From the panel, select **Database Analyst (Jack)** detailed page for the agent will appear with following tabs. * Overview * Configuration * Knowledge Follow the steps below to configure the agent: Alt image 1. Enter the agent details, such as **Name** and **Description**. 2. Choose an **icon** from the drop-down menu to represent the agent. 3. Inside the **Configuration** tab. 4. Enter the **Agent instructions** to define the agent’s behavior and analysis tasks. 5. Select **Markdown LLM** from the drop-down menu to specify the model for data analysis Alt image 6. Click on the **Knowledge** tab to continue configuring the agent. Alt image 7. To add new knowledge, click the **Add Knowledge** button located at the top right corner. Add knowledge page appears. Alt image 8. Click **AI/BI GENIE** knowledge card on page. Add source details page appears Alt image 9. Enter the **Genie Space ID**, **Personal Access Token**, and **Server Host Name** in the provided fields. 10. Then, click **Add** to add the knowledge. You can see the added knowledge in the panel under the knowledge tab. Alt image 11. To modify existing knowledge, select the desired resource and click **Edit**. 12. To delete existing knowledge, select the desired knowledge resource and click the **Delete** option at the top. 13. Click on **Save Changes** to apply the changes. ### C. Microsoft Copilot Studio Agent (MSA) The Microsoft Copilot Studio Agent (MSA) is designed to integrate with the Copilot Studio, offering powerful capabilities for task automation and process management. From the panel, select **Microsoft Copilot Studio Agent (MSA)**. detailed page for the agent will appear with following tabs. * Overview * Configuration Follow the steps below to configure the agent: Alt image 1. Enter the agent details, such as **Name** and **Description**. 2. Choose an **icon** from the drop-down menu to represent the agent. 3. Inside the Configuration tab, enter the **Agent instructions** in the provided space to define the agent’s behavior. Alt image 4. Enter the **Connection Secret** and **Connection URL** to link the agent with the relevant system or environment. 5. Click on **Save** to apply the changes. ### D. Document Data Analyst Agent (Devin) The Document Data Analyst Agent (Devin) focuses on processing and analyzing document-based data, extracting key insights from unstructured content to enhance decision-making. From the panel, select **Document Data Analyst Agent (Devin)** detailed page for the agent will appear with following tabs * Overview * Configuration * Knowledge Follow the steps below to configure the agent: Alt image 1. Enter the agent details, such as **Name** and **Description** 2. Choose an **icon** from the drop-down menu to represent the agent. 3. In the provided space, enter the **Agent instructions** to define the agent’s behavior for document processing. Alt image 4. Click on the **Knowledge** tab to continue configuring the agent. Alt image 5. To add new knowledge, click the **Add Knowledge** button located at the top right corner. Add knowledge page appears Alt image 6. Drag and drop the required file or click **Browse Document** to upload it. Alt image 7. To connect **public websites** to the agent, click on the Public Website tab. Alt image Public Website page appears. Alt image 8. Enter the website's URL. 9. Click **Add** to include the public website. 10. Click on **Save** to add the knowledge. Before configuring **SharePoint** in the Devin Agent, you must first establish a SharePoint connection. 11. Navigate to **Dataflow** and click on the **Connections** tab. 12. Click on **Setup** of SharePoint connector. Alt image The **SharePoint Setup** page appears. Alt image 13. Fill in the required fields **Connection Name,Connection Display Name, Site URL, Tenant ID,Client ID,Client Secret.** 14. Click **Validate Credentials** to verify the information provided. 15. Once validated, click **Save** to complete the connection setup. To configure SharePoint as a knowledge source for the Devin Agent, follow the steps below: 16. Click on the **SharePoint** option in the Devin Agent to securely connect to internal SharePoint data. Alt image Share point page appears. Alt image 17. Enter the **Name** and **Display name** of the Knowledge. 18. Select the **Sharepoint Connection** from the drop-down menu. 19. Add the **Storage Path.** 20. Click **Add** to save the SharePoint Knowledge configuration. 21. To modify existing knowledge, select the desired resource and click **Edit**. 22. To delete existing knowledge, select the desired knowledge resource and click the **Delete** option at the top. 23. Click **Save Changes** to apply the changes. ## How to create new Agent? Follow the steps below to create a new agent: 1. Click the **New Agent** button to add a new agent to the Copilot. Alt image Create a new agent page appears. Alt image 2. Select the **Agent type** from the drop-down. 3. Add agent details such as **Name**,**Description**. 4. Select the suitable **Icon** to represent agent from dropdown. 5. Click on **Next** to proceed. Alt image 6. Under the Configuration tab, add **Agent Instructions** to define how your agent should interpret and respond. Knowledge configuration depends on the type of agent you are creating. Based on the selected agent type, follow the steps above to configure knowledge where applicable. ## How to Enable and Disable an Agent? Follow the steps below to enable existing agents after completing the configuration based on your specific needs. 1. Click the **toggle switch** on the agent card to **enable** or **disable** the agent, as shown in the image. Alt image 2. To **edit** click on the three-dot menu on the agent card. # Source: https://docs.skypoint.ai/docs/alis # ALIS ## Overview The ALIS connector integrates ALIS (Assisted Living Integrated Solution) with skyData, bringing clinical and operational data from assisted living communities into your unified data environment. ALIS is an eHR platform designed specifically for assisted living communities, providing tools for compliance management, medication tracking, assessments, family engagement, and real-time operational support. By connecting ALIS to skyData, organizations unlock intelligent automation, predictive analytics, and smarter decision-making across their communities. ## What Data We Extract The ALIS connector brings in key clinical and operational data including: * **Residents** — Resident demographics, profiles, and admission records * **Assessments** — Clinical assessments, care plans, and evaluation data * **Medications** — Medication administration records, schedules, and compliance tracking * **Compliance** — Regulatory compliance records and audit documentation * **Staff** — Staff assignments, credentials, and activity logs ## Why It Matters Clinical and operational data from assisted living communities is essential for delivering quality care and meeting regulatory requirements. Connecting ALIS to skyData enables organizations to: * Combine resident clinical data with operational and financial metrics for complete community intelligence * Automate compliance monitoring and proactively identify regulatory risks * Enhance resident care with AI-driven insights, personalized reporting, and proactive alerts * Gain cross-community visibility into clinical outcomes, staffing patterns, and quality metrics ## Who Benefits | Role | Value | | :------------------ | :-------------------------------------------------------------------------------- | | Clinical Directors | Unified view of resident care data with predictive analytics and outcome tracking | | Compliance Officers | Automated compliance monitoring with regulatory reporting across communities | | Operations Leaders | Cross-community analytics combining clinical, workforce, and financial data | ## Get Started Contact your Skypoint representative to configure the ALIS connector for your organization. # Source: https://docs.skypoint.ai/docs/amazonredshift # Amazon Redshift In order to allow skyData access to your Amazon Redshift account, you'll need: 1. **Host Name** 2. **Database Name** 3. **Port Number** 4. **Username** 5. **Password** ## Steps to Achieve all the necessary details for your Redshift account 1. Go to the Redshift Cluster and view properties. 2. The publicly accessible option allowing instances and devices outside the VPC to connect to the database through the cluster endpoint should be **Yes**. The default value is **No**. Alt text 3. Click on the security group and go to inbound rules and click on edit inbound rules and there is a list of IPs and fill in all the detailed IPs with their details. Alt text Below is the list of IPs required to be inside the inbound rules. * 13.91.229.38 * 40.112.243.7 * 40.118.229.231 * 13.64.89.216 * 40.78.16.175 * 52.160.111.150 * 13.64.91.216 * 13.64.94.144 * 13.91.124.136 * 13.91.125.204 * 40.118.239.192 Alt text 4. The hostname required for connecting to our platform can be found under the heading **endpoint** under connection details. The hostname includes everything from the beginning of the endpoint up to the ".com". Alt text 5. The database name, port and username can be extracted under the database configuration section. Alt text 6. The password is the same that was set during the creation of the database. ## Steps to Import Data Using Amazon Redshift 1. Navigate to **Data** > **Dataflows** and click on *Add Dataflow* button present on top right side of the platform. 2. Fill in the name of the connector and click on **Next**. 3. You will see all the different types of connectors present in front of you. In the search bar present on top right, type Amazon Redshift and select the Amazon Redshift connector. Alt text 4. Next, fill in the display name and type in a brief description about the connector in the ***general section*** and then go to ***Connector Section*** to configure the connector with our platform. 5. Enter all the necessary details and click on **Connect**. It might take some time to finish up, depending upon the data size. 6. Once its completed, you will be able to see the imported entities below the Connect button. 7. Follow [this](dataflows.md) to get a better understanding of how to input data. This will be beneficial for data processing as the data will be stored in a convenient manner. 8. As the data gets integrated, we will be able to see the number of entities present in that dataflow. On successful completion of the process, the status of the dataflow will be a green check denoting that the data has been integrated and can now be taken into further usages. 9. In entities, we will be able to see the names of the different entities present in the data, the number of records present in each entity, entity type (**default: Custom**) and many more. There are filters present on top of every column which gives user the ability to search a particular entity based on some criteria. By clicking on a particular entity, we can get its details like the **attributes** defining all the different attributes present in the data with their datatypes. To know more about the entities, visit [here](https://skypointcdpdocs.z22.web.core.windows.net/docs/entities.html) 10. Next, based on the requirements, the data can be processed using the Stitch operation offered on our Platform. The data that has been collected in the previous processes is now ready for some mapping. For that, we provide the **Stitch** option that helps the user to map different entities and find correlation between entities and create a cumulative entity with maximum information on the data. The Stich involves three different types of actions which helps in refining the data. They are: * Map: Mapping involves correlating different entities in a dataflow using specific feature attributes to produce a more extractable data-form. * Match: After successful completion of Map, we are ready to match the entities. The match phase specifies how to combine your datasets into a cumulative customer profile dataset. * Merge: The final phase of Stitch is Merge where we choose and exclude attributes to merge within or mapped and matched data. Some of the attributes are automatically merged. To know more about Stitch, click [here](stitch.md) # Source: https://docs.skypoint.ai/docs/amazons3export # Amazon S3 Export lets you extract data from the Lakehouse and load it into Amazon S3. Alt text ## Step 01 - Add export * Click on **Add export** on the left top corner of the screen. * Enter a Name. (Name starts with a letter and only **Letters** and **Numbers** are allowed. No spaces) * Display name (which displays the name in the UI of the export saved). * Select Credentials * Select from existing * Create new ## Step 02 - Select from existing **In order to allow skyData access to your *Amazon S3* account, you'll need the following details:** * **Select Dataflow** * **Bucket Name** (Amazon S3 bucket name) * **Access Key ID** (Check with Amazon S3 source credentials.) * **Secret Access key** * **Region Name** * **Encryption** * Click **Next** Button ## Step 02 - Create new **In order to allow skyData access to your *Amazon S3* account, you'll need the following details:** * **Bucket Name** (Amazon S3 bucket name) * **Access key ID** (Check with Amazon S3 source credentials.) * **Secret Access Key** * **Region Name** * **Encryption** (Supported encryption values are SSE-S3, SSE-KMS, SSE-C) * Click **Next** Button. ## Step 03 - Entities to export Here all the Lakehouse data will be available Entity wise. * Select the entities from the list to be exported * Click **Create** Button. * Click on "Destinations". * Click on the "Ellipsis" under Actions. * Click on "Run". # Source: https://docs.skypoint.ai/docs/amazonsthree # Amazon S3 Dataflow lets you integrate your data from different sources. ## Step 01 - Add Dataflow * Click on **Add Dataflow** on the right top corner of the screen. * Enter a Name. (Name starts with a letter and only **Letters** and **Numbers** are allowed. No Spaces) * Click **Next**. * You will be directed to choose a **Connector**. * Search **Amazon S3** and click on it. ## Step 02 - Access Source Data In order to allow skyData access to your *Amazon S3* account, you'll need the following details: * **Access key ID** (Check with Amazon S3 source credentials.) * **Secret Access Key** * **S3 Bucket** * Navigate to the folder you want download the data from. * Click **Save**. * Once you select the **S3 Bucket** you will see a notification *Data loaded successfully*. * Data from the source will be loaded to the table with headers **File Name**, **Entity Name**, **Datetime Format**, **Delimiter**, * **First Row as Header**, **Advanced Settings**. * **File Name**
The file name will be the name of file which exist in the source of **Amazon S3**. * **Entity Name**
**Entity Name** is the unique name that is created for the data collected from the source. * **Datetime Format**
There are a number of available **Datetime Formats** and Skypoint Modern Data Stack Platform is set to automatically detect them. * **Delimiter**
**Delimiter** is the key that is used for setting up a boundary or a separator of variables in the data imported. Currently the available separators are, *Comma (,)*, *Semicolon (;)*, *Pipe (|)*, *Tab (\t)*, *Start of Heading (\u0001)* and *No Delimiter*". * **First Row as Header**
This is a checkbox, which call the first row as Header for the table sheet we are importing. The system will automatically collect the data according to the Header Contents. * **Advanced Settings**
Advanced Settings will allow you to fine tune the import process with minute details. * **Advanced Settings**
When you click on the **Advanced Settings** link, you will see a pop-up window with certain additional information. All these information are key factors of the source data and how it is organized. If you are getting any errors, please check with the data source for these additional information. * **Compression Type**
Compression type in **Advanced Settings** is the method that is used for compressing the details from the source, Amazon S3. (To identify what is the compression methods that's used, please check with your credentials.) * **Row Delimiter**
The data stream will have a separator that identify the boundaries of its flow. If there were any different separator is used in it, that information need to be changed to get more accuracy in data ingestion. * **Encoding**
Since the data is coming in data stream there is always a kind of encoding used for deciphering it. If any separate value of mentioned in the encoding process, you may need to select the appropriate encoding. Default encoding is **UTF-8**. * **Escape Character**
An escape character is a particular case of metacharacters. Which given an identification of sequence start or end. You can manually select the **Escape Character** from the dropdown. * "/" **Forward Slash** * "\\" **Backslash** * **No Escape Character** * **Quote Character**
There are three **Quote Character** types are mentioned in the advanced **Quote Character** dropdown. * (") **Double Quote** * (') **Single Quote** * **No Quote Character** ## Step 03 - *Action* Button. Action button includes specific actions on the imported lists. * **Show All**
When you select **Show All** dropdown, the list will show all the files available in the folder. * **Show Selected**
Once you click on the **Show Selected** option, the list will display only the selected files from all the data in the folder. * **Select All**
With one click you can **Select All** the files in that list imported form selected folder. * **Clear All**
If you don't want to get all the folders selected, with just one click of the **Clear All** button all the selections will be removed from the selection. ## Step 04 - Save * Click **Save** Button. * You will be taken to the Dataflow page. # Source: https://docs.skypoint.ai/docs/api # Getting started with the skyData API skyData is an API-First platform, it provides APIs to build your own applications based on your data in skyData. Details of these APIs are listed on the [skyData APIs reference](https://developer.skypointcloud.com/). They include additional information about operations, parameters, and responses. This article describes how to access the skyData APIs, create an Azure App Registration (if needed), and get started with the available client libraries. # Prerequisites Before you can start using the API, you need to do the following: Sign up for skyData account and follow the instructions on the [Skypoint Developer Portal](https://developer.skypointcloud.com/) ## Skypoint client libraries in C#, Python and NodeJS This section helps you get started using the client libraries available for the skyData APIs. All library source code and sample applications can be found on our github repo. *Please note that this feature not released yet* # Throttling & Rate Limits Throttling is the process responsible for regulating the rate at which the application processing is conducted. Throttling is a way to practically implement rate-limiting. It lets API developers control how their API is used by setting up a temporary state, allowing the API to assess each request. Rate Limits refers to controlling the amount of incoming and outgoing traffic to or from a network. In this case, the rate limit for our API is set to be 2000 requests/sec. If the number of requests get above the threshold, then the requests after the threshold will get an error message responding that the request limit has reached. # Source: https://docs.skypoint.ai/docs/augusthealth # August Health ## Overview The August Health connector integrates the August Health EHR platform with skyData, bringing clinical and resident data from senior living communities into your unified data environment. August Health is an electronic health records (EHR) platform designed for senior living communities, providing tools for resident care management, compliance tracking, medication administration, assessments, and family engagement. By connecting August Health to skyData, organizations can streamline administrative processes, enhance resident safety through real-time data synchronization, and power AI-driven care strategies. ## What Data We Extract The August Health connector brings in key clinical and operational data including: * **Residents** — Resident demographics, profiles, care levels, and admission records * **Assessments** — Clinical assessments, care plans, and evaluation data * **Medications** — Medication administration records, schedules, and compliance tracking * **Visits** — Visitor management records and engagement tracking * **Staff** — Staff assignments, care activities, and scheduling data ## Why It Matters Senior living communities depend on accurate, real-time clinical data to deliver quality care and maintain regulatory compliance. Connecting August Health to skyData enables organizations to: * Modernize workflows by unifying clinical data with operational and financial analytics * Support proactive care strategies with AI-driven insights across resident populations * Automate compliance reporting and identify risks before they become issues * Gain cross-community visibility into clinical outcomes, care quality, and resident satisfaction ## Who Benefits | Role | Value | | :------------------ | :-------------------------------------------------------------------------------- | | Clinical Directors | Unified view of resident care data with predictive analytics and outcome tracking | | Compliance Officers | Automated compliance monitoring with regulatory reporting across communities | | Operations Leaders | Cross-community analytics combining clinical, workforce, and financial data | ## Get Started Contact your Skypoint representative to configure the August Health connector for your organization. # AWS Cognito Source: https://docs.skypoint.ai/docs/awscognito Connect Amazon Web Services (AWS) Cognito to import data into Skypoint AI ## Overview AWS Cognito is a user identity and data synchronization service that provides authentication, authorization, and user management for your web and mobile apps. You can use SkyPoint AI's built-in connector for importing data from AWS Cognito. This document will guide you through the process of connecting AWS Cognito to Skypoint AI. ## Prerequisite You will need the following details to configure and import data using AWS Cognito: * [AWS account](https://aws.amazon.com/) * User Pool ID * Access key ID * Secret access key. You can refer to the InforHMS Setup Guide document to learn more about the Prerequisites. ## Import Data using AWS Cognito connector Follow the below steps to create a new dataflow for the Cognito connector: 1. In the left pane, go to **Dataflow** > **Imports**. The Dataflow window appears. Dataflow window 2. Click **New Dataflow** to create dataflow. Set Dataflow Name 3. Enter a **Name** to identify your Dataflow. The name must start with a letter and contain only letters and numbers, with no spaces between the characters. 4. Click **Next**. ## Add AWS Cognito Connector 1. In the **All categories** or **Services and apps** tab, select the **AWS Cognito** connector to start the configuration. Choose Connector 2. Also, you can use the **Search** function to find the **AWS Cognito** connector. Set Dataflow Name 3. In the **Set dataflow name**, enter the **Display Name** to identify your dataflow. 4. In the **Description** box, you can enter an explanation of the connector. 5. Click **Next**. Configuration ## To configure AWS Cognito 1. Enter your credentials such as **User Pool ID**, **Access key ID**, **Secret access key**, and **Region** to configure with the AWS Cognito connector. 2. Click **Connect**. Entity Details 3. Enter the **Table Details** to process the data. | Item | Description | | :--------- | :---------------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (All, Customer 360, or Privacy-Metadata) for each table. | | Data | Loads customer data | | Metadata | Loads Metadata | | File Name | Displays the name of the file that you imported. | | Table Name | Displays the imported table name. | After configuration, all tables that are part of the connector are listed in the **Table Details**. For a single table, it displays by default, showing information specific to the client connector and the tables it supports upon successful connection. However, for multiple tables, you can select only those tables you want to import and process. For example, to import customer data, choose the tables containing customer information such as name, email, address, and contact details. 4. Click **Save** to apply the changes. ## Run, edit, and delete the imported data 1. After saving the connection, the AWS Cognito connector appears on the Dataflow page. Also, you can see the list of created tables in the [Databases](entities.md) section. Output | Item | Description | | :----------- | :------------------------------------------------------------------------------------------ | | Name | Displays the name of the Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Table Count | Displays the number of tables. | | Created Date | Displays date of creation. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date. This date will get updated whenever you refresh the data. | | Group by | Option to view the items in a specific Group (For example, name, type, status). | 2. Select the horizontal ellipsis in the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run History**. | You can view the error message for data import failures from a data source in the Dataflow under **Run History** > **Description**. ## Overview This document will help you gather all credentials for connecting AWS Cognito with Skypoint AI. ## Prerequisite You must have the following details: * An admin access in AWS console. ## To find User pool ID Follow below steps to find the User pool ID: 1. Log in to your [AWS console](https://signin.aws.amazon.com/signin?redirect_uri=https%3A%2F%2Fconsole.aws.amazon.com%2Fconsole%2Fhome%3FhashArgs%3D%2523%26isauthcode%3Dtrue%26state%3DhashArgsFromTB_ap-northeast-1_8a684904bdeaf413\&client_id=arn%3Aaws%3Asignin%3A%3A%3Aconsole%2Fcanvas\&forceMobileApp=0\&code_challenge=le9NjcCPfq1AGl2y-OIrdj_ePPrnFUg4pqDsJNSdslk\&code_challenge_method=SHA-256). 2. Go to **Cognito > User pools**. User pool ID ## To find Access key ID Follow below process to locate your Access key: 1. Go to **IAM > Users > Security credentials**. 2. You can locate the **Access key ID** in **Access keys** section. Access key ## To locate Secret access key 1. Go to **IAM > Users > Security credentials**. 2. Click **Create access key** in the **Access keys** section. 3. Download the file containing **Access key ID** and **Secret access key**. The Secret Access Key is displayed only once, so it is recommended to download the file and store it securely. ## To identify the Region You can locate the region of data to import in the AWS console. Follow below steps: 1. Go to **AWS console > User Pools**. 2. Click **Location** drop-down list. 3. Select your desired location from the Location drop-down list. Region The selection of region is based on your AWS Cognito setup. 4. Once you select the **Region**, the **User pools** for that region is displayed along with the **User pool ID**, **Created time**, and **Last updated time**. Region selected ## Next step After completing the data import, start the [Master Data Management (MDM) - Stitch](stitch.md) process to develop a unified view of your customers. # Source: https://docs.skypoint.ai/docs/azureblobstorage # Connecting to Azure Blob Storage ## Overview Azure Blob Storage helps you to store massive amounts of unstructured data in the Blob storage. You can use skyData's built-in connector for importing data from Azure Blob Storage. This document will guide you through the process of connecting Azure Blob Storage to skyData. ## Prerequisite You will need the following details to configure and import data using [Azure Blob Storage](https://azure.microsoft.com/en-in/free/): * Storage Account Name * Account Key * Storage Path. You can refer to the Setting up Azure Blob Storage document to learn more about the Prerequisites. ## To import data using Azure Blob Storage connector Follow the below steps to create a new dataflow for the Azure Blob Storage import connector: 1. Go to **Dataflow > Imports**. 2. Click **New dataflow**. The **Set dataflow name** page appears. Alt text 3. In the **Set dataflow name** page, type dataflow name in the **Name** text area. 4. Click **Next**. The **Choose connector** page appears. Alt text ## To add Azure Blob Storage connector 1. In the **Choose connector** page, select **Azure Blob Storage** connector. You can also use the **Search** feature to find the connector. The **Azure Blob Storage** connector is located under the **Cloud** and **Data Warehousing** category.
Alt text 2. Enter **Display Name** for your dataflow in the text area. 3. Enter **Description** for your dataflow in the text area. 4. Click **Next**. The **Connect to Azure Blob Storage** page appears. Alt text ## To configure Azure Blob Storage Follow the below steps to configure the connection to **Azure Blob Storage**: 1. Enter the **Storage account name** in the text area. 2. Enter the **Account key** in the text area. 3. Click the **Folder icon** in the **Storage path** text area. Once you select the storage path, the **Table Details** columns appear. Alt text 4. Enter the **Table Details** to process the data. | Item | Description | | :------------------ | :----------------------------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Load customer data. | | Metadata | Load Metadata. | | File Name | Displays the name of the file that you imported. | | Table Name | Displays the imported table name. | | Datetime format | Displays a number of **Datetime Formats** and skyData is set to automatically detect them. | | Delimiter | Displays available separators for the variables in the imported data. | | First Row as Header | Check the box for the system to automatically collect the data according to the Header Contents. | | Advanced Settings | Select the options to fine tune the Import process with minute details. | 5. Click the **Advanced settings** for your desired file name. The **Advanced settings** pop-up appears. Alt text | Item | Description | | :--------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Compression type | Method that is used for compressing the details from source, Azure Blob Storage. | | Row delimiter | A separator that identifies the boundaries of the flow of a data stream. In case, a different separator is used in it, the information requires change for more accuracy in data ingestion. | | Encoding | As the data comes in data stream, there is a type of encoding used for deciphering it. The default encoding is **UTF-8**. | | Escape character | It is a particular case of metacharacters that is given an identification of start or end. You can manually select it from the drop-down list. | | Quote character | You can select one of the advanced Quote characters mentioned in the drop-down list. | 6. Click **Save** on the **Advanced settings** pop-up to save the advanced settings. 7. Click **Save**. ## Run, edit, and delete the imported data Once you save the connector, the Azure Blob Storage connector gets displayed in the list of tables created in the Dataflow page. Alt text | Item | Description | | :---------------- | :------------------------------------------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables. | | Created Date | Displays date of creation. | | Last refresh type | Displays the refresh value. After the last data refresh, it will indicate whether the value is Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Group by | Option to view the items in a specific Group (For example, name, type, status). | * Select the horizontal ellipsis in the Actions column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run** history. | ## Next step After completing the data import, start the [Master Data Management (MDM) - Stitch](stitch.md) process to develop a unified view of your customers. # Azure Data Lake Storage Gen2 Source: https://docs.skypoint.ai/docs/azuredatalakestoragegen2 Connect Azure Data Lake Storage Gen2 to import data into Skypoint AI ## Overview You can make Azure Storage the foundation for building enterprise data lakes on Azure with the help of [Azure Data Lake Storage Gen2](https://azure.microsoft.com/en-in/pricing/details/storage/data-lake/). You can use SkyPoint AI's built-in connector for importing data from Azure Data Lake Storage Gen2. This document will guide you through the process of connecting Azure Data Lake Storage Gen2 to Skypoint AI. ## Prerequisite You will need the following details to configure and import data using Azure Data Lake Storage Gen2 connector: * Storage account name * Account key details * Storage path. ## Import data using Azure Data Lake Storage Gen2 connector Follow the below steps to create a new dataflow for the Azure Data Lake Storage Gen2 import connector: 1. Go to **Dataflow > Imports**. 2. Click **New dataflow**. The **Set dataflow name** page appears. Set dataflow name 3. In the **Set dataflow name** page, type Dataflow name in the **Name** text area. 4. Click **Next**. The **Choose connector** page appears. Choose connector 1. Azure Data Lake Storage Gen2: Use this to import each CSV file as an entity. 2. Azure Data Lake Storage Gen2 v2: Use this to import multiple CSV files in a folder as a single entity. ## To choose Azure Data Lake Storage Gen2 connector 1. In the **Choose connector** page, select **Azure Data Lake Storage Gen2** connector. You can use the Search feature to find the connector. Also, the Azure Data Lake Storage Gen2 connector is available under both Analytics and Cloud categories. The **Set dataflow name** page appears. Set dataflow display name 2. Type a **Display Name** for your dataflow in the text area. 3. Type a **Description** for your dataflow in the text area. 4. Click **Next**. The **Configuration** page appears. ADLS Configuration ## To configure Azure Data Lake Storage Gen2 Follow the below steps to configure the connection to **Azure Data Lake Storage Gen2**: 1. Type the **Storage account name** in the text area. 2. Type the **Account key** in the text area. 3. Click the **Folder icon** in the **Storage path** text area. Once you select your Storage path, the **Table Details** columns appear. Configuration table details Upon selecting Azure Data Lake Storage Gen2 v2, **Table Details** appear as per the image below. v2 Configuration table details 4. Enter the **Table Details** to process the data. | Item | Description | | :------------------ | :----------------------------------------------------------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data. | | Metadata | Loads Metadata. | | File Name | Displays the name of the file that you imported. | | Folder Name | Displays the name of the folder that are present in the selected folder. (Applicable only for Azure Data Lake Storage Gen2 v2) | | Table Name | Displays the imported table name. | | Datetime format | Displays a number of **Datetime Formats** and Skypoint AI is set to automatically detect them. | | Delimiter | Displays available separators for the variables in the imported data. | | First Row as Header | Check the box for the system to automatically collect the data according to the Header Contents. | | Advanced Settings | Select the options to fine tune the Import process with minute details. | 5. If necessary, apply the **Advance settings** to modify the default settings. The **Advanced settings** pop-up appears. Advanced settings Advanced settings allow you to modify the default settings. It gives more flexibility to apply advanced use cases. However, the default settings are adequate to perform the task. | If you want to | Then | | :----------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------- | | Modify data types such as fixed or variable data types. | Select from the **Compression type**. It allows you to reduce the size of data by removing the number of bits. | | Change the delimiter | Click **Row delimiter**. By default, a column delimiter is selected, and each row is separated with a comma. | | Change information or instruction | Choose from the **Encoding** list. By default, UTF-8 encoding is selected. | | Modify the escape character such as backslash (\\) or slash (/) | Select from the **Escape character**. | | Apply different quote characters such as Single quote (') or Double quote ("). | Select from the **Quote character**. | 6. Click **Save**. ## Run, edit, and delete the imported data Once you save the connector, the Azure Data Lake Storage Gen2 connector gets displayed in the list of tables created in the Dataflow page. Output | Item | Description | | :---------------- | :------------------------------------------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables. | | Created Date | Displays date of creation. | | Last refresh type | Displays the refresh value. After the last data refresh, it will indicate whether the value is Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Group by | Option to view the items in a specific Group (For example, name, type, status). | * Select the horizontal ellipsis in the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run** history. | ## Overview This document will help you gather all credentials for connecting Azure Data Lake Storage Gen2 with Skypoint AI. ## Prerequisite You must have an [Azure](https://azure.microsoft.com/en-us/free/) account. ## To Create a Storage Account Follow below steps to create a storage account: 1. Log in to **Microsoft Azure**. 2. Go to **Azure services > Storage accounts**. Azure homepage 3. Click **Create**. ### Creating Storage account name 1. Go to **Create a storage account > Basics**. 2. Fill the information to create a new **Storage account**. * Type a desired **Storage account name** in the text area. The name must be unique among all existing storage account names in Azure. It must be between 3 to 24 characters long and can contain only lowercase letters and numbers. Create storage account * In **Advanced** tab, check **Enable hierarchical namespace** under **Data Lake Storage Gen2** section to create your ADLS Gen2 storage account. Enable namespace * Fill all text areas under each tab to configure and create your storage account. ### Finding the Account Key Follow below steps to find the account key for your storage account: 1. Go to **Storage account > Access Key**. Access keys 2. Click **Show** for the **Key** text area. Copy this key and paste it into the Account Key text area of your Skypoint AI's Azure Data Lake Storage Gen2 configuration form. ## Next step After completing the data import, start the [Master Data Management (MDM) - Stitch](stitch.md) process to develop a unified view of your customers. # Source: https://docs.skypoint.ai/docs/azuresynapseanalytics # Connecting to Azure Synapse Analytics This guide provides a step-by-step approach to effortlessly connecting Azure Synapse Analytics with skyData. The Azure Synapse Connector allows your platform to connect with Azure Synapse Analytics, enabling the ingestion of structured data from Synapse SQL pools. It simplifies data integration by securely extracting data from Synapse tables or views and loading it into your platform for further transformation, reporting, or AI-driven insights. The Azure Synapse Analytics Connector in skyData enables seamless integration with Azure Synapse Analytics, a cloud-based data warehouse and analytics service. It allows organizations to ingest and synchronize structured data such as transactions, general ledger entries, and vendor records using secure connections. The connector supports service principal authentication, automated scheduling, and data transformation, making it easy to integrate data into Skypoint’s unified data models. This enhances data accessibility, analytics capabilities, and enables AI-driven financial and operational insights. ## Prerequisite You will need the following details to configure and import data using [Azure Synapse Analytics](https://azure.microsoft.com/en-us/products/synapse-analytics/?msockid=1a11f2b867a16f663b53e74266c36eee) * Server Name * Database Name * Port Number * User Name * Password ## To Import data using Azure Synapse Analytics Follow the below steps to create a new dataflow for the Azure Synapse Analytics import connector: 1. Go to **Dataflow > Imports**. Alt image 2. Click **New dataflow**. The **Set dataflow name** page appears. Alt image 3. Enter **Dataflow name** in the **Name** text area. 4. Click **Next**. The **Choose connector** page appears. Alt text ## Add Azure Synapse Analytics connector 1. On the **Choose Connector page**, use the Search feature to locate and select the **Azure Synapse Analytics** Connector. Alt image 2. Enter the **Display Name** for your dataflow in the text area. 3. You can add a **Description** in the text area. 4. Click **Next** to proceed. The **Configuration** page appears. Alt image ## Connect to the Azure Synapse Analytics account 1. Fill in the required details on the **Configuration page**. 2. Click **Connect**. Once the connection is established, you can use the connector to import data from a table in Azure Synapse Analytics. Alt image 3. Scroll down to the **Table Details** section, select the checkboxes for the tables you wish to import, and use the dropdown menu to label them as either **Data** or **Metadata**. Alt image In the **Table Details** section, by default, all tables are selected. You can mark only those tables that you want to import and process the data. For example, to import customer data, you can check those tables which contain customer information, such as name, email, address, and contact details. | Item | Description | | :---------- | :-------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data | | Metadata | Loads Metadata | | File name | Displays the name of the file that you imported. | | Entity name | Displays the imported table name by default. You can rename it if required. | 5. Click **Save** to apply the changes. Alt image After saving the connection, the Azure Synapse Analytics connector appears on the **Dataflow** > **Imports** page. ## Run, edit, and delete the imported data Once the table is imported, you can execute, modify, and remove the imported table from the Dataflow. Follow the below steps: 1. Go to the **Dataflow** > **Imports** page. Alt image | Item | Description | | :---------------- | :------------------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables count | Displays the number of tables imported. | | Created Date | Displays date of creation. | | Last refresh type | Displays the refresh value: Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Group by | Option to view the items in a specific group. For example, type, status, tables count, etc. | 2. Select the horizontal ellipsis under the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run history**. | Alt image You can see the error message corresponding to failure while importing data from a data source in the Dataflow under **Run History** > **Description**. Also, you can see the status, start time, and end time for the execution of the data pipeline. ## Next step After completing the data import, start the [Master Data Management (MDM) - Resolve](https://docs.skypointcloud.com/docs/stitch.html) process to create a single, unified view of the data. With SkyPoint MDM, you can ensure that your data is accurate, consistent, and reliable, making it easier to use for business processes, analytics, and reporting. # Source: https://docs.skypoint.ai/docs/billinginfo # Billing Info ## Overview The Billing Info module provides visibility into your skyData subscription, usage, and payment details. Monitor resource consumption, manage your subscription plan, and access billing history from a centralized location. ## Key Capabilities ### Subscription Management View and manage your current billing plan. skyData offers flexible pricing models designed to match your usage patterns and business needs. ### Pricing Models * **Consumption-Based** - Pay for what you use with no upfront commitments or minimums. Credits are billed per second of usage with a one-minute minimum. * **Monthly Subscription** - Subscription-based pricing for predictable monthly costs based on storage capacity and reserved compute usage. ### Promotional Codes Apply promotional codes to your account for discounts or credits. Valid promo codes are applied to your billing cycle automatically. ### Usage Tracking Monitor resource utilization across your tenants and instances. Access usage reports to understand consumption patterns and optimize resource allocation. ### Plan Changes Request changes to your billing plan by contacting the skyData team. Transitions between plans are handled with minimal disruption to your operations. ### Support Integration Submit support requests for detailed usage reports and billing inquiries directly from the Billing Info module. ## Business Benefits * **Cost Visibility** - Clear understanding of resource consumption and associated costs * **Flexibility** - Choose the pricing model that best fits your usage patterns * **Budget Control** - Monitor usage to stay within budget allocations * **Transparency** - Access detailed billing history and usage reports ## Who Benefits * **Finance Teams** - Track costs and manage billing operations * **Administrators** - Monitor usage across tenants and instances * **Procurement Teams** - Evaluate and optimize subscription plans * **Management** - Visibility into platform costs and resource utilization # Source: https://docs.skypoint.ai/docs/boulevard # Connecting to Boulevard # Import data ## Overview Boulevard is an intelligent scheduling solution and comprehensive point of sale system that increases revenue and lowers costs for salons, spas, and other appointment-based businesses. You can use skyData's built-in connector for importing data from Boulevard. skyData collects, analyzes, and provides a solution to transform data into meaningful information for generating valuable insights. ## Prerequisite You will need the following details to configure and import data: * API Key * API Secret * URL * Business Id ## Import Data using Boulevard connector Follow the below steps to create a new dataflow for the Boulevard connector: 1. Go to **Dataflow > Imports**. 2. Click **New dataflow**. The **Set dataflow name** page appears: Alt text 3. In the **Set dataflow name** page, enter Dataflow name in the **Name** text area. 4. Click **Next**. The **Choose connector** page appears: Alt text ## To add Boulevard connector 1. In the **Choose connector** page, select **Boulevard** connector. You can use the Search feature too to find the connector. Also, the Boulevard connector can be found under **Sales** categories. The **Set dataflow name** page for **Boulevard** appears: Alt text 2. Enter the **Display Name** to identify your Dataflow. 3. In the **Description** box, you can enter an explanation of the connector. 4. Click **Next**. The **Connect to Boulevard** page appears: Alt text ## To configure Boulevard Follow the below steps to configure the connection to Boulevard: 1. Enter your credentials such as **API Key**, **API Secret**, **URL**, and **Business Id** to configure with the Boulevard connector. 2. Click **Connect**. The **Table Details** columns appear. Alt text 3. Enter the **Table Details** to process the data. | Item | Description | | :--------- | :------------------------------------------------------------ | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data. | | Metadata | Loads Metadata. | | File Name | Displays the name of the file that you imported. | | Table Name | Displays the imported table name. | 4. Click **Save**. ## Run, edit, and delete the imported data Once you save the connector, the Boulevard connector gets displayed in the list of tables created in the Dataflow page. Alt text | Item | Description | | :---------------- | :------------------------------------------------------------------------------------------------------- | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables. | | Created Date | Displays date of creation. | | Last refresh type | Displays the refresh value. You can see the value is Full or Incremental after the last refresh of data. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date. This date will get updated whenever you refresh the data. | | Group by | Option to view the items in a specific Group (For example, name, type, status). | * Select the horizontal ellipsis in the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run** history. | ## Next step After completing the data import, start the [Master Data Management (MDM) - Stitch](stitch.md) process to develop a unified view of your customers. # ERD BOULEVARD ERD # Source: https://docs.skypoint.ai/docs/campaignmonitor # Connecting to Campaign Monitor ## Overview Campaign Monitor is a leading email marketing platform that helps businesses to design and send personalized email messages. You can amplify your business growth with all the resources to manage new subscriptions, send email newsletters and generate reports. You can use skyData's built-in connector for importing data from Campaign Monitor. skyData collects, analyzes, and provides a solution to transform data into meaningful information for generating valuable insights. ## Prerequisite You will need the following details to configure and import data with [Campaign monitor](https://www.campaignmonitor.com/api/getting-started/): * Client ID * API Key. ## Import data using Campaign Monitor connector Follow the below steps to create a new dataflow for the Campaign Monitor import connector: 1. Go to **Dataflow > Imports**. 2. Click **New dataflow**. The **Set dataflow name** page appears. Alt text 3. In the **Set dataflow name** page, enter dataflow name in the **Name** text area. 4. Click **Next**. The **Choose connector** page appears. Alt text ## To add Campaign Monitor connector 1. In the **Choose connector** page, select **Campaign Monitor** connector. You can also use the Search feature to find the connector. Additionally, the Campaign Monitor connector is located under the Marketing category.
Alt text 2. Enter **Display Name** for your dataflow in the text area. 3. Enter **Description** for your dataflow in the text area. 4. Click **Next**. The **Connect to Campaign Monitor** page appears. Alt text ## To configure Campaign Monitor Follow the below steps to configure the connection to Campaign Monitor: 1. Enter your credentials such as **Client ID** and **API Key** to configure with the Boulevard connector. 2. Select **Activity Start Date** to set a start date range to import activities since the beginning. 3. Select **Activity End Date** to set an end date range to import activities till today. 4. Click **Connect**. The **Table Details** columns appear. Alt text 5. Enter the **Table Details** to process the data. | Item | Description | | :------- | :------------------------------------------------------------ | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data. | | Metadata | Loads Metadata. | | Name | Name of the imported tables. | | Type | Type of table where the details have been stored. | 6. Click **Save**. ## Run, edit, and delete the imported data Once you save the connector, the Campaign Monitor connector gets displayed in the list of tables created in the Dataflow page. Alt text | Item | Description | | :---------------- | :-------------------------------------------------------------------------------------------------------------- | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables. | | Created Date | Displays date of creation. | | Last refresh type | Displays the refresh value. You can see whether the value is ‘Full or Incremental’ after the last data refresh. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Group by | Option to view the items in a specific Group (For example, name, type, status). | * Select the horizontal ellipsis in the Actions column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run** history. | ## Next step After completing the data import, start the [Master Data Management (MDM) - Stitch](stitch.md) process to develop a unified view of your customers. # Source: https://docs.skypoint.ai/docs/campaignmonitorexport # Campaign Monitor Export lets you extract data from the Lakehouse and load it into Campaign Monitor. Alt text ## Step 01 - Add export * Click on **Set up** on the Campaign monitor card. * Enter a Name. (The name must start with a letter and can only contain **Letters** and **Numbers**, with no spaces allowed.) * Display name (which displays the name in the UI of the export saved). * Select credentials: * Select from existing * Create new ## Step 02 - Select from existing **In order to allow skyData access to your Campaign Monitor account, you'll need the following details:** * **Select Dataflow** * **API key** * **ClientID** * Click **Next** Button. ## Step 02 - Create new **In order to allow skyData access to your Campaign Monitor, you'll need the following details:** * **API Key** * **ClientID** * Click **Next** Button. ## Step 03 - Entities to export Here all the Lakehouse data will be available Entity wise. * Select the entities from the list to be exported * Click **Create** and then click on three dot button and select **Run**. * The export will be started. # Source: https://docs.skypoint.ai/docs/cmssnf # Connecting to CMS SNF This guide provides a step-by-step approach to effortlessly connecting CMS SNF with skyData. The CMS SNF Connector integrates CMS Skilled Nursing Facility data into skyData’s Lakehouse, ensuring compliance, efficiency, and real-time insights by ingesting data via API calls, storing it in azure blob storage, and processing it through Databricks into Delta Lake via Unity Catalog. With Databricks as a fully managed warehouse, SNFs gain real-time insights, personalized reports, and proactive alerts to optimize patient care. ## To import data using the CMS SNF connector Follow these steps to create and configure a new dataflow for the CMS SNF import connector: 1. Navigate to **Dataflow > Imports**. initiate setting up dataflow for cms snf 2. Click **New dataflow** as indicated by an arrow. The **Set dataflow name** page appears. set dataflow name for cms snf 3. Enter the desired name for the dataflow in the **Name** text field. 4. Click **Next** to proceed. The **Choose connector** page appears. search and select cms snf connector ## Add CMS SNF connector 1. On the **Choose Connector** page, use the **Search** feature to locate and select the **CMS SNF** Connector. 2. Enter the **Display Name** for your dataflow in the provided text field. 3. Optionally, add a **Description** in the designated text area. key in dataflow name 4. Click **Next** to proceed. The **Configuration** page appears. cms snf credentials are autofilled ## Connect to the CMS SNF account 1. The credentials gets auto populated on the Configuration page. 2. Click **Connect**. connect cms snf with skypoint ai Once the connection is established, the connector can be used to import data from CMS SNF tables. 3. Scroll down to the **Table Details** section, select the checkboxes for the tables you wish to import, and use the dropdown menu to label them as either **Data** or **Metadata.** select snf table to import data to skypoint ai By default, all tables in the **Table Details** section are selected. You can choose to import only specific tables that are relevant to your data processing needs. For example, to import customer data, select tables containing details like name, email, address, and contact information. | Item | Description | | :---------- | :-------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data | | Metadata | Loads Metadata | | File name | Displays the name of the file that you imported. | | Entity name | Displays the imported table name by default. You can rename it if required. | 4. Click **Save** to apply the changes. CMS snf dataflow saved on skypoitn ai Congratulations ! for saving the CMS SNF connector dataflow, which appears on the **Dataflow > Imports** page. ## Run, Edit, and Delete the imported data Once the table is imported, you can execute, modify, and remove the imported table from the Dataflow. Follow the below steps: 1. Go to the **Dataflow** > **Imports** page. seamless execution of cms snf dataflow on skypoint ai studio | Item | Description | | :---------------- | :------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Connector Name | Displays connector name. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables imported. | | Created Date | Displays date of creation. | | Last Refresh Type | Displays the refresh value: Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Actions | Provides multiple options for managing dataflows. | 2. Select the horizontal ellipsis under the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the dataflow | Select **Run history**. | 3. Click **Run** to execute the dataflow. Once the execution is successful, the data pipeline status will update to **Completed**, as illustrated in the figure below. data import from cms snf to skypoint ai In the Dataflow's **Run History Description,** you can view error messages related to data import failures from a data source. Additionally, you can check the status, start time, and end time of the data pipeline execution. ## Next step After completing the data import, start the [Master Data Management (MDM) - Resolve](https://docs.skypointcloud.com/docs/stitch.html) process to create a single, unified view of the data. With Skypoint MDM, you can ensure that your data is accurate, consistent, and reliable, making it easier to use for business processes, analytics, and reporting. # Source: https://docs.skypoint.ai/docs/copilots # Configure Copilots in skyData Studio ## Overview Welcome to the Copilots personalization guide! The Copilots allows you to create a fully personalized experience, aligned with your brand and theme preferences. This customizable feature offers unique branding, theme options, and dedicated access points. It is divided into two main sections: 1. **Branding:** Customize your site's appearance to align with your brand identity. Update the customer URL, page title, and theme color to reflect your branding. Additionally, personalize your experience by adding your brand icon and image, with flexible alignment options to suit your preferences. 2. **Prompt Library:** The Prompt Library allows you to browse, arrange, edit, and create custom questions. This helps tailor prompts to your specific needs, making workflows more efficient and personalized. 3. **Instructions:** Configure how the AI responds and interacts based on your specific requirements. Adjust behavior settings to align with your desired tone, response style, and level of assistance. ## A. Branding Follow these steps to have a fully branded, personalized copilot that aligns with your brand’s identity and business needs. 1. Navigate to the **Copilots** tab within the **Applications** module on your skyData Studio dashboard. Alt text 2. Click the **New Copilot** button to start customizing your copilot. If you're revising an existing copilot, follow the instructions in the final step. 3. Click on **Edit** to do the necessary customizations for Copilot. 4. In the Site **Branding** section, enter the custom URL for your copilot, set the page title, and choose a theme color, brand icon and image, with options to customize the image alignment. Alt text Ensure that your branding elements (icons, images, colors) are consistent throughout the copilot for a cohesive look. ## B. Prompt Library In the Prompt Library section, you can see the list or set of prompts, where you can edit, re-arrange, delete the prompts. 1. To add a new prompt, click on **Add** Alt text The Add new question form appears. 2. Add **preview** prompt. 3. Select the **category** from the drop-down. 4. Enter a list of prompts in **Detailed prompts**. 5. Click on **Save** to apply changes. Alt text ## C. Instructions Go to the **Instructions** tab to set up and customize AI behaviors Alt text 1. Add **Domain context** to your Copilot for more relevant responses. Domain context provides foundational knowledge such as industry-specific terminology, workflows, and user intents that enables your Copilot to generate accurate and relevant responses tailored to your organization’s needs. 2. Provide **Grounding instructions** to align Copilot’s responses with your context. Grounding instructions help anchor your Copilot’s responses in your specific business context. By providing clear guidelines such as preferred tone, response style, or reference materials you ensure the Copilot consistently reflects your organization’s voice, priorities, and domain expertise. 3. In the **Terminologies** tab and use the input fields to add relevant **terms** along with their corresponding **definitions**. 4. Click **Add Entity** to insert new entries as needed. Alt text 5. Click **Save Changes** at the top-right corner to apply all the configurations. 6. To discard the changes and revert to the previous state, click **Reset**. ## D. Agents Navigate to the **Agents** tab to enable the agents that align with your business needs. Alt text Only agents that have been configured and enabled in the Agent module will appear in the Agents tab. Ensure the required agents are set up there before proceeding. 1. Use the **toggle** next to each agent to enable or disable it for copilot as per your requirements. 2. Click **Save** to apply the changes. # Source: https://docs.skypoint.ai/docs/dashboard # Dashboard ## Overview The Dashboard is your real-time operations center. It gives you an at-a-glance view of the health and activity of your data platform without needing to navigate into individual sections. The Dashboard is made up of three widgets. You can **drag and reorder** these widgets by grabbing the handle icon on any widget and moving it to your preferred position. Your layout is saved automatically. skyData Dashboard ## Prerequisite Log in to **skyData** using your credentials. If you need help and support, click the **Need help?** button in the bottom right corner of the page. For more information regarding raising a new ticket, refer to the [Help+Support](/docs/help+support) section. ## Dataflows Widget This widget shows the current state of all your data import pipelines. * A **donut chart** displays the percentage breakdown of your dataflows by status: Completed, Running, Failed, and Cancelled * Below the chart, a table lists your most recent dataflows with their current status and last run date * Quick action buttons let you **Run**, **Cancel**, or **View History** for any dataflow without leaving the Dashboard * If you have no dataflows yet, a **Create Dataflow** button is shown to help you get started * A **View all dataflows** link takes you to the full Imports management page Learn more about [Dataflows](/docs/dataflows). ## Schedulers Widget This widget shows the status of your automated pipeline schedules. * Summary counts display the total number of schedules broken down by **Active**, **Running**, and **Failed** * A list of your most recently updated schedules is shown below * Quick action buttons let you **play** (trigger a manual run), **stop**, or **edit** each schedule directly from the Dashboard * A **Create Schedule** option is available if you have no schedules yet * A **View all schedulers** link takes you to the full Schedule management page Learn more about [setting up Schedules](/docs/schedule). ## Recent Activity Widget This widget provides a chronological feed of everything that has happened across your platform. * Activities include dataflow runs, connector updates, audience changes, configuration edits, and user actions * Each entry shows the type of activity, a description, and a relative timestamp (e.g., "2 hours ago") * Color-coded icons help you quickly categorize the activity type at a glance At the bottom, the widget shows the total count of recent activities. This widget helps you stay informed about what's happening in your skyData instance and track data processing activities. # Source: https://docs.skypoint.ai/docs/dataflow-import-dataverse # Connecting to Dataverse ## Overview Dataverse is an easy to use, easy to manage, compliant, secure, scalable, and globally available SaaS data service that stores your data within a set of tables. It helps you to acquire insights and motivate corporate action by enabling the use of any sort of data or app. ## Prerequisite You will need the following details to configure and import data using [Dataverse connector](https://powerplatform.microsoft.com/en-in/dataverse/): * [Service Uri](https://learn.microsoft.com/en-us/azure/data-factory/connector-dynamics-crm-office-365?tabs=data-factory#create-a-linked-service-to-dynamics-365-microsoft-dataverse-or-dynamics-crm-using-ui) * Office 365 Username and Password. ## Import data using Dataverse connector Follow the below steps to create a new dataflow for the Dataverse import connector: 1. Go to **Dataflow > Imports**. 2. Click **New dataflow**. The **Set dataflow name** page appears. Alt text 3. Enter **Dataflow name** in the **Name** text area. 4. Click **Next**. The **Choose connector** page appears. Alt text ## To add Dataverse connector 1. In the **Choose connector** page, select **Dataverse** connector. You can also use the Search feature to find the connector. The Dataverse connector is listed under both the Developer Tools and Sales categories.
Alt text 2. Enter **Display Name** for your dataflow in the text area. 3. Enter **Description** for your dataflow in the text area. 4. Click **Next**. The Connect to **Dataverse** page appears. Alt text ## To configure Dataverse Follow the below steps to configure the connection to Dataverse: 1. Enter the **Service Uri** details in the text area. 2. Select an **Authentication Type** option from the drop-down list. 3. Enter your **Username** and **Password**. 4. Click **Connect**. The **Table Details** columns appear. Alt text 5. Enter the **Table Details** to process the data. | Item | Description | | :--------- | :------------------------------------------------------------ | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data. | | Metadata | Loads Metadata. | | File name | Displays the name of the file that you imported. | | Table name | Displays the imported table name. | 6. Click **Save**. ## Run, edit, and delete the imported data Once you save the connector, the Dataverse connector gets displayed in the list of tables created in the Dataflow page. Alt text | Item | Description | | :---------------- | :------------------------------------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables. | | Created Date | Displays date of creation. | | Last refresh type | Displays the refresh value. You can see whether the value is Full or Incremental after the last data refresh. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Group by | Option to view the items in a specific Group (For example, name, type, status). | * Select the horizontal ellipsis in the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run history**. | ## Next step After completing the data import, start the [Master Data Management (MDM) - Stitch](stitch.md) process to develop a unified view of your customers. # Source: https://docs.skypoint.ai/docs/dataflows # Dataflow ## Overview Dataflow is the first step in creating a customer 360-degree view that allows you to load the data from disparate data sources into skyData Lakehouse. skyData consolidates data from different sources and accelerates informed decisions across your organization. You can accomplish this using prebuilt or custom-built connectors. Our platform already supports 200+ built-in connectors that allow data import from different data sources. It helps you to manage large volumes of data and gain actionable insights into a 360-degree view of your customer. Dataflow also allows you to export the data from skyData Lakehouse to your corresponding data sources. Dataflow provides a strong data foundation, maintains privacy compliance, delivers personalized experiences, and drives the business towards a collective vision. Alt text skyData’s built-in ELT allows you to centralize all your data, maintain privacy compliance, and deliver personalized experiences with real-time insights. You can connect all your databases securely and accelerate analytics with integrated transformation tools. You can build a secure and reliable ELT data pipeline that gives you a single source of truth. ### See also * [Imports](imports.md) * [Exports](exports.md) * [Connectors](connectors.md) # Source: https://docs.skypoint.ai/docs/datalineage # Data lineage ## Overview One of the key features of skyData configured dbt models is the ability to track data lineage. Data lineage refers to the origin, history, and transformations of data as it moves from its source to its destination. Data lineage offers a comprehensive understanding of the journey of data within an organization, including its transformations and utilization. ## View the data lineage Data lineage is represented as a Directed Acyclic Graph (DAG) of relationships between models, which are the building blocks of a dbt project. Each model represents a set of transformations that are applied to one or more source tables, and the lineage is captured by recording the dependencies between models. Follow the below steps to view the data lineage: 1. Go to **Dataflow** > **Transformations**. 2. Select the specific **Output model name** you want to view. The Transformation details page appears. Alt image The DAG view provides an overview of the relationships between models, making it easier to understand the structure of the data pipeline. For example, a data pipeline can process data from an e-commerce website in dbt and represents a DAG of relationships between models. In a DAG workflow, the dbt data lineage for an e-commerce business can include a source table `ecommercecontact` and two models: `stg_customers` and `dim_customers`. * `ecommercecontact`: This is the source table that contains raw customer data from the e-commerce database. * `stg_customers`: This model depends on the `ecommercecontact` table and performs initial data cleaning and preparation operations, such as removing duplicates, fixing data types, and standardizing values. * `dim_customers`: This model depends on the `stg_customers` model and performs additional data cleaning and preparation operations, such as splitting names into separate first and last name fields and calculating the age of each customer. The `dim_customers` model also integrates data from other table sources, such as `ecommerceloyaltypoints`, `ecommercepurchases`, to create a comprehensive customer profile. This DAG represents the relationships between the source table and the two models and shows how the `dim_customers` model is derived from the `ecommercecontact` table through the intermediate step of preparing the data in the `stg_customers` model. Visualizing these relationships simplifies understanding the flow of data, the transformations applied, and troubleshooting any issues in the data pipeline. # Source: https://docs.skypoint.ai/docs/dayforce # Connecting to Dayforce This guide provides a step-by-step approach to effortlessly connecting Dayforce with skyData. Dayforce Connector integrates Dayforce Global HCM Software with your platform, streamlining HR processes by automating employee data synchronization, ensuring data accuracy, and providing access to advanced analytics for better decision-making. This integration enhances operational efficiency and seamlessly connects your HR systems with other enterprise applications. The Dayforce Connector in skyData enables seamless integration with Dayforce Global HCM Software, commonly used for HR, payroll, time tracking, and talent management. It allows organizations to efficiently ingest and sync employee data using secure APIs, supporting service account credentials, automated scheduling, and data transformation. This connector streamlines data integration into Skypoint’s unified models, enhancing analytics capabilities and AI-driven decision-making. ## Prerequisite You will need the following details to configure and import data using [Dayforce](https://www.dayforce.com/) * Company * Username * Password * Default Activity Start Date * Reload Records From (Days) ## To Import data using Dayforce connector Follow the below steps to create a new dataflow for the Dayforce import connector: 1. Go to **Dataflow > Imports**. Alt image 2. Click **New dataflow**. The **Set dataflow name** page appears. Alt image 3. Enter **Dataflow name** in the **Name** text area. 4. Click **Next**. The **Choose connector** page appears. Alt text ## Add Dayforce connector 1. On the **Choose Connector page**, use the Search feature to locate and select the **Dayforce** Connector. Alt image 2. Enter the **Display Name** for your dataflow in the text area. 3. You can add a **Description** in the text area. 4. Click **Next** to proceed. The **Configuration** page appears. Alt image ## Connect to the Dayforce account 1. Fill in the required details on the **Configuration page**. 2. Click **Connect**. Once the connection is established, you can use the connector to import data from a table in Dayforce. Alt image 3. Scroll down to the **Table Details** section, select the checkboxes for the tables you wish to import, and use the dropdown menu to label them as either **Data** or **Metadata**. Alt image In the **Table Details** section, by default, all tables are selected. You can mark only those tables that you want to import and process the data. For example, to import customer data, you can check those tables which contain customer information, such as name, email, address, and contact details. | Item | Description | | :---------- | :-------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data | | Metadata | Loads Metadata | | File name | Displays the name of the file that you imported. | | Entity name | Displays the imported table name by default. You can rename it if required. | 5. Click **Save** to apply the changes. Alt image After saving the connection, the Dayforce connector appears on the **Dataflow** > **Imports** page. ## Run, edit, and delete the imported data Once the table is imported, you can execute, modify, and remove the imported table from the Dataflow. Follow the below steps: 1. Go to the **Dataflow** > **Imports** page. Alt image | Item | Description | | :---------------- | :------------------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables count | Displays the number of tables imported. | | Created Date | Displays date of creation. | | Last refresh type | Displays the refresh value: Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Group by | Option to view the items in a specific group. For example, type, status, tables count, etc. | 2. Select the horizontal ellipsis under the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run history**. | Alt image You can see the error message corresponding to failure while importing data from a data source in the Dataflow under **Run History** > **Description**. Also, you can see the status, start time, and end time for the execution of the data pipeline. ## Next step After completing the data import, start the [Master Data Management (MDM) - Resolve](https://docs.skypointcloud.com/docs/stitch.html) process to create a single, unified view of the data. With SkyPoint MDM, you can ensure that your data is accurate, consistent, and reliable, making it easier to use for business processes, analytics, and reporting. # Source: https://docs.skypoint.ai/docs/dbeaverintegration # Integrating DBeaver with Lakehouse SQL ## Overview DBeaver is an open-source database tool for developers, analysts, and database administrators. It supports Databricks as well as other popular databases. You can use the skyData Lakehouse SQL tool to run SQL commands and browse database objects in Databricks. Also, you can perform CRUD (Create, Read, Update, and Delete) operations using an SQL server.\ Follow the below steps to integrate DBeaver with Lakehouse SQL: ## Step 1: Install DBeaver and Download Databricks JDBC Driver Follow the below steps to install DBeaver and Download Databricks JDBC Driver: * Download **DBeaver** from [https://dbeaver.io/download/](https://dbeaver.io/download/). Choose Dbeaver Community 22.2.0, run the process, and set up the tool. You download the latest version. This article is tested with version 22.2.0. * Download the [Databricks JDBC Driver](https://databricks.com/spark/jdbc-drivers-download) onto your local development machine. Extract the `databricksJDBC42.jar` file from the downloaded `DatabricksJDBC42.zip` file. Once DBeaver is installed, follow the below steps to configure and work with databases in Lakehouse SQL. ## Step 2: Configure the Databricks JDBC Driver for DBeaver Set up DBeaver with information about the Databricks JDBC Driver that you downloaded earlier. 1. Start DBeaver. 2. If you are prompted to create a new database, click **No**. 3. If you are prompted to connect to or select a database, click **Cancel**. 4. Click **Database** > **Driver Manager**. 5. In the **Driver Manager** dialog box, click **New**. 6. In the **Create new driver** dialog box, click the **Libraries** tab. 7. Click **Add File**. 8. Browse to the `databricksJDBC42.jar` file that you extracted earlier and click **Open**. 9. Click **Find Class**. 10. In the **Driver class** list, confirm that **com.simba.spark.jdbc.Driver** is selected. 11. On the **Settings** tab, for **Driver Name**, enter `Databricks`. 12. On the **Settings** tab, for **Class Name**, enter `com.simba.spark.jdbc.Driver`. 13. Click **OK**. 14. In the **Driver Manager** dialog box, click **Close**. ## Step 3: Connect DBeaver to your Lakehouse SQL endpoint Use DBeaver to connect to the Lakehouse SQL endpoint to access the databases in your Databricks workspace. 1. In DBeaver, click **Database > New Database Connection**. Alt text 2. In the **Connect to a database** dialog box, on the **All** tab, click **Azure** **Databricks** **(Native)**, and then click **Next**. Alt text 3. Click the **Main** tab and enter credentials to connect. Alt text 4. To find credentials such as JDBC URL and Personal access token, follow the below steps: 1. Open Skypoint Studio. 2. In the left pane, go to **Lakehouse** > **SQL Access**. 3. Click **Show fields** to copy your credentials to your clipboard. For more information, refer to [Getting started with skyData Lakehouse SQL](lakehousesql). Alt text 5. Enter **JDBC URL** and do the following: | To | Do | | --------------------- | ------------------------------------------------------------------------------- | | Validate **Username** | Type token. This token is just the word token only. | | Validate **Password** | Enter the Personal Access token that you have taken from the Skypoint Platform. | 6. Check **Save password locally**. 7. Click **Test Connection**. 8. If the connection succeeds, click **OK** in the **Connection Test** dialog box. 9. In the **Connect to a database** dialog box, click **Finish**. In the **Database Navigator** window, a new entry is displayed. ## Step 4: Use DBeaver to browse databases and their objects Use DBeaver to access databases and database objects in your Lakehouse SQL workspaces such as tables and table properties, views, indexes, data types, and other object types. 1. In the **Database Navigator** window, right-click **Azure Databricks**. Alt text 2. If **Connect** is enabled, click it. (If **Connect** is disabled, you are already connected.) 3. Expand **hive\_metastore**. Alt text 4. Expand the database that you want to browse. 5. Expand **Tables**. 6. Right-click on the table you wish to query and click on read data in SQL editor. 7. Repeat the instructions in this step to access additional databases and database objects. # Source: https://docs.skypoint.ai/docs/declaringsource # Declaring a source ## Overview The `source.yml` file in dbt project is used to define the source tables that will be transformed by dbt models. This file contains information such as the database name, schema, and corresponding tables for a given source. By using the `{{ source() }}` function, you can select from source tables in your models and establish the lineage of your data. To use dbt transformations, you need to create the `source.yml` file. Here is an example of what the `source.yml` file might look like for a project that transforms data from a database: ```yaml theme={null} Sources: - name: klaviyo database: platformdbtest\_production\_main schema: platformdbtest\_production tables: - name: campaigns identifier: klaviyo\_klaviyo\_campaign - name: events identifier: klaviyo\_klaviyo\_events  - name: square database: platformdbtest\_production\_main schema: platformdbtest\_production tables: - name: orderlineitems identifier: square\_square\_orderlineitems - name: order identifier: square\_square\_order - name: location identifier: square\_square\_location ``` In this example of a `source.yml` file for dbt transformation, there are two specified sources: `klaviyo` and `square`. The `klaviyo` source is in the `platformdbtest\_production\_main` database, `platformdbtest\_production` schema and has two tables that can be referenced in dbt models for transformations: `klaviyo\_klaviyo\_campaign` as campaigns and `klaviyo\_klaviyo\_events` as events. You can use the `{{ source() }}` function to select from source tables in dbt models and establish data lineage. For example, if you create a model that references the `klaviyo\_klaviyo\_campaign` table from the `klaviyo` source, you can track the lineage of the data in your models. Similarly, if you create a model that references the `square\_square\_order` table from the `square` source, you can establish the data lineage for that table. # Source: https://docs.skypoint.ai/docs/dockhealth # Connecting to DockHealth This guide covers the steps to seamlessly connect DockHealth with skyData, including setup, configurations, and best practices. Dock Health is a HIPAA-compliant platform designed to streamline healthcare operations through efficient task management and workflow automation. It enables healthcare teams to securely capture, assign, and track both clinical and administrative tasks, fostering collaboration and accountability. Key features include robust task management, workflow automation, seamless team communication tools, and strong compliance with HIPAA regulations. skyData Platform (AIP) integrates with DockHealth via an inbuilt connector, automating the secure processing of daily CSV files from SFTP sites, ensuring reliable data ingestion, transformation, and storage into platforms like Databricks, enhancing operational efficiency and data accuracy. ## Prerequisite You need the following details to configure and import data using the DockHealth⇗ connector: * Host * Username * Private Key * Directory ## To import data using the DockHealth connector Follow the below steps to create and configure a new dataflow for the DockHealth import connector: 1. Navigate to **Dataflow > Imports**. Add new Dockhealth dataflow 2. Click **New dataflow** as indicated by an arrow. The **Set dataflow name** page appears. Set dataflow name for dockhealth 3. Enter the desired name for the dataflow in the **Name** text field. 4. Click **Next** to proceed. The **Choose connector** page appears. Search and choose dockhealth connector ## Add DockHealth connector 1. On the **Choose Connector** page, use the **Search** feature to locate and select the **DockHealth** Connector. 2. Enter the **Display Name** for your dataflow in the provided text field. 3. Optionally, add a **Description** in the designated text area. Add dockhealth connector 4. Click **Next** to proceed. The **Configuration** page appears. Configure dockhealth connector dataflow ## Connect to the DockHealth account 1. Fill in the required details on the Configuration page. 2. Click **Connect**. Connect dockhealth connector Once the connection is established, the connector can be used to import data from DockHealth tables. 3. Scroll down to the **Table Details** section, select the checkboxes for the tables you wish to import, and use the dropdown menu to label them as either **Data** or **Metadata.** Select tables to import from dockhealth By default, all tables in the **Table Details** section are selected. You can choose to import only specific tables that are relevant to your data processing needs. For example, to import customer data, select tables containing details like name, email, address, and contact information. | Item | Description | | :---------- | :-------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data | | Metadata | Loads Metadata | | File name | Displays the name of the file that you imported. | | Entity name | Displays the imported table name by default. You can rename it if required. | 4. Click **Save** to apply the changes. Save the dockhealth connector dataflow Congratulations! for saving the DockHealth connector dataflow, which appears on the **Dataflow > Imports** page. ## Run, Edit, and Delete the imported data Once the table is imported, you can execute, modify, and remove the imported table from the Dataflow. Follow the below steps: 1. Go to the **Dataflow** > **Imports** page. Options to modify dockhealth dataflow | Item | Description | | :---------------- | :------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Connector Name | Displays connector name. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables imported. | | Created Date | Displays date of creation. | | Last Refresh Type | Displays the refresh value: Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Actions | Provides multiple options for managing dataflows. | 2. Select the horizontal ellipsis under the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the dataflow | Select **Run history**. | 3. Click **Run** to execute the dataflow. Once the execution is successful, the data pipeline status will update to **Completed**, as illustrated in the figure below. Dockheath dataflow setup is complete In the Dataflow's **Run History Description,** you can view error messages related to data import failures from a data source. Additionally, you can check the status, start time, and end time of the data pipeline execution. ## Next step After completing the data import, start the [Master Data Management (MDM) - Resolve](https://docs.skypointcloud.com/docs/stitch.html) process to create a single, unified view of the data. With Skypoint MDM, you can ensure that your data is accurate, consistent, and reliable, making it easier to use for business processes, analytics, and reporting. # Source: https://docs.skypoint.ai/docs/dotdigital # Connecting to the Dotdigital connector ## Overview Dotdigital is an online marketing platform that provides marketing solutions for campaigns, eCommerce, automation, email, and SMS marketing. You can use skyData's built-in connector for importing data from Dotdigital. Our platform collects, analyzes, and provides a solution to transform data into meaningful information for generating valuable insights. ## Prerequisite * You have a [Dotdigital account](https://dotdigital.com/) and credentials such as Username and Password. Each API user has credentials that must be used to start the configuration. If you want to create an API user account, you need to set up an [API user](https://support.dotdigital.com/hc/en-gb/articles/115001718730-How-do-I-create-an-API-user). ## Import Data using Dotdigital connector 1. In the left pane, go to **Dataflow** > **Imports**. The Dataflow window appears. Alt text 2. Click **New Dataflow** to create dataflow. Alt text 3. Enter a **Name** to identify your Dataflow. The name must start with a letter and can only contain letters and numbers, with no spaces between characters. 4. Click **Next**. ## Add Dotdigital Connector 1. In the **All** or **Marketing** tab, select the **dotdigital** connector to start the configuration. Alt text 2. Also, you can use the **Search** function to find the **dotdigital** connector. Alt text 3. In the **Set dataflow name**, enter the **Display Name** to identify your dataflow. 4. In the **Description** box, you can enter an explanation of the connector. 5. Click **Next**. Alt text ## To configure Dotdigital Follow the below steps to configure the connection to Dotdigital: 1. Enter your credentials such as **Username**, **Password**, and **Region** to configure with the Boulevard connector. | Configuration | Description | | :------------------ | :------------------------------------------------------------------------------------ | | Region | Based on an API endpoint, it allows you to import data only for a particular region.  | |
r1
| Allows you to use an API endpoint to import data for the **Europe** region. | |
r2
| Allows you to use an API endpoint to import data for the **North America** region. | |
r3
| Allows you to use an API endpoint to import data for the **Asia Pacific** region. | 2. Enter your [credentials](https://kwicloud9docs.com/kwi-api-details/) such as **Username**, **Password**, and **Region** to configure with the Dotdigital connector. For details about the Dotdigital account, click [Get account information](https://developer.dotdigital.com/reference/get-account-information). If you want to create an API user, refer to the [setting up your API user](https://developer.dotdigital.com/docs/getting-started-with-the-api). For more information about API endpoints and regions, see [API conventions](https://developer.dotdigital.com/docs/api-conventions). 3. Click **Connect**. Alt text 4. You can enter the **Table Details** to process the data. In the **Table Details**, all address-books and campaigns are selected by default. Also, you can mark only those address-books or campaigns that you want to import and process the data. For example, to import customer data from the address book and marketing campaigns, you can check only those which contain customer information, such as name, email, address, and contact details. | Item | Description | | :------------------------ | :------------------------------------------------------------ | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | |
Data
| Loads customer data | |
Metadata
| Loads Metadata | | File Name | Displays the name of the file that you imported. | | Table Name | Displays the imported table name. | 5. Click **Save** to apply the changes. ## Run, edit, and delete the imported data 1. After saving the connection, the Dotdigital connector appears on the Dataflow page. Also, you can see the list of created tables in the [Databases](https://skypointcdpdocs.z22.web.core.windows.net/docs/entities.html) section. Alt text | Item | Description | | :----------- | :------------------------------------------------------------------------------ | | Name | Displays the name of the Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables. | | Created Date | Displays date of creation. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Group by | Option to view the items in a specific Group (For example, name, type, status). | 2. Select the horizontal ellipsis in the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run History**. | You can view the error message for any data import failures from a data source by navigating to Dataflow and checking under **Run history** > **Description**. ## Next step After completing the data import, start the [Master Data Management (MDM) - Stitch](stitch.md) process to develop a unified view of your customers. # Source: https://docs.skypoint.ai/docs/drchrono # Connecting to Dr Chrono Dr Chrono provides a healthcare platform with EHR, practice management, and billing, featuring the OnPatient Portal for HIPAA-compliant messaging, online scheduling, digital onboarding and patient engagement. You can seamlessly integrate DrChrono with skyData Studio to automate secure data retrieval for patients, appointments, custom demographics, appointment profiles, and doctor entities. The built-in connector with skyData Studio offers features like automatic token refresh, cursor-based pagination, and flexible synchronization with incremental updates and full data loads, ensuring data remains current and accessible. This document will guide you through the process of connecting Dr Chrono to the skyData. ## Prerequisite You need the following details to configure and import data using the DrChrono⇗ connector: * Client ID * Client Secret * Refresh token * Default Activity start date ## To import data using the Dr Chono connector Follow the below steps to create and configure a new dataflow for the Dr Chrono import connector: 1. Navigate to **Dataflow > Imports**. Alt image 2. Click **New dataflow** as indicated by an arrow. The **Set dataflow name** page appears. Alt image 3. Enter the desired name for the dataflow in the **Name** text field. 4. Click **Next** to proceed. The **Choose connector** page appears. Alt image ## Add Dr Chrono connector 1. On the **Choose Connector** page, use the **Search** feature to locate and select the **Dr Chrono** Connector. 2. Enter the **Display Name** for your dataflow in the provided text field. 3. Optionally, add a **Description** in the designated text area. Alt image 4. Click **Next** to proceed. The **Configuration** page appears. Alt image ## Connect to the Dr Chrono account 1. Fill in the required details on the Configuration page. 2. Click **Connect**. Alt image Once the connection is established, the connector can be used to import data from the Dr Chrono tables. 3. Scroll down to the **Table Details** section, select the checkboxes for the tables you wish to import, and use the dropdown menu to label them as either **Data** or **Metadata.** Alt image By default, all tables in the **Table Details** section are selected. You can choose to import only specific tables that are relevant to your data processing needs. For example, to import patient data, select tables containing details like name, email, address, patient check-in, appointments and more. | Item | Description | | :---------- | :-------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data | | Metadata | Loads Metadata | | File name | Displays the name of the file that you imported. | | Entity name | Displays the imported table name by default. You can rename it if required. | 4. Click **Save** to apply the changes. Alt image Congratulations ! for saving the Dr Chrono connector dataflow, which appears on the **Dataflow > Imports** page. ## Run, Edit, and Delete the imported data Once the table is imported, you can execute, modify, and remove the imported table from the Dataflow. Follow the below steps: 1. Go to the **Dataflow** > **Imports** page. Alt image | Item | Description | | :---------------- | :------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Connector Name | Displays connector name. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables imported. | | Created Date | Displays date of creation. | | Last Refresh Type | Displays the refresh value: Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Actions | Provides multiple options for managing dataflows. | 2. Select the horizontal ellipsis under the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the dataflow | Select **Run history**. | 3. Click **Run** to execute the dataflow. Once the execution is successful, the data pipeline status will update to **Completed**, as illustrated in the figure below. Alt image In the Dataflow's **Run History Description,** you can view error messages related to data import failures from a data source. Additionally, you can check the status, start time, and end time of the data pipeline execution. ## Next step After completing the data import, start the [Master Data Management (MDM) - Resolve](https://docs.skypointcloud.com/docs/stitch.html) process to create a single, unified view of the data. With Skypoint MDM, you can ensure that your data is accurate, consistent, and reliable, making it easier to use for business processes, analytics, and reporting. # Source: https://docs.skypoint.ai/docs/dynamics365 # Dynamics 365 Setup Guide Follow our setup guide to connect **Dynamics 365** to **skyData**. ## Steps to Connect 1. Navigate to **Data** > **Dataflow** and click on **Add Dataflow** present on the top right side of the screen. 2. Fill in the name of the dataflow and click on Next. 3. Click on **Services** and select Dynamics365. Alt text 4. Enter the display name of the dataflow and give a brief description of the dataflow and then click on Connector. Alt text 5. Fill in the **User Name**, **Password**, **ClientID**, **Client Secret** and **domain URL**. Click on Connect. 6. You will see a set of entities. Select all those you want and then click on SAVE. 7. You will be taken to the Dataflow page. 8. Click on the three dots under Action and select Run for the desired Dataflow. 9. It will take some time and your dataflow will be ingested. Alt text # Source: https://docs.skypoint.ai/docs/entities # Exploring the Databases ## Overview *** Alt text *** The **Databases Explorer** in Skypoint Studio provides an intuitive, unified interface to browse all data in your Lakehouse. It leverages Databricks **Unity Catalog** to organize data assets (catalogs, schemas, and tables) in a hierarchical structure. This upgrade offers a comprehensive view of all schemas and tables under Unity Catalog, greatly improving data visibility, organization, and accessibility within the Lakehouse for a seamless and efficient user experience. All changes made through Lakehouse SQL (such as adding or deleting records in a table) are synced to the Lakehouse in real-time, ensuring the explorer always reflects the latest data. ## To select and explore a Database For every import, skyData adds a database to its analytical store known as Lakehouse. 1. In the left pane, go to the **Lakehouse** > **Explorer**. Alt text The **Catalog page** appears 2. Click on the **Drop-down** menu of the Catalog to see the Schemas under the Catalog. Alt text 3. Then Click on the **Schemas** drop-down menu to see tables. Alt text 4. Click on **Search** button to search the tables. 5. Then, Click on **Table name (Arrow)** button to sort the tables. In the **Explorer** window under **Schema**, tables are organized into three layers: **Bronze**, **Silver**, and **Gold**. Each layer is represented by an icon-Bronze, Silver, or Gold, Indicating the data layer of a table: Alt text * **Bronze** – Tables of raw data ingested from various sources in the Dataflow with every light transformation. * **Silver** – Refined and transformed view of the data for example after Map in the MDM / Stitch process. * **Gold** – Consumption and business level aggregate tables such as Profiles (Customer, Employee, Organization etc.), Timelines, Audience, and Metrics. | Item | Description | | :---------------- | :------------------------------------------------------------------- | | Table Name | The name of the tables. | | Total Records | The number of rows of that individual database. | | Edited by | The user who updated the imported data to formulate a processed one. | | Last Refresh date | The date and time when the database were last updated. | | Source | Type of data source such as connectors that ingested the data. | | Data Completeness | Indicates the comprehensiveness of the data. | | Data Standards | The layers of data present in table. | 2. Select a **Table** to know more about the attributes and the data. 3. The **Attributes** tab is opened by default and shows a table. In the Attributes page, you will see a tabular representation of all the attributes present inside that entity with their details. Alt text | Item | Description | | :---------------- | :------------------------------------------------------------------------------------------------------------------- | | Attribute Name | Shows name of the attribute. | | Data type | Shows data type such as String or Integer. | | Semantic Label | Describes the attribute. | | Is Sensitive Data | Use this property to classify Semantic Labels as Sensitive. | | Summary | Indicates you with some important characteristics of the data, such as unique values, counts, nulls, missing values. | 4. Select the **Data** tab to see the details about individual records of the entity. 5. Click on **Columns** to customize the columns needed.

Alt text 6. .Click on **Download CSV** to download data in CSV In the data tab, you will see a tabular representation of the data with its values. The Column option side panel drop-down fields have a search functionality with a search placeholder. The search functionality has been made easier for the user by adding a search feature on the Column Options drop-down fields. Users can type for any attributes in the Column Settings field. This avoids the scrolling required for the long list of attributes. After a successful download, you can open the CSV file. You'll find detailed information about a list of attributes associated with the database. # Source: https://docs.skypoint.ai/docs/exports # Exports ## Overview The Export process in the Dataflow transfers unified data to storage accounts, data warehouses, tools, and other external business applications. You need to connect with a storage service to export the data. After connection, you can transfer your data to various applications. skyData has the ability to Export data to any data source including pre-built connectors with popular applications (e.g. Shopify, Salesforce etc.), web activity through our JavaScript tag as well as custom data sources (e.g. Amazon S3, Azure Blob etc). skyData exports standardized, modular, and extensible collection of data schemas from the Lakehouse to the location specified. For more information on import data click here - [Data Flow ingest](connectors) ## Prerequisite * You have credentials to configure with your storage service. ## To export data from the Dataflow * In the left pane, go to **Dataflow** > **Exports**. The Exports window appears. By default, the **Export Gallery** page opens. Export Gallery | Item | Description | | :------------- | :-------------------------------------------------------------------------------------- | | Export Gallery | Displays all export destinations to build setup. | | Destinations | Displays the name, type, and status of all exported data. Also, you can add new Export. | | Run History | Displays the execution history of the Export process. | ## Set up a new export 1. You can add the export connection in two ways. However, the procedure is similar in both cases. These are as follows: * In the **Export Gallery** tab, select the export destination. * In the **Destinations** tab, click **Add Export** to configure with storage service. 2. If you add an export connection through **Export Gallery**, click **Set up** to create a new export. The Setup window appears. Export Setup 3. Enter **Name** and **Display name**. The name must start with a letter and can only include letters and numbers. 4. Enter the credentials of the storage service for validation. If you have already created an export connection and you want to copy the credentials, then choose **Select from existing**. 5. Click **Next**. Export Select Entities 6. Select the tables that you want to export and click **Next**. You can export audiences created from prediction models, enabling targeted outreach to high-value customers or those at risk of churning. Attributes to be Exported 7. Select the attributes that you want to export and click **Create**. The Export setup consists of three steps, and you need to perform them in the following order: 1. Configure with the storage service. 2. Select the tables. 3. Select the attributes. The above images are shown the SFTP set up to connect and export data from the skyData platform. If you have already created an SFTP connection, then click **Select from exiting** to enter your credentials. However, for a new SFTP connection, enter SFTP credentials such as **Host**, **Port, Host Key Fingerprint, Username**, **Password**, and **Storage Path**. Similarly, based on the storage service (file-based or API-based), you need to enter the credentials to build the configuration. Then, select the tables and attributes to export data from the skyData platform. If you configure it with an API-based connector, click **Save** after entering a valid API key. The system will not allow you to proceed without providing the API key. ## Run, Cancel Run, and Remove the exported data 1. After creating an export connection, it appears in the **Destinations** tab. Exports Output | Item | Description | | :------------ | :---------------------------------------------------------------------------------------------------------------------------- | | Name | Name of the Export connection. | | Display Name | Displays the name of the exported data. It can be different from the Export connection name. Also, you can change this later. | | Type | Displays the connector type. | | Created | Displays date of creation. | | Last Exported | Displays last modified date. | | Status | Indicates whether the data is exported successfully. | | All | Option to view all export connections. Also, you can view the items in a specific Group (for example, name, type, status). | 2. Select the horizontal ellipsis in the **Actions** column and do the following: | If you want to | Then | | :-------------------------------------------------------------------- | :--------------------- | | Modify the Export | Select **Edit**. | | Execute the Export process | Select **Run**. | | Cancel the execution of the data export | Select **Cancel Run**. | | Configure a refresh schedule to add new or updated data in the Export | Select **Schedule**. | | Delete the Export | Select **Remove**. | | See the history of the Export | Select **History**. | ## Check real-time Export status Follow the below steps to check the real-time status of your data exports: 1. Go to **Exports > Destinations**. 2. Click the three horizontal ellipsis under the **Actions** column for your export. 3. Click **History** on the **Actions** menu. The **Export History** pop-up appears, displaying the real-time export details. Export History ## Edit the Export You can update the exported data for the existing storage account. 1. Go to **Dataflow** > **Exports**. 2. Open the **Destinations** tab and select the export that you want to update. 3. In the **Actions** column, click the horizontal ellipsis and select **Edit**. 4. Modify the data and select **Update**. 5. Click **Run** to execute modification. ## Schedule the Export skyData platform provides you to set a time for new or updated data to include in the exported file. The Export process runs whenever the system refreshes data or system refresh is triggered. With the **Schedule** feature, you can customize the refresh schedule. By default, export runs as part of every scheduled system refresh run for the particular instance. The system refresh schedule is shown on the **Settings** page. You can check through **Settings** > **Schedule**. 1. Go to **Dataflow** > **Exports**. 2. Open the **Destinations** tab and select the export that you want to schedule. 3. In the **Actions** column, click the horizontal ellipsis and select **Schedule**. The Schedule window appears. Export Schedule 4. To activate the scheduled export automatically, click on the **Schedule run** button to set **On**. If turned off, the export will only run if activated manually. 5. Choose a **Recurrence** value to refresh the exported data automatically. 6. Click **Save** to apply your changes. ## To export metadata along with CSV files You can export a metadata JSON file, containing information about the purpose, frequency, etc. The metadata JSON file is exported with each exported CSV file. Follow the below steps: 1. Go to **Dataflow > Exports**. 2. In the **Exports** page, click **Destinations** tab. 3. Select the created Export connector and click the horizontal ellipses under **Actions** column. 4. Click **Edit**. The **Edit export** form appears. Exports Metadata 5. Click **Next** and select a table for export. 6. Add key-value pairs in the **Metadata to be exported** section. You can add up to 10 key-value pairs. 7. Click **Update**. ## List of all export connectors * [Amazon S3](amazons3export) * [Azure Data Lake Storage Gen2](adlsgen2export) * [Campaign Monitor](campaignmonitorexport) * [Facebook Ads](facebookadsexport) * [Google Ads](googleadsexport) * [Klaviyo](klaviyoexport) * [Salesforce Marketing Cloud](sfmcexport) * [SendGrid](sendgridexport) * [Actions](actions) * [Triggers](triggers) * [Throttling](throttling) ## See also * [Dataflows](dataflows) * [Platform Schedule Settings](schedule) * [Connectors](connectors) # Source: https://docs.skypoint.ai/docs/facebookadsexport # Facebook Ads Manager Export lets you extract data from the Lakehouse and load into Facebook Ads Manager. ## Steps to Export 1. Navigate to **Activate** > **Export**. You will see all the different categories of storages that can be used for export. Alt text 2. Go to **Destinations** and click on ***Add Export*** if the export connect is not there in the export gallery. 3. Fill up the name and the display name of the export and click on the dropdown column to select one of the type options. Here we select Facebook Ads. Alt text 4. As soon as we select it, there will be a choice to either select an existing dataflow or to create a new one. 5. In case you are choosing to create a new one, fill up the **APP Id**, **APP Secret**, **Access Token** and the **AD Account ID** and then click on NEXT. 6. You will be able to see all the Lakehouse data available infront of you entity wise. Choose all the entities that you will need to export. 7. Click on **create** and then click on three dot button and select Run. 8. In case you are choosing to select from existing, fill up the details **Select Account**, **APP ID**, **APP secret**, **Access Token**, **AD Account Id**. 9. Click on Next. 10. You will be able to see all the Lakehouse data available infront of you entity wise. Choose all the entities that you will need to export. 11. Click on **create** and then click on three dot button and select Run. 12. If the export connector- Facebook Ads manager is already present in the export gallery then click on setup. 13. Fill the details- Name, Display name , select from options- Select from existing, Create new In case you are choosing to create a new one, fill up the **APP Id**, **APP Secret**, **Access Token** and the **AD Account ID** and then click on NEXT. 14. You will be able to see all the Lakehouse data available infront of you entity wise. Choose all the entities that you will need to export. 15. Click on **create** and then click on three dot button and select Run. 16. Your export will be started. 17. In case you are choosing to select from existing, fill up the details **Select Account**, **APP ID**, **APP secret**, **Access Token**, **AD Account Id**. 18. Click on Next. Here all the Lakehouse data will be available Entity wise. * Select the enities from the list to be exported. * Click Create and then click on three dot button and select Run. * Your export will be started. ## Steps to Extract App Credentials for Facebook Export 1. To export, you need to have an app created on developer's website. 2. Navigate to the [Developer's Portal](https://developers.facebook.com/apps/) and click on **Create App**. Alt text 3. Choose which type of app you want to create and click on Continue. 4. Enter the name of the display name of the app, the contact email and add the purpose for creating the app. Alt text **Note :** Choosing the business manager account is optional but if you have one, selecting it would be preferable. 5. Your app is created and you will be redirected to the Dashboard. Alt text 6. The last thing that you need to do is to Integrate the Facebook Marketing API with your app. Under the section **Add Products to your App**, watch out for **Marketing API** and click on ***Set Up***. Alt text 7. Then, click on **Tools**, and select the token permissions and click on **Get Token** to extract the access token. Alt text 8. You can get the **App ID** and **App Secret** when you navigate to **Settings** > **Basic**. The AD Account ID is the number written in parenthesis after the Business manager account name and has a prefix **act\_**. Alt text # Source: https://docs.skypoint.ai/docs/fhir # Connecting to FHIR The Fast Healthcare Interoperability Resources (FHIR) connector enables efficient, secure exchange and management of healthcare data in compliance with the FHIR standard. It integrates with business intelligence tools for real-time data visualization and analysis, enhancing organizational efficiency and informed decision-making. Additionally, the connector supports various authentication methods, including basic authentication and OAuth tokens, ensuring secure access to FHIR servers. Fast Healthcare Interoperability Resources (FHIR) connector seamlessly integrates with the skyData Platform (AIP), facilitating efficient and secure healthcare data exchange. By connecting with business intelligence tools, it enables real-time data visualization and analysis, empowering organizations with intelligent automation and predictive analytics. This streamlined workflow enhances efficiency, ensures compliance, and supports informed decision-making. This document will guide you through the process of connecting FHIR to the skyData. ## Prerequisite You need the following details to configure and import data using the FHIR⇗ connector: * Display Name * Provider Name ## To import data using the FHIR connector Follow the below steps to create and configure a new dataflow for the FHIR import connector: 1. Go to **Dataflow > Imports**. Alt image 2. Click **New dataflow** (indicated by an arrow). The **Set dataflow name** page appears. Alt image 3. Enter the dataflow name in the **Name** text area. 4. Click **Next** to proceed. The **Choose connector** page appears. Alt image ## Add Workday connector 1. On the **Choose Connector page**, use the Search feature to locate and select the **FHIR** Connector. Alt image 2. Enter the **Display Name** for your dataflow in the text area. 3. You can add a **Description** in the text area. 4. Click **Next** to proceed. The **Configuration** page appears. Alt image ## Connect to the FHIR account 1. Fill in the required details on the **Configuration page**. 2. Click **Connect**. Once the connection is established, you can use the connector to import data from a table in FHIR. Alt image 3. Scroll down to the **Table Details** section, select the checkboxes for the tables you wish to import, and use the dropdown menu to label them as either **Data** or **Metadata**. Alt image In the **Table Details** section, by default, all tables are selected. You can mark only those tables that you want to import and process the data. For example, to import customer data, you can check those tables which contain customer information, such as name, email, address, and contact details. | Item | Description | | :---------- | :-------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data | | Metadata | Loads Metadata | | File name | Displays the name of the file that you imported. | | Entity name | Displays the imported table name by default. You can rename it if required. | 5. Click **Save** to apply the changes. Alt image After saving the connection, the FHIR connector appears on the **Dataflow** > **Imports** page. ## Run, edit, and delete the imported data Once the table is imported, you can execute, modify, and remove the imported table from the Dataflow. Follow the below steps: 1. Go to the **Dataflow** > **Imports** page. Alt image | Item | Description | | :---------------- | :------------------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables count | Displays the number of tables imported. | | Created Date | Displays date of creation. | | Last refresh type | Displays the refresh value: Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Group by | Option to view the items in a specific group. For example, type, status, tables count, etc. | 2. Select the horizontal ellipsis under the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run history**. | Alt image You can see the error message corresponding to failure while importing data from a data source in the Dataflow under **Run History** > **Description**. Also, you can see the status, start time, and end time for the execution of the data pipeline. ## Next step After completing the data import, start the [Master Data Management (MDM) - Resolve](https://docs.skypointcloud.com/docs/stitch.html) process to create a single, unified view of the data. With SkyPoint MDM, you can ensure that your data is accurate, consistent, and reliable, making it easier to use for business processes, analytics, and reporting. # Source: https://docs.skypoint.ai/docs/fivetran # Connecting Fivetran and Databricks on skyDataP ## Overview Skypoint provides built-in connectors and the flexibility to integrate with external tools like Fivetran, dbt, Power BI, and Tableau to seamlessly import data from user databases. With Fivetran, users can securely transfer data from multiple sources to Databricks on skyDataP within a robust cloud network. This guide will walk you through the steps to set up your data pipeline. Find the architectural outline of the data pipeline as follows: Alt text ## Prerequisite * You have an active account with Fivetran and skyDataP & credentials to access them. ## Setup Guide to Connect Fivetran and Databricks on skyDataP The setup involves two key steps: * Integrating your Data Source with Fivetran * Connect Fivetran to Databricks on skyDataP ## Step 1: Integrate your Data Source with Fivetran Fivetran offers a comprehensive range of connectors to integrate with your databases. 1. Follow the Fivetran setup guide to configure the connectors, required to set up connection with your data source. 2. Prepare the following prerequisites for your source and destination to streamline the setup: * Valid database credentials * An active Fivetran account * skyData account with access to the Studio. ## Step 2: Connect Fivetran to Databricks on skyDataP ### Actions on skyData Studio * Log in to your skyData Studio account. * Go to Lakehouse > Workspaces. * Click the Show Fields button to display connection strings. Alt text * Copy the following credentials: * Server Hostname * HTTP Path * Personal Access Token Alt text ### Actions on Fivetran * Log in to your Fivetran account. * From the left panel, navigate to Destinations. Alt text * Click Add Destination. Alt text * Select Databricks as your destination. This selection is based on the fact that Skypoint Lakehouse is built on Databricks, ensuring seamless integration, enhanced compatibility, and optimal performance. Alt text * Name the destination by following the naming convention for the destination as follows: `skypoint_aip____lakehouse_bronze` **Components:** * **`skypoint_aip`**: Identifies the skyDataP platform. * **``**: Placeholder for the tenant's name. * **``**: Placeholder for the instance name. * **`lakehouse_bronze`**: Specifies the Bronze layer in the Lakehouse architecture. Alt text * Key in the required credentials by copying from skyData Studio. Alt text * Under **Authentication Type,** select **Personal Access Token** from the dropdown menu. * Key in the personal access token in the field below. * Click Save & Test to verify the connection. Alt text ### Congratulations! You’ve successfully connected Fivetran with Databricks on skyDataP. Your data pipeline is ready to sync securely! ## Post-Setup Verification * The new connection will appear on the Destinations page. * Click on the connection to view its details. * Use the **Actions** menu to: * Test the connection * Rename the destination * Edit connection details Alt text ## Next step After completing the data import, start the [Master Data Management (MDM) - Stitch](stitch.md) process to develop a unified view of your customers. # Source: https://docs.skypoint.ai/docs/fmx # FMX ## Overview The FMX connector integrates the GoFMX facilities management platform with skyData, bringing work order, equipment, and property operations data into your unified data environment. FMX is a facilities management platform used by schools, districts, and organizations to manage work requests, preventive maintenance, resources, and building operations. By connecting FMX to skyData, organizations gain a consolidated view of their facilities operations alongside workforce, financial, and operational data. ## What Data We Extract The FMX connector brings in key facilities and operational data including: * **Organization** — Top-level organizational structure and community information * **Buildings** — Property records, building details, and location information * **Work Requests** — Submitted maintenance and service requests with current status * **Work Request Assignments** — Staff assignments and ownership of each work request * **Work Request Options** — Configuration and categorization options for work requests * **Request Types** — Categories and types used to classify work requests * **Work Tasks** — Scheduled preventive maintenance tasks and recurring work items * **Work Task Occurrences** — Individual occurrences of each scheduled task with completion tracking * **Work Task Instruction Sets** — Step-by-step instructions associated with maintenance tasks * **Resources** — Equipment, assets, and physical resources tracked in FMX * **Equipment Options** — Configuration options and attributes for tracked equipment * **Users** — Staff and user accounts within the FMX platform * **User Types** — Role classifications for FMX users * **Schedule Request Occurrences Options** — Configuration for scheduled and recurring requests ## Why It Matters Organizations rely on well-maintained facilities to deliver quality services and safe environments. Connecting FMX to skyData enables organizations to: * Combine facilities and maintenance data with workforce and operational analytics * Track work order trends across buildings and locations to identify recurring issues * Monitor preventive maintenance completion rates alongside operational performance * Build comprehensive dashboards that include facilities health alongside clinical or academic outcomes * Identify resource utilization patterns and equipment maintenance history across properties ## Who Benefits | Role | Value | | -------------------------- | ------------------------------------------------------------------------------------- | | Facilities Directors | Cross-property visibility into work orders, task completion, and maintenance backlogs | | Operations Leaders | Unified view of building health and resource utilization across the organization | | Finance and Planning Teams | Maintenance cost and resource data combined with operational and financial metrics | | Executive Leadership | Portfolio-level facilities performance alongside organizational outcomes | ## Get Started Contact your Skypoint representative to configure the FMX connector for your organization. # Source: https://docs.skypoint.ai/docs/frontline # Frontline ## Overview The Frontline connector integrates the Frontline Education platform with skyData, bringing recruiting, hiring, and absence management data into your unified data environment. Frontline Education is a platform built specifically for K-12 school districts and educational organizations. It provides two core capabilities: **AppliTrack** for recruiting and hiring, and **AESOP** for substitute and absence management. By connecting Frontline to skyData, education organizations gain a consolidated view of their workforce pipeline and daily staffing operations alongside student outcomes and financial data. ## What Data We Extract The Frontline connector covers two product areas: **Recruiting and Hiring (AppliTrack)** * **Applications** — Candidate applications including submission details and current status * **Application Job Progress** — Stage-by-stage progress of each application through the hiring pipeline * **Pipeline Stages** — Hiring workflow stages and their configurations * **Vacancies Selected** — Positions that candidates have applied to or been selected for * **Job Postings** — Open positions posted for recruitment * **Field Responses** — Candidate answers to application form questions * **Application References** — Reference submissions linked to candidate applications * **Application EEO Data** — Equal Employment Opportunity data associated with applications * **Education** — Candidate educational background and qualifications * **Experiences** — Candidate work history and professional experience * **Certificates** — Professional certifications and licenses held by candidates * **Certificate Details** — Detailed records of each certification including status and expiration * **Notes** — Internal notes added to candidate records during the hiring process * **Locations** — School and district locations associated with job postings * **Positions** — Defined positions and roles within the organization **Absence Management (AESOP)** * **Absence Data** — Substitute and absence records including dates, reasons, and fill status ## Why It Matters Education organizations manage complex, ongoing hiring cycles and daily staffing challenges. Connecting Frontline to skyData enables organizations to: * Combine recruiting pipeline data with workforce headcount and retention metrics * Track time-to-hire and candidate pipeline health across schools and departments * Monitor absence and substitution patterns to identify staffing risk by school or grade level * Align hiring data with student outcomes to understand the impact of staffing decisions * Build district-wide workforce dashboards that span recruiting, absence, and HR data ## Who Benefits | Role | Value | | --------------------------------------- | --------------------------------------------------------------------------------------- | | HR Directors | End-to-end visibility into the recruiting pipeline and time-to-hire across the district | | Principals and School Leaders | Absence patterns and substitution coverage data specific to their school | | Workforce Analysts | Combined recruiting and absence data for holistic workforce planning | | Superintendent and Executive Leadership | District-level staffing health alongside student performance and operational data | ## Get Started Contact your Skypoint representative to configure the Frontline connector for your organization. > **Note:** The Frontline connector supports both AppliTrack (Recruiting) and AESOP (Absence Management). Absence Management can be enabled or disabled independently during setup. # Glossary Source: https://docs.skypoint.ai/docs/glossary ## Operations Ontology ### skyData The brain of Skypoint's agentic ecosystem, enabling private, compliant, compound AI systems tailored to healthcare operations. skyData harmonizes 250+ data sources and enriches them with healthcare ontologies in a low-code/no-code environment designed for building, testing, and deploying healthcare-specific AI agents. ### skyMDM Master Data Management solution providing AI-driven match, merge, dedupe, and real-time 360° data capabilities. It creates a single source of truth across healthcare organizations by resolving identities and maintaining accurate, connected records. ## Applications & Agents ### skyAdmissions Automated referral intake, scoring, eligibility checks, and reimbursement optimization for SNFs (Skilled Nursing Facilities). Streamlines the admissions process to maximize occupancy while ensuring appropriate patient placement. ### skyAdvisor AI sales agent that converts inquiries into tour-ready leads 24/7. Engages prospects through chat, web forms, and surveys with intelligent conversation, then seamlessly hands off qualified leads to your sales team for personal follow-up. ### skyAuth - Provider Automates the entire prior authorization (PA) lifecycle from the provider perspective, reducing manual workload and accelerating patient access to care. ### skyAuth - Payor Streamlines the prior authorization review and decision process for health plans. Automates medical necessity reviews, reduces turnaround times, and improves consistency in authorization decisions. ### skyCare - Senior Living Enables care teams to improve resident outcomes and satisfaction through seamless integration with clinical workflows. Provides specialized tools for assisted living, memory care, and independent living communities. ### skyCare - VBC Enables care teams to close gaps, improve outcomes, and meet VBC (Value-Based Care) metrics through seamless integration with clinical workflows. Supports population health management and risk-based contracting success. ### skyChat Secure, healthcare-trained conversational AI for instant answers and advanced reasoning. Provides staff and patients with intelligent, compliant assistance powered by healthcare-specific knowledge. ### skyCommand Orchestrates predictive insights, proactive alerts, and workflow automation in real time. Serves as the intelligent command center for healthcare operations. ### skyComply Automates corrective actions and compliance workflows to ensure timely, verifiable quality and regulatory readiness. Helps healthcare organizations maintain continuous compliance with evolving regulations. ### skyInsights Real-time financial and operational analytics integrated into Power BI. Transforms complex healthcare data into actionable insights that drive better decision-making. ### skyPrivacy Self-service privacy portal with automated DSR (Data Subject Request) processing for effortless compliance. Empowers individuals to exercise their privacy rights while reducing the burden on privacy teams. ### skyReferral Reduces leakage by routing referrals to the right provider or facility. Optimizes the referral process to keep patients within your network while ensuring appropriate care. ### skyReputation AI-powered SEO and reputation improvement through smarter review collection, aggregation, and action. Helps healthcare organizations build and maintain a strong online presence. ### skyRFP AI automation for RFP (Request for Proposal) workflows used by health plans. Accelerates the RFP response process while improving quality and consistency. ### skySign Enterprise-grade electronic signature solution for healthcare organizations. Supports senior living move-ins, provider agreements, and general business documents with HIPAA-compliant workflows, available as full SaaS or self-managed deployment. ### skyLearn AI-powered learning management system built for healthcare operators, part of skyWorkforce. Manages the complete training lifecycle from course creation to certification issuance with AI-driven automation for scheduling, compliance tracking, and learner engagement. ### skySupport AI-powered enterprise helpdesk and support ticketing platform for healthcare, senior living, and HR organizations. Features intelligent ticket management, AI-powered knowledge base, unified agent workspace, and service catalog with compliance-ready design. ### skyHire AI-powered recruiting, hiring, and applicant tracking system for healthcare organizations, part of skyWorkforce. Features skyAgent (autonomous AI recruiter), skyJobs (branded career pages), recruiting automation, and digital onboarding—delivering faster time-to-hire with 75% lower cost. ### skyVoice Human-like AI that manages inbound and outbound calls instantly. Provides 24/7 intelligent voice assistance for healthcare organizations. ## Platform Concepts ### Dataflow Data import, export, connections, transformations, and data quality management within the Skypoint platform. ### Entities Each data source ingested from the dataflows is added as an entity. Entities represent tables of data that can be mapped, matched, and merged. ### Export Exports entities to destinations using export connectors provided by the platform. ### Instances Subsidiaries of a single tenant. As soon as you create an account on the platform, a default instance is created (Sandbox). ### Lakehouse Brings together the benefits of a data lake and data warehouse to create a new open data management architecture based on Delta Lake. ### Ontology Used to create associations/relationships between entities for use in analytics and applications. ### Tenants Each customer organization on the Skypoint platform. ### Transformations Data transformation capabilities using dbt Core for preparing and enriching data within the platform. # Source: https://docs.skypoint.ai/docs/googleadsexport # Google Ads Manager Export lets you extract data from the Lakehouse and load into Google Ads Manager. ## Steps to Export 1. Navigate to **Active** > **Export**. You will see all the differenet categories of storages that can be used for export. 2. Go to **destimations** and click on \***Add Export** if the export connect is not there in the export gallery 3. Fill up the name and the display name of the export and click on the dropdown column to select one of the type options. Here we select Google Ads. Alt text 4. Fill up the **Customer ID** and **Developer Token** and then click on **Authenticate Google Ads**. It will open a pop up where you will be asked to sign in using your Google Ads Account. 5. **Note** : In order to get the customer ID, sign in to the google ads portal and click on your profile icon present on the top right of the window. There will be a XXX-XXX-XXXX format number visible there, which will be your **customer ID**. To get your **developer token**, you need to sign in to the manager google ads account, and navigate to **Tools & Settings** > **API Center** and get the developer token from there. If not present, you can create a new one too from the same destination. 6. Once you enter all required credentials and authenticate your account, click on **Next**. You will be able to see all the Lakehouse data available infront of you entity wise. Choose all the entities that you will need to export. 7. Click on **Save**. Your export will be saved. You can run it now and the entities will be exported. If the export connector- Google Ads manager is already present in the export gallery then click on setup. Fill the details- Name, Display name, Customer ID, Developer Token. Click Next. Here all Lakehouse data will be available Entity wise. * Select the enities from the list to be exported * Click Create and then click on three dot button and select Run. * The export will be started. # Google Cloud Storage Source: https://docs.skypoint.ai/docs/googlecloudstorage Connect Google Cloud Storage to import data into Skypoint AI ## Overview Google cloud Storage enables you to store, access, and maintain data so that you do not need to own and operate data centers, moving expenses from a capital expenditure model to operational. You can use SkyPoint AI's built-in connector for importing data from Google Cloud Storage. This document will guide you through the process of connecting Google Cloud Storage to SkyPoint AI. ## Prerequisite You will need the following details to configure and import data using [Google Cloud Storage](https://cloud.google.com/storage): * Access key ID and Secret access key * Project ID * Service URL * Service account JSON file * Google cloud bucket. ### Permissions required for Service account You can control the access to the resources in your Google Cloud project with the help of [IAM](https://cloud.google.com/storage/docs/access-control/iam) (Identity and Access Management). The resources comprise of Cloud Storage buckets and objects that are stored within buckets, along with other Google Cloud entities such as [Compute Engine instances](https://cloud.google.com/compute). [Permissions](https://cloud.google.com/transfer-appliance/docs/4.0/prepare-permissions) allow principals to perform specific actions on buckets or objects in Cloud storage. There are two options to prepare the Google cloud permissions and storage: * Option 1: Using the Transfer Appliance Cloud Setup Application * Option 2: Configuring Google Cloud permissions and Cloud Storage step-by-step. You need to create the JSON file and Permissions by logging into Google Cloud Console. You can refer to the Setting up Google Cloud Storage document to learn more. ## Import data using Google Cloud Storage connector Follow the below steps to create a new dataflow for the Google Cloud Storage import connector: 1. Go to **Dataflow > Imports**. 2. Click **New dataflow**. The **Set dataflow name** page appears. Set dataflow name 3. In the **Set dataflow name** page, type dataflow name in the **Name** text area. 4. Click **Next**. The **Choose connector** page appears. Choose connector ## To add Google Cloud Storage connector 1. In the **Choose connector** page, select **Google Cloud Storage** connector. You can use the **Search** feature too to find the connector. Also, the **Google Cloud Storage** connector can be found under **Cloud** category. Set dataflow configure 2. Enter **Display Name** for your dataflow in the text area. 3. Enter **Description** for your dataflow in the text area. 4. Click **Next**. The **Connect to Google Cloud Storage** page appears. Configuration page ## To configure Google Cloud Storage Follow the below steps to configure the connection to Google Cloud Storage: 1. Enter your credentials such as **Access key ID**, **Secret access key**, **Project ID**, and **Service URL** to configure with Google Cloud Storage. 2. Click **Choose file** to upload the JSON file. 3. Click the **Folder icon** in the **Google cloud bucket** text area. Once you select the cloud bucket, the **Table Details** columns appear. Table details 4. Enter the **Table Details** to process the data. | Item | Description | | :------------------ | :----------------------------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data. | | Metadata | Loads Metadata. | | File Name | Displays the name of the file that you imported. | | Table Name | Displays the imported table name. | | Datetime format | Displays a number of **Datetime Formats** and Skypoint AI is set to automatically detect them. | | Delimiter | Displays available separators for the variables in the imported data. | | First Row as Header | Check the box for the system to automatically collect the data according to the Header Contents. | | Advanced Settings | Select the options to fine tune the Import process with minute details. | 5. Click the **Advanced settings** for your desired file name. The **Advanced settings** pop-up appears. Advanced settings | Item | Description | | :--------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Compression type | Method that is used for compressing the details from source, Azure Data Lake Storage Gen2. | | Row delimiter | A separator that identifies the boundaries of the flow of a data stream. In case, a different separator is used in it, the information requires change for more accuracy in data ingestion. | | Encoding | As the data comes in data stream, there is a type of encoding used for deciphering it. The default encoding is **UTF-8**. | | Escape character | It is a particular case of metacharacters that is given an identification of start or end. You can manually select it from the drop-down list. | | Quote character | You can select one of the advanced Quote characters mentioned in the drop-down list. | 6. Click **Save** on the **Advanced settings** pop-up to save the advanced settings. 7. Click **Save**. ## Run, edit, and delete the imported data Once you save the connector, the Google Cloud Storage connector gets displayed in the list of tables created in the Dataflow page. Import output | Item | Description | | :---------------- | :------------------------------------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables. | | Created Date | Displays date of creation. | | Last refresh type | Displays the refresh value. You can see whether the value is Full or Incremental after the last data refresh. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time the data is refreshed. | | Group by | Option to view the items in a specific Group (For example, name, type, status). | * Select the horizontal ellipsis in the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run** history. | ## Overview Specific actions can be performed on buckets or objects in Cloud Storage by principals. This is enabled by Permissions. The process of giving permissions to the principals includes granting of roles, comprising of one or more permissions bundled with them. ## Prerequisite You need the following resources to set up permissions for service account: * Google Cloud Storage bucket * Google Cloud account. ## Download JSON to Set up Google Cloud Storage Follow the below steps to download the JSON file from Google Cloud Storage console: 1. Log in to your [Google Cloud Storage console](https://developers.google.com/maps/premium/devconsole-access). 2. Go to **IAM & Admin > Service Accounts**. Console Service account You can find your buckets under Cloud Storage > Buckets. To create new buckets, click on **Create**. 3. In the Service accounts page, click the **Vertical ellipsis** under **Actions** column. The **Actions** menu appears. Console Service account created 4. Click **Manage keys**. 5. Click **ADD KEY > Create new key**. The **Create private key** pop-up appears. Console Key JSON 6. Select **JSON**. 7. Click **CREATE**. The **Service Account JSON** file gets created. Private key output You will have to upload this JSON file by clicking on Choose File while configuring the Google Cloud Storage import connector on Skypoint AI. ## To add Permissions for Service Accounts Follow the below steps to add permissions for your service accounts: 1. Go to **IAM & Admin > Service Accounts**. 2. Click **Permissions** tab. 3. Click **GRANT ACCESS**. Grant access 4. In the **Grant access** page, click **ADD ANOTHER ROLE**. Add role 5. Select a desired role to assign under **Cloud Storage** in the **Role** drop-down list. 6. Click **Save**. ### Create a custom Role In case, you need to create custom roles, follow the below steps: 1. Go to **IAM & Admin > Roles**. Create role 2. Click **CREATE ROLE**. To edit an existing role, click the vertical ellipses under the Status column. This new role can be assigned in the Service Accounts. 3. Select **General Availability** in the **Role launch** stage drop-down list. 4. Click **ADD PERMISSIONS** to create permission for the assigned role. 5. Click **CREATE**. Role edit ## Next step After completing the data import, start the [Master Data Management (MDM) - Stitch](stitch.md) process to develop a unified view of your customers. # Source: https://docs.skypoint.ai/docs/help+support # Help + Support ## Overview The Help + Support module provides a centralized support experience across all Skypoint products. Whether you're working in skyData, skyCopilot, skyReputation, or any other Skypoint application, you can easily access technical assistance, submit support requests, and track the resolution of your issues. ## Key Capabilities ### Support Request Management Create, submit, and manage support tickets directly within any Skypoint product. The unified support system ensures your requests are routed to the appropriate technical experts for timely resolution. ### Request Categories Submit support requests across four categories to help our team understand and prioritize your needs: * **Enhancement** - Request new functionality or upgrades to existing capabilities * **Issues** - Report bugs, deviations in functionality, or service-related problems * **Questions** - Seek information about products, services, or platform features * **Requests** - Ask for documentation, guidance, or specific information about the platform ### Priority-Based Support Assign priority levels to your requests based on business impact, ensuring critical issues receive immediate attention: | Priority | Level | First Response | Target Resolution | | ----------------- | ----- | ----------------- | ----------------- | | Business-Critical | P1 | 4 Business Hours | 1 Business Day | | Production Impact | P2 | Next Business Day | 2 Business Days | | System Impaired | P3 | Next Business Day | 5 Business Days | | General Guidance | P4 | 3 Business Days | 5 Business Days | ### Ticket Tracking Monitor the status and progress of all your support requests from a single view. Track ticket status (Active, Resolved, Canceled), view submission details, and access resolution timelines. ### Direct Communication Engage directly with the Skypoint support team through the ticket discussion feature. Add comments, provide additional context, and attach supporting documents to expedite resolution. ### Attachment Support Upload reference documents, screenshots, or log files to help the support team quickly identify and resolve issues. ## Accessing Help + Support Help + Support is available across all Skypoint products through: * The **Need Help?** button available throughout the platform * The dedicated **Help + Support** section in navigation menus * Settings pages within individual applications ## Business Benefits * **Unified Experience** - Single support system across all Skypoint products * **Faster Resolution** - Priority-based routing ensures critical issues get immediate attention * **Full Visibility** - Track all support requests and their status in one place * **Direct Access** - Communicate directly with technical experts handling your case ## Who Benefits * **All Users** - Access help and support from any Skypoint product * **Administrators** - Oversee support requests across teams and instances * **Operations Teams** - Report and track operational issues * **Business Users** - Get answers to product questions and feature requests # Source: https://docs.skypoint.ai/docs/hightouchintegration # Integrating Hightouch with Lakehouse SQL ## Overview Hightouch serves as a Reverse ETL (Extract, Transform, Load) solution, facilitating the extraction of data from a warehouse or database, and subsequently loading it into Sales, Marketing, and analytics tools via SQL, with no need for scripts. By leveraging this technology, you can synchronize your data from skyData Lakehouse SQL with Hightouch, without having to rely on APIs, CSVs, or engineering assistance. skyData is built to accommodate various analytics, machine learning, artificial intelligence, and business intelligence applications. skyData leverages Databricks SQL to create a connection between the Lakehouse in skyData and the Hightouch client. This connection allows for the seamless loading of data into your Sales, Marketing, or analytics tools. ## Prerequisites * Lakehouse SQL must be provisioned in your tenant. * Connection details, such as Server Hostname, Port, and HTTP Path, are required to establish a connection. * You must also have a personal access token from Lakehouse SQL for your tenant. Follow the below steps to integrate Hightouch with Lakehouse SQL: ## Connect Hightouch to Lakehouse SQL 1. [Login in](https://app.hightouch.com/login) with a Hightouch account. Alt text 2. Click **Create a workspace** to create a new workspace. If you have already created a workspace, then select an existing one. Alt text 3. Enter a name for a new workspace and click **Create workspace**. You can choose **Region** from the drop-down list. Alt text 4. In the left pane, click **Sources**. 5. Click **Add source**. Alt text 6. Click **Databricks**, and then click **Continue**. Alt text 7. Enter **Server Hostname**, **Port**, **HTTP Path**, and **Access Token**. You can get these deatils from Lakehouse > SQL access in Skypoint studio. Alt text 8. For Default Schema, enter the name of the target database. 9. Click **Test Connection**. Make sure that testing the source returns green before continuing to the next step. 10. Once the connection is successful, click **Continue**. Alt text 11. You can enter a name for the connection, and then click **Finish**. ## Connect to your destination Follow the below steps to set your destination: 1. In the workspace navigation pane, click **Destinations**. 2. Click the **Add destination** button to add your destination. You can also type your destination in the filter box. Alt text 3. Select your **Destination catalog** and click on **Continue**. For instance, in this section, we create a connection with the SendGrid connector for exporting data. Alt text 4. Enter **API Key** and then click **Continue**. Alt text 5. Enter a name for the destination, and then click **Finish**. Alt text ## Add a model in Hightouch Follow the below steps to add a model in Hightouch: 1. Select "Add model" and select the datasource for which the model has to be added. Alt text 2. Define the model. Hightouch provides 3 options for defining the model: Query with SQL, Create from table or view and import model from Sigma. Alt text 3. For instance,in this section we select Query with SQL to define a model. 4. After the model definition, provide basic details for the model like name, description and primary key. Alt text 5. Click on "Finish". ## Add a sync in Hightouch To add sync for models in Hightouch, follow these steps: 1. Click on "Add sync" and choose the model you want to sync. Alt text 2. Select the destination where you want the data to be synced. Alt text 3. Configure the sync to the destination by selecting the primary keys and mapping the necessary fields between the source and destination tables. Alt text 4. Set the schedule type for the sync and click "Finish". 5. Run the sync. ## Check the exported data in destination. To check the exported data in the destination after syncing with Hightouch, follow these steps: 1. Access the destination where the data was synced. 2. Look for the data that was exported from the source table and confirm that it has been successfully transferred to the destination. 3. Verify that the data is accurate and complete. 4. If needed, perform any necessary data cleansing or transformation. 5. The exported data is now available for further usage, such as reporting, analysis, or integration with other systems. # Source: https://docs.skypoint.ai/docs/imports # Imports ## Overview Dataflow allows you to import data from the relevant sources, transform, and load it into the Lakehouse. skyData imports raw customer data from across systems and applications with over 200 connectors spanning e-commerce platforms, marketing automation tools, email service providers, cloud platforms, and many more. You can ingest data to skyData from any data source including pre-built connectors with popular applications such as Shopify, Salesforce, etc. skyData Studio ingests raw data and transforms it into a standardized, modular, and extensible collection of data schemas for further processing in downstream processes such as [Master Data Management (MDM) - Resolve process](stitch). Alt text ## To add new Dataflow 1. In the left pane, go to **Dataflow** > **Imports**. The Dataflow window appears. Alt text 2. Click **New Dataflow** to create dataflow. Alt text 3. Enter a **Name** to identify your Dataflow. The name must start with a letter and can only contain letters and numbers, with no spaces between the characters. 4. Click **Next**. ## Add connectors 1. Choose one of the existing **Connectors** to access the data. Alt text ### Connectors categorization You can filter the list of connectors concerning the following categories: | Connector type | Description | | :--------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | All Categories | Displays all types of connectors that are available on the skyData platform. | | Analytics | You can simplify big data and run parallel data transformation and processing programs. These connectors enable you to connect with analytics-based connectors. | | Cloud | The Cloud connector is used to integrate with cloud providers. | | Data Warehousing | You can connect to the data management system that is designed to enable and support Business Intelligence (BI) activities. These connectors enable you to import data from data warehouses. | | Developer Tools | This is a set of services and features that connect you to develop a software application. | | Ecommerce | These connectors allow you to integrate with various eCommerce platforms and marketplaces for data exchange. | | Healthcare | These connectors allow you to connect with a health organization or insurer. Also, you can exchange Electronic Health Records (EHR). | | Marketing | You can import marketing data into skyData Lakehouse to gain new business insights. Marketing connectors synchronize data between a marketing environment and the skyData platform. | | Sales | You can import data with Sales connectors to build relationships and increase your sales. | 2. You can use the search function to find the available connectors. For more information about different type of connectors and their configuration, refer to the [Connector section](connectors). Alt text 3. Enter the **Display Name** and the **Description** of the connector. 4. Click **Next**. Alt text 5. Enter the account credentials to configure with the selected connector. For each connector, credentials may differ. You need to enter the relevant information based on the connector to configure and import the data. In this example, we have selected the Azure Data Lake Storage Gen2 connector to import data. For the Azure Data Lake Storage Gen2 connector, you need to enter the **Storage account name**, **Account key**, and **Storage path** to import the data. 6. If you want to keep the same date-time format for all the tables, select the check box. 7. Click **Save** 8. Select the **Table Details** to process the data. After configuration, all tables that are part of the connector are listed in the **Table Details**. By default, it displays a single table upon successful connection, showing information specific to the client connector and the tables it supports. For multiple tables, you can select only those tables you want to import and process. For example, to import customer data, choose the tables that contain customer information such as name, email, address, and contact details. Alt text | Item | Description | | :------------------ | :-------------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Load customer data | | Metadata | Load Metadata | | File name | Indicates file extension and the name of the file that you imported. | | Table name | Displays the imported table name by default. You can rename it later as required. | | Datetime format | Option to select the Datetime data in a different format. | | Delimiter | Indicates characters that separate text strings. | | First row as header | Indicates the first row of data as a header. | You can select the Datetime in MM/DD/YYYY format, for example, 11/25/1979. It helps the system to understand the data format and apply further formatting if required during downstream processes. The same Datetime format as set in the [instance settings](instance) can be seen for the Datetime fields in the profile card. If you wish to see the profile card with Datetime as imported from the source, you can unselect the option in **Stitch** > **Map** > **advanced settings**. 9. If necessary, apply the **Advance settings** to modify the default settings. Advanced settings allow you to modify the default settings. It gives more flexibility to apply advanced use cases. However, the default settings are adequate to perform the task. Alt text | If you want to | Then | | :----------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------- | | Modify data types such as fixed or variable data types. | Select from the **Compression type**. It allows you to reduce the size of data by removing the number of bits. | | Change the delimiter | Click **Row delimiter**. By default, a column delimiter is selected, and each row is separated with a comma. | | Change information or instruction | Choose from the **Encoding** list. By default, UTF-8 encoding is selected. | | Modify the escape character such as backslash (\\) or slash (/) | Select from the **Escape character**. | | Apply different quote characters such as Single quote (') or Double quote ("). | Select from the **Quote character**. | 10. Click **Save** to apply the changes. ## Run, edit, and delete the imported data 1. After saving the connection, imported data appears on the Dataflow page. Also, you can see a list of the created tables in the [Databases](entities) section. Alt text | Item | Description | | :---------------- | :------------------------------------------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables. | | Created Date | Displays date of creation. | | Last refresh type | Displays the refresh value. After the last data refresh, it will indicate whether the value is Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Group by | Option to view the items in a specific Group (For example, name, type, status). | 2. Select the horizontal ellipsis in the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run history**. Run history for Dataflow imports is available for the last three runs. | The **Run history** for SQL connector import in Dataflow shows all the states for ingestion and the completion time reflects the complete time for the pipeline completion. Also, you can see the error message corresponding to failure while importing data from a data source under Description. Alt text ## To configure Email Notification for pipeline status Now you can set up email notifications to stay informed about pipeline success or failure events in real time. Follow these steps to configure notifications. 1. Click on the **Pipeline name** for which you want to configure notifications. Alt text Configuration page appears Alt text 2. Click **Notification Settings** on the right corner of the screen. 3. Notification settings form appears, check the box **Enable Notifications** to proceed. Alt text 4. Enter the mail id to **Add user**. 5. Click on the **Add** button to add multiple users. 6. Access the **Manage User** option to configure notifications for pipeline completion of statuses of success, failure, or both. Alt text 7. Click **Save** to apply for changes. 8. User will get the mail notification based on the configuration, such as **Sucess/Failure** or **Both**. Notifications set for a specific connector are limited to that connector only. You won't receive updates for other connectors unless configured separately. 9. Follow the same steps to configure notifications for other pipelines. ## Get assistance for your connector In case, you are unable to find your desired import connector, you can perform one of the action in the **Looking for more** section. You can raise a support ticket for the Skypoint team to look into your request. ## List of all import connectors * [Alis](alis) * [August Health](augusthealth) * [AWS Cognito](awscognito) * [Amazon Redshift](amazonredshift) * [Amazon S3](amazonsthree) * [Azure Synapse Analytics](azuresynapseanalytics) * [Microsoft Azure Blob Storage](azureblobstorage) * [Microsoft Azure Data Lake](azuredatalakestoragegen2) * [Boulevard](boulevard) * [Campaign Monitor](campaignmonitor) * [CMS SNF](cmssnf) * [Dataverse](dataflow-import-dataverse) * [Dayforce](dayforce) * [DockHealth](dockhealth) * [Dotdigital](dotdigital) * [Dr Chrono](drchrono) * [Microsoft Dynamics 365](dynamics365) * [FHIR](fhir) * [Google Cloud Storage](googlecloudstorage) * [Infor HMS](inforhmsimportconnector) * [iSolved](isolved-importconnector) * [KWI](kwi) * [Klaviyo](klaviyo) * [MRINexcore](mrinexcore) * [MySQL](mysql) * [NetSuite](netsuite) * [Paychex](paychex) * [PayNW](paynw) * [PayNW Reports](paynwreports) * [PointClickCare](pcc) * [Reftab](reftab) * [SFTP](sftp) * [Salesforce](salesforce) * [Salesforce Marketing Cloud](salesforcemarketingcloud) * [SharePoint](sharepoint) * [Shopify](shopifyimport) * [Sitelligence](sitelligence) * [Snowflake](snowflakeconnector) * [SQL Server](sqlserver) * [Square](square) * [Stripe](stripe) * [UKG PRO](ukgpro) * [UKG WFM](ukgwfm) * [WelcomeHome](welcomehome) * [Workday](workday) * [Workday Raas](workdayRaaS) * [Zenoti](zenoti) ## Next step After completing the data import, start the [Master Data Management (MDM) - Stitch process](stitch) to develop a unified view of your customers. ## See also * [Dataflow](dataflows) * [Connectors](connectors) * [Databases](entities) # Source: https://docs.skypoint.ai/docs/inforhmsimportconnector # Connecting to Infor HMS # Import data ## Overview Infor HMS creates full industry suites in the cloud and deploys technology that prioritizes the user experience, leverages data science, and integrates easily into existing systems. You can use skyData's built-in connector for importing data from Infor HMS. This document will guide you through the process of connecting Infor HMS to skyData. ## Prerequisite You will need the following details to configure and import data using [Infor HMS](https://www.infor.com/products/hms): * Display Name * Account Key * Storage Path. You can refer to the Infor HMS Setup Guide document to learn more about the Prerequisites. ## To import data using Infor HMS connector Follow the below steps to create a new dataflow for the Infor HMS import connector: 1. Go to **Dataflow > Imports**. 2. Click **New dataflow**. The **Set dataflow name** page appears. Alt text 3. In the **Set dataflow name** page, type dataflow name in **Name** text area. 4. Click **Next**. The **Choose connector** page appears. Alt text ## To add Infor HMS connector 1. In the **Choose connector** page, select **Infor HMS** connector. Use the Search function to locate the connector. You'll find the Infor HMS connector under the Healthcare category. Alt text 2. Enter **Display Name** for your dataflow in the text area. 3. Enter **Description** for your dataflow in the text area. 4. Click **Next**. The **Infor HMS configuration** page appears. Alt text ## To configure Infor HMS Follow the below steps to configure the connection to Infor HMS: 1. Enter **Display account name** in the text area. 2. Enter **Account key** in the text area. 3. Click the **Folder icon** in **Storage path** text area. Once you select the storage path, **Table Details** columns appear. Alt text 4. Enter the **Table Details** to process the data. | Item | Description | | :--------- | :------------------------------------------------------------ | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Load customer data. | | Metadata | Load Metadata. | | File Name | Displays the name of the file that you imported. | | Table Name | Displays the imported table name. | 5. Click **Save**. ## Run, edit, and delete the imported data Once you save the connector, the Infor HMS connector gets displayed in the list of tables created in the Dataflow page. Alt text | Item | Description | | :---------------- | :------------------------------------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables. | | Created Date | Displays date of creation. | | Last refresh type | Displays the refresh value. You can see whether the value is Full or Incremental after the last data refresh. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time the data is refreshed. | | Group by | Option to view the items in a specific Group (For example, name, type, status). | * Select the horizontal ellipsis in the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run** history. | # Setup guide ## Overview This document will help you gather all credentials for connecting [Infor HMS](https://www.infor.com/products/hms) with skyData. ## Prerequisite You must have the following details: * Infor HMS account * API for integration. ## To call API skyData will expose API for the following five entities: * Reservations * GuestProfile * Inventory * Rate * GuestStayRevenue Once the APIs are called, a data dump will be created in your skyData integrated Storage account. ## Finding Storage account The **Storage account name** will be shared with you from skyData. ## Finding Account key The **Account key** will be shared with you from skyData. ## Selecting Storage Path Follow the below steps on the skyData platform Configuration page to select Storage Path: 1. Click the folder icon in **Storage Path** text area. 2. Select **raw-data** and then select the folder inside it. 3. Select **HMS**. Alt text 4. In the **HMS** folder, click **Select**. Follow the steps in Connecting to Infor HMS import connector to complete your integration. # Source: https://docs.skypoint.ai/docs/insights # Using skyData Insights ## Overview skyData **Insights** feature integrates Power BI’s reporting capabilities into the Skypoint platform, providing a seamless and secure analytics experience. Instance Admins and above can create custom Insights, each accessible through a customized unique web URL. By organising multiple Power BI reports under a single Insight, this feature delivers a centralized and efficient way to manage and present analytics. It streamlines data access, enables informed decision-making, and enhances operational efficiency across teams. ## How to configure and manage Insights on skyData Studio User can create Insights on skyData studio by following the below steps: ## A. To setup a connection 1. Sign in to skyData Studio. 2. Navigate to Dataflow and choose **Connections**. 3. Click on **Set up** for Power Bi. Alt text 4. Fill in the required connection details and **validate credentials**. Alt text 5. Click the **SAVE** button to confirm the changes. A configured connection can be used to set up multiple Insights. ## B. To add new Insights 1. In the left pane, go to **Applications** > **Insights**. The Insights window appears. Alt text 2. Click on **New Insights** to create new insight. The Add Insights window appears Alt text 3. Enter the **name** for the Insight to appear across the platform. 4. Enter the **Access endpoint**(url). To Add Power Bi Report: 1. Select the **Category** of power bi connection from the dropdown menu. 2. **Report Name** will be auto populated based on the category of connection. 3. Enter power bi **Workspace ID**. 4. Enter power bi **Report ID**. 5. Click on **Save** to add the report. You can see the report in the **Added Reports** window. Multiple reports can be added to an insight by instance admin. 7. Click on the **Add Report** button to add more reports,Follow the same steps to add reports. ## C. To edit existing Insights Follow the steps below to change or edit the existing insights 1. Select an **Insight** from the list to make changes. 2. Click on **Edit** to edit the insight. Alt text 3. You can change the **Name** of existing Insight. 4. Add the new report to the **existing** Insight. 5. Delete the report by clicking the **delete** icon on the added report window. | Item | Description | | :------------ | :------------------------------------------------------------------------------------------------------------------------------------------- | | Report Name | Name of the Report | | Bi Connection | Category of the Power Bi connection | | Workspace ID | The workspace id of the power bi report | | Report ID | Report id of the power bi report | | Action | Refers to the menu or control within the Insights where users can perform tasks such as **edit** or **delete** a report linked to an Insight | The access endpoint will be inactive and cannot be edited once set. Ensure the correct endpoint is configured during initial setup. ## D. To delete existing Insights Follow the steps below to delete the existing insights 1. Select the insight which you want to delete. 2. Click the **Delete** tab located at the top of the page. Alt text Confirmation page appears with the insight details Alt text 3. Click on the **delete** button to remove the insight. Once an Insight is deleted, it is permanently removed and cannot be undone. Please proceed with caution. ## E. To Activate Access Endpoint for an Insight This step is applicable for adminstrator only This process links your Insight application to a custom domain by configuring and verifying DNS details. 1. Select the **Insight** from the list of Insights, select the newly added insight. Alt text 2. Click on **Add DNS Info** located in the top tab to open the Add DNS Information form. Alt text 3. Enter **Subdomain** and **Value** in the provided space. 4. Click on **Save** to apply changes. 5. Click on **View DNS Info** from the top tab to open and verify the DNS Information form. Alt text 6. Verify **Subdomain** and **Value**. 7. Click the **Fulfill** button to finalize the DNS setup and activate the Access Endpoint. Alt text Once fulfilled, the **Access Endpoint** for the Insight application will be activated ## How to access skyData Insights Applications? Follow the steps below to access Insights and view the required reports through two different methods: * You can directly access the Insights application using the provided access endpoint (URL). * You can access the Insights application through Studio under the Applications module. Simply click on the Insight access point (URL) to be redirected to the Insights application. 1. In the left pane, go to **Applications** > **Insights**,the Insights window appears. Alt text 2. Click on the **Access endpoint (URL)** provided to navigate to the Insight application. ## How to access reports in skyData Insight Applications? ### A. To Navigate and View Reports 1. In the left panel, you can see a list of available reports. 2. Click on the **report** you need to open. Alt text 3. You can switch to another report by clicking on the reports listed in the left panel. ### B. To Interact with Reports 1. You can interact with the report by applying filters based on your needs. Alt text 2. Use the available options to explore and analyze specific data within the report. ### C. To Refresh Reports 1. To view the latest data, locate the Refresh button on the report page. Alt text 2. Click the Refresh button to update the report with the most current information. ### D. To Export Reports 1. To export the report, click the Export button at the top-right corner of the report panel. A dropdown menu will appear Alt text 2. Select your preferred file format (e.g., Excel or PDF) to download the report. The export button is enabled only for workspaces with premium capacity ### E. To logout from Insight Portal 1. From the bottom in the left side panel, click on your profile icon. Alt text 2. Click on the **Log out** button to log off from the portal. # Source: https://docs.skypoint.ai/docs/instance # Instances ## Overview The Instances module enables you to create and manage multiple environments within your skyData tenant. Each instance operates as a distinct workspace for organizing data, configurations, and user access, providing isolation and flexibility for different use cases. ## Key Capabilities ### Instance Creation Create new instances to support different business units, projects, or environments. Choose to start fresh or copy settings from an existing instance for faster setup. ### Copy from Existing Replicate configurations from an existing instance to quickly set up new environments with consistent settings, reducing setup time and ensuring standardization. ### Instance Properties Configure key properties for each instance including: * **Name and Display Name** - Identify and label your instance * **Domain** - Associate the instance with your business domain * **Time Zone** - Set the appropriate time zone for operations * **Language** - Configure the preferred language * **Date and Number Format** - Match regional formatting preferences * **Currency** - Set the default currency for the instance ### Instance Management Perform administrative actions on existing instances: * **Edit** - Modify instance properties and configurations * **Remove** - Delete an instance and its associated data * **Reset Instance Data** - Clear specific data while preserving configurations * **Reset Privacy Data** - Remove privacy-related data * **History** - View the change history for the instance * **Backups** - Create backups or restore from previous backups ### Data Reset Options When resetting instance data, choose from multiple options: * Reset everything including all data and settings * Keep dataflows while removing other data * Preserve dataflows, stitch settings, and profile tables ## Business Benefits * **Environment Isolation** - Separate development, testing, and production environments * **Multi-Business Unit Support** - Create dedicated instances for different departments or regions * **Rapid Deployment** - Clone existing instances to accelerate new environment setup * **Data Protection** - Backup and restore capabilities for business continuity ## Who Benefits * **Administrators** - Manage and configure instance environments * **IT Teams** - Maintain separation between different operational environments * **Project Managers** - Create isolated spaces for specific projects * **Business Units** - Operate within dedicated instance environments # Welcome to Skypoint AI Source: https://docs.skypoint.ai/docs/intro Skypoint AI is a composable platform of 22 purpose-built AI applications that automate every operation across healthcare — sales, marketing, engagement, care, billing, compliance, staffing, and analytics. 250+ integrations. Not suggestions. Not dashboards. Work that gets done. Trusted by 1,100+ senior living communities and healthcare organizations nationwide. From 5-community operators to 100+ location portfolios. Start your journey with Skypoint AI by creating an account and connecting your data sources. Understand the fundamental concepts of Skypoint AI's platform and architecture. ## Proven Results Staff spend 60% of their time on tasks AI agents can handle — scheduling, documentation, billing reconciliation. Operators using Skypoint recover 5-10% in labor efficiency. skyAdvisor automates lead response via chat, SMS, and phone — 24/7, no missed inquiries. The operators winning are the ones converting and retaining faster. 100+ hours saved per community per month. Automated reporting, AI briefings, and back-office workflow execution that used to consume entire teams. Bickford Senior Living: \$1M net new revenue per year. Billing reconciliation that took 100+ people consolidated to one. 20x ROI. ## Key Strengths and Impact Engineers work directly with customers to integrate and operationalize AI inside existing workflows, accelerating adoption and ensuring sustained value. New capabilities ship every two weeks. HITRUST r2 and SOC 2 Type 2 certifications across infrastructure, data, and AI layers. Fully supports HIPAA compliance and long-term regulatory readiness. Healthcare organizations across every care setting use Skypoint to improve efficiency, financial performance, and growth. 1,100+ communities including Bickford Senior Living, Cascadia Senior Living, New Perspective, Juniper, Traditions, Schlegel Villages, and more. ## Composable Platform Architecture Skypoint's platform consists of three integrated layers of innovation: ### 1. Foundation - HITRUST r2 Managed Services Provider A healthcare-grade Managed Services Provider (MSP) cloud infrastructure that is HITRUST r2 certified and deployed across Microsoft Azure Cloud, Google Cloud, AWS, and private data centers. * Continuous compliance, cost optimization, and high availability * Automated security operations, encryption, patching, incident response, and auditing * Fully managed cloud environments with end-to-end monitoring and operational support ### 2. Healthcare Operations Ontology A unified data integration and healthcare ontology layer with semantic models powering analytics, AI agents, applications, enterprise data, and AI governance. **Supports Modern Data Stacks:** EPIC Caboodle, PostgreSQL, Databricks, Snowflake, Fabric, BigQuery, Power BI, and more.

skyData skyData
The integration engine behind Skypoint. 250+ bidirectional connectors to clinical, operational, and financial systems.
skyMDM skyMDM
AI matches, merges, and dedupes data across every system to deliver a connected, real-time 360° view. ### 3. AI Applications and Agents 22 purpose-built AI applications powered by skyAgent — the AI workforce layer embedded in every Skypoint application. Start with one app. Solve a real problem. Expand when ready. #### Platform Foundation
skyAgent skyAgent
The AI workforce layer across your entire operation. Executes tasks, triggers workflows, and does the admin work so your staff doesn't have to.
skyMSP skyMSP
Build custom AI agents and applications with your preferred tools. Deploy in Skypoint's secure, HITRUST r2-certified cloud. #### Growth & Engagement
skyAdvisor skyAdvisor
Converts web visitors into tour-ready leads via chat, SMS, phone, and email, 24/7. Built-in CRM included.
skyVoice skyVoice
Human-like AI that answers and places calls instantly — no hold times, no missed inquiries.
skyReputation skyReputation
AI-powered reputation management — review aggregation, survey automation, AI-generated responses, and search optimization.
skyMarketIQ skyMarketIQ
AI-powered market intelligence across 45,000+ communities, 7,000 operators, and 1B+ data points.
skyRFP skyRFP
AI-powered RFP response automation — ingests RFPs, retrieves approved content, and drafts proposals. Cuts response time from 15–20 hours to a fraction. #### Revenue Cycle
skyAuth skyAuth
End-to-end prior authorization lifecycle automation.
skyReferral skyReferral
Automated referral tracking and care coordination.
skyAdmissions skyAdmissions
Purpose-built SNF CRM for referral intake, admissions automation, and payor-ready documentation.
skySign skySign
Digital contracts, eSignature, and AI agreement management from initial inquiry to move-in. #### Operations
skyCare skyCare
AI-native care management embedded in EHR workflows — ADLs, meds, care plans, and family portal.
skySupport skySupport
AI-powered helpdesk for community staff — ticketing, service requests, and maintenance resolution.
skyComply skyComply
AI turns deficiency reports, EHR data, and policy violations into correction plans. State survey readiness automated.
skyPrivacy skyPrivacy
Self-service privacy portal with automated DSR processing for effortless compliance. #### Workforce
skyHire skyHire
Automated recruiting, applicant tracking, and onboarding — purpose-built for healthcare.
skyLearn skyLearn
AI-powered learning and training management. Accelerates onboarding and maintains regulatory compliance. #### Analytics & Intelligence
skyCommand skyCommand
Unified operations command center with 350+ KPIs — occupancy, NOI, move-in velocity, length of stay, labor as a percentage of revenue.
skyInsights skyInsights
Real-time clinical, financial, and operational analytics integrated into skyCommand and Microsoft Fabric (Power BI).
skyChat skyChat
Private, secure AI assistant that answers operational questions and creates content from your own data.
skyNote skyNote
Meeting Intelligence — live transcription with speaker attribution, in-meeting AI assistance, and deep search across every conversation. ## Healthcare Segments We Serve Automate operations, utilization management, compliance workflows, risk adjustment, and member engagement. Reduce front-office burden, automate revenue cycle processes, and improve provider productivity. Boost occupancy, increase NOI, reduce compliance risk and automate operations across every community. Improve census, reduce labor costs, and automate operational processes. Automate operations and compliance, and support better financial outcomes. Automate operations, quality reporting, and population health workflows. Reduce care management costs, accelerate gap closure, automate operations and improve performance. Automate operations, optimize Medicaid revenue, and improve clinical documentation. ## The Skypoint Advantage Purpose-built for the complexity, interoperability needs, and regulatory demands of healthcare and life sciences industry. HITRUST r2 and SOC 2 Type 2 certified, with full HIPAA compliance across our infrastructure, data platform, and AI systems. Embedded engineers ensure rapid integration, workflow optimization, and sustained outcomes. New capabilities ship every two weeks. 22 independently sold products across data, ontology, AI agents, applications, and 250+ integrations. Start with one. Expand when ready. *** ## Documentation Directory ### Getting Started * [Platform Overview](overview) - Understand the fundamental concepts of Skypoint AI's platform and architecture ### Platform Foundation * [skyAgent](skyagent) - AI execution engine inside every Skypoint application * [skyMSP](skymsp) - Build and deploy custom agentic applications in Skypoint's secure cloud * [skyData Overview](skydata) - Data integration across 250+ clinical, operational, and financial systems * [Dashboard](dashboard) - Platform overview and navigation * [Dataflow](dataflows) - Data import, export, and transformations * [Applications](copilots) - Copilots, Agents, and Insights * [Lakehouse](lakehouse) - Data lake and warehouse capabilities * [Settings](settings) - Platform configuration and administration * [Help + Support](help+support) - Access support resources * [Developer Portal](api) - API documentation and development tools * [skyMDM Overview](skymdm) - AI match, merge, dedupe for unified 360° records ### Growth & Engagement * [skyAdvisor](skyadvisor) - AI sales agent for lead conversion via chat, SMS, phone, and email * [skyVoice](skyvoice) - Human-like AI for inbound and outbound calls * [skyReputation](skyreputation) - AI-powered reputation and review management ### Revenue Cycle * [skyAuth](skyauth-provider) - Prior authorization automation * [skyReferral](skyreferral) - Referral management and optimization * [skyAdmissions](skyadmissions) - Automated referral intake and scoring * [skySign](skysign) - eSignature and AI agreement management ### Operations * [skyCare](skycare-seniorliving) - AI-native care management for clinical workflows * [skySupport](skysupport) - AI-powered enterprise helpdesk and ticketing * [skyComply](skycomply) - AI compliance and survey readiness automation * [skyPrivacy](skyprivacy) - Privacy portal and DSR automation ### Workforce * [skyHire](skyhire) - AI-powered recruiting and applicant tracking * [skyLearn](skylearn) - AI-powered learning management and certification ### Analytics & Intelligence * [skyCommand](skycommand) - Unified operations command center with 350+ KPIs * [skyInsights](skyinsights) - Real-time analytics with Power BI * [skyChat](skychat) - Private AI assistant for secure queries on your own data ### Reference * [Glossary](glossary) - Definitions of platform concepts and terminology # Source: https://docs.skypoint.ai/docs/isolved-importconnector # Connecting to iSolved iSolved is a cloud-based Human Capital Management (HCM) software that unites payroll, benefits, learning management, workforce management, talent acquisition, and core human resources activities. It simplifies management tasks and unifies data to reduce errors and increase productivity. You can connect the iSolved to skyData Studio and perform various tasks such as data ingestion, unification, transformation, and enrichment to gain insights from the data. You can use SkyPoint AI's built-in connector for importing data from iSolved. This document will guide you through the process of connecting iSolved to the skyData. ## Prerequisite You will need the following details to configure and import data using the iSolved⇗ connector: * Client Id * Legal Ids * Key * ApiSecret ## To import data using the iSolved connector Follow the below steps to create and configure a new dataflow for the iSolved import connector: 1. Go to **Dataflow > Imports**. 2. Click **New dataflow**. The **Set dataflow name** page appears. Alt image 3. Enter the dataflow name in the **Name** text area. 4. Click **Next**. The **Choose connector** page appears. Alt image ## Add iSolved connector 1. In the **Choose connector** page, select **iSolved** connector. You can use the **Search** feature to find the connector. Also, the iSolved connector can be found under the **Services and apps** category. Alt image 2. Enter the **Display Name** for your dataflow in the text area. 3. You can add a **Description** in the text area. 4. Click **Next**. The **Configuration** page appears. Alt image ## Connect to the iSolved account Log in to your iSolved account using your username and password. If you do not have an account, you can create one by visiting the iSolved⇗ website. 1. Enter **Client Id** the unique identifier assigned to your application. 2. Enter the **Legal Ids** to manage data associated with the legal entity, such as payroll, benefits, and other tax and legal-related information. 3. Enter the **Key** to identify and authenticate a user. 4. Enter the **ApiSecret** to authenticate a user or service when making requests to a web service. 5. Select the **Default Activity Start Date** to set the starting date for a new activity. 6. Click **Connect**. Once the connection is established, you can use the connector to import data from a table in iSolved. Alt image 7. In the **Table details** section, click the checkbox for the tables you wish to import, then use the dropdown to mark them as either **Data** or **Metadata**.
In the **Table Details section**, all tables are selected by default. You can select the tables you want to import and process. For example, to import customer data, choose the tables that contain customer information, such as name, email, address, and contact details. | Item | Description | | :---------- | :------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data | | Metadata | Loads Metadata | | File name | Displays the name of the file that you imported. | | Entity name | Displays the imported table name. You can rename it later if needed. | 8. Click **Save** to apply the changes. After saving the connection, the iSolved connector appears on the **Dataflow** > **Imports** page. ## Run, edit, and delete the imported data Once the table is imported, you can execute, modify, and remove the imported table from the Dataflow. Follow the below steps: 1. Go to the **Dataflow** > **Imports** page. Alt image | Item | Description | | :---------------- | :------------------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables count | Displays the number of tables imported. | | Created Date | Displays date of creation. | | Last refresh type | Displays the refresh value: Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Group by | Option to view the items in a specific group. For example, type, status, tables count, etc. | 2. Select the horizontal ellipsis under the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run history**. |
You can view the error message related to data import failures from a data source in the Dataflow under **Run History** > **Description**. Additionally, you can check the status, start time, and end time for the execution of the data pipeline. ## Next step After completing the data import, start the [Master Data Management (MDM) - Resolve](https://docs.skypointcloud.com/docs/stitch.html) process to create a single, unified view of the data. With Skypoint MDM, you can ensure that your data is accurate, consistent, and reliable, making it easier to use for business processes, analytics, and reporting. # Source: https://docs.skypoint.ai/docs/klaviyo # Connecting to Klaviyo # Import data ## Overview  Klaviyo provides a marketing automation platform used primarily for email and SMS marketing. You can use skyData's built-in connector for importing data from Klaviyo. This document will guide you through the process of connecting Klaviyo to skyData. ## Prerequisite  You will need the following details to configure and import data using Klaviyo: * [Klaviyo Account](https://www.klaviyo.com/login) * API Key. To create the API key, log in to your Klaviyo account. For more information, refer to the Setting up Klaviyo document. ## Import Data using Klaviyo connector  1. In the left pane, go to **Dataflow** > **Imports**.  The Dataflow window appears. Alt text 2. Click **New Dataflow** to create dataflow. Alt text 3. Enter a **Name** to identify your Dataflow.  The name must start with a letter and can only contain letters and numbers, with no spaces between the characters. 4. Click **Next**.  ## Add Klaviyo Connector  Follow the below steps to add Klaviyo connector: Alt text 1. In the **All** or **Marketing** categories tab, select the **Klaviyo** connector to start the configuration. 2. Also, you can use the **Search** function to find the **Klaviyo** connector. Alt text 3. Enter the **Display Name** to identify your dataflow.  4. In the **Description** box, you can enter an explanation of the connector.   5. Click **Next**. Alt text ## To configure Klaviyo 1. Enter **API Key** to configure with the Klaviyo connector. 2. Click **Connect**. Alt text 3. You can enter the **Table Details** to process the data.  In the **Table Details** column, all fields are selected by default. You can select only those fields that you want to import and process the data. | Item  | Description  | | :------------------------ | :----------------------------------------------------------- | | Purpose  | Option to assign a purpose (Data, Metadata) for each table.  | |
Data
| Loads customer data.  | |
Metadata
| Loads Metadata.  | | Name | Displays the name of the file that you imported.  | | Type | Displays the imported data type. | 4. Click **Save** to apply the changes.  ## Run, edit, and delete the imported data  After saving the connection, the Klaviyo connector appears on the Dataflow page. Also, you can see the list of created entities in the [Databases](https://skypointcdpdocs.z22.web.core.windows.net/docs/entities.html) section. Alt text | Item  | Description  | | :------------ | :------------------------------------------------------------------------------- | | Name  | Displays the name of the Dataflow.  | | Type  | Displays connector type symbol.  | | Status  | Indicates whether the data is imported successfully.  | | Tables Count  | Displays the number of entities.  | | Created Date  | Displays date of creation.  | | Updated Date  | Displays last modified date.  | | Last Refresh  | Displays the latest refresh date, which updates each time you refresh the data. | | Group by  | Option to view the items in a specific Group (For example, name, type, status).  | 1. Click on the connector on the Imports page that you want to modify. 2. Select the horizontal ellipsis in the **Actions** column and do the following:  | If you want to  | Then  | | :------------------------------------ | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow  | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes.  | | Execute the Dataflow  | Select **Run**.  | | Bring the data to its previous state  | Select **Rollback**.  | | Delete the Dataflow  | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow  | Select **Run History**.  |
You can view the error message for data import failures in the Dataflow under **Run History** > **Description**. # Setup guide ## Overview This document will help you gather all credentials for connecting Klaviyo with skyData. ## Prerequisite You should have access to an active [Klaviyo](https://www.klaviyo.com/login?next=/account) account. To manage the API keys, you must have an Owner, Admin, or Manager role on the account. ## To create an API key Follow the below steps to create a Private API key: 1. Log in to your **Klaviyo** account. 2. Click **Account > Settings**. 3. Select **API Keys** tab. Alt text 4. Click **Create Private API Key**. 5. Name the API key. 6. Choose the **Scope** you want to give the API key. Alt text | Scope | Description | | :-------------- | :------------------------------------------------------------------------------- | | Read-only Key | Limits third party access to read-only for every API scope. | | Full Access Key | Allows third parties to create, delete, or make changes for every API scope. | | Custom Key | Allows you to decide how much access for each API scope to give the third party. | 7. Click **Create**. You can select the scope to control third-party access to information when sharing the private API key.
## Next step  After completing the data import, start the [Master Data Management (MDM) - Stitch process](stitch.md) to develop a unified view of your customers. # ERD BOULEVARD ERD # Source: https://docs.skypoint.ai/docs/klaviyoexport # Klaviyo ## Steps to Export Export lets you extract data from the Lakehouse and load into Klaviyo. Alt text ## Step 01 - Add export * Click on **Set up** in Klaviyo export gallery. * Enter a Name. (Name starts with a letter and only **Letters** and **Numbers** are allowed. No spaces) * Display name (which displays the name in the UI of the export saved). * Select credentials: * Select from existing * Create new ## Step 02 - Select from existing **In order to allow skyData access to your Klaviyo account, you'll need the following details:** * **Select Dataflow** * **API key** * Click **Next** Button. ## Step 02 - Create new **In order to allow skyData access to your klaviyo account, you'll need the following details:** * **API Key** * Click **Next** Button. ## Step 03 - Entities to export Here all the Lakehouse data will be available Entity wise. * Select the entities from the list to be exported * Click **Create** and then click on three dot button and select **Run**. * The export will be started. ## To check export history You can check the success, failures, and total number of records in the Export history. Follow the below steps to check the export history: 1. Go to **Dataflow > Exports**. The **Export gallery** page appears. *** Alt text *** 2. Click the **Destinations** tab. 3. Click the horizontal ellipsis in the **Actions** column of your Klaviyo export. The **Actions** list appears. *** Alt text *** 4. Click **History** on the **Actions** list. The **Export History** pop-up appears. *** Alt text *** # Source: https://docs.skypoint.ai/docs/kwi # Connecting to the KWI connector ## Overview KWI platform supports Point of Sale (POS), Merchandise/Inventory management, Order management, Customer Relationship Management (CRM), and eCommerce. You can use skyData's built-in connector for importing data from KWI. ## Prerequisite * You have a [KWI account](https://kwicloud9docs.com/login/?redirect_to=https%3A%2F%2Fkwicloud9docs.com%2F) and required credentials such as username, password, and domain URL. ## Import Data using KWI Connector Follow the below steps to create a new dataflow for the KWI import connector: 1. In the left pane, go to **Dataflow** > **Imports**. The Dataflow window appears. Alt text 2. Click **New Dataflow** to create dataflow. Alt text 3. Enter a **Name** to identify your Dataflow. The name must start with a letter and can only contain letters and numbers, with no spaces between the characters. 4. Click **Next**. ## Add KWI connector 1. In the **All** or **Sales** tab, select the **KWI** connector to start the configuration. Alt text 2. Also, you can use the **Search** function to find the **KWI** connector. Alt text 3. In the **Set dataflow name** page, enter the **Display Name** to identify your dataflow. 4. In the **Description** box, you can enter an explanation of the connector. 5. Click **Next**. Alt text ## To configure KWI 1. Enter your credentials such as **Username**, **Password**, and **Base** **URL** to configure with the KWI connector. For details about each API, click [KWI Commerce API Details](https://kwicloud9docs.com/kwi-api-details/). Also, if you want to know how to connect with API, click [KWI Commerce API Try Out Functionality](https://kwicloud9docs.com/try-out-api-functionality/). 2. Click **Connect**. Alt text 3. Enter the **Table Details** to process the data. | Item | Description | | :------------------------ | :------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | |
Data
| Loads customer data | |
Metadata
| Loads Metadata | | Name | Displays the name of the file that you imported. | | Table Name | Displays the imported table name. You can rename it later if needed. | 4. Click **Save** to apply the changes. ## Run, edit, and delete the imported data 1. After saving the connection, the KWI connector appears on the Dataflow page. Also, you can see the list of created tables in the [Databases](https://skypointcdpdocs.z22.web.core.windows.net/docs/entities.html) section. Alt text | Item | Description | | :----------- | :------------------------------------------------------------------------------ | | Name | Displays the name of the Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables. | | Created Date | Displays date of creation. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Group by | Option to view the items in a specific Group (For example, name, type, status). | 2. Select the horizontal ellipsis in the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run History**. |
You can find the error message related to data import failures in the Dataflow under **Run History** **Description**. ## Next step After completing the data import, start the [Master Data Management (MDM) - Stitch process](stitch.md) to develop a unified view of your customers. # Source: https://docs.skypoint.ai/docs/lakehouse # Lakehouse ## Overview Lakehouse is an integrated form of data warehouse and data lake using Delta Lake technology. This combination of two unique tools brings the best of both techniques to users. Alt text It takes the unstructured data from a data lake and utilizes the management features and tools from data warehouses to implement them together as a larger system. You can simplify and scale your enterprise data strategy while supporting Business Intelligence (BI) and Artificial Intelligence (AI) use cases in one place utilizing Databricks as the primary computing engine. Advantages of the Lakehouse are as follows: * Flexible and low-cost storage. * Supports advanced analytics workloads (Artificial Intelligence (AI), Business Intelligence (BI)) and predictive analytics to take advantage of the full set of data. * Improve data governance. * Manage any type of data (structured or unstructured). skyData's analytical store consists of [Databases](entities.md) and [Ontology](ontology.md). # Source: https://docs.skypoint.ai/docs/mrinexcore # Connecting to MRINexcore This guide provides a step-by-step approach to effortlessly connecting MRINexcore with skyData. MRINexCore is a platform by MRI Software designed for managing real estate operations, including lease administration, accounting, and property management. It provides flexible tools for handling complex portfolios and supports integration with other systems to streamline workflows and enhance data-driven decision-making. The MRINexCore connector in skyData enables seamless integration with MRI Software’s NexCore platform, commonly used in property and real estate management. It allows organizations to efficiently ingest and sync data from NexCore using secure methods like APIs. With support for service account credentials, automated scheduling, and data transformation, the connector streamlines data integration into Skypoint’s unified models for enhanced analytics and AI applications. ## Prerequisite You need the following details to configure and import data using the MRINexcore⇗ connector: * Username * Display name * Password * Default Activity Start Date ## To import data using the MRINexcore connector Follow the below steps to create and configure a new dataflow for the MRINexcore import connector: 1. Navigate to **Dataflow > Imports**. Alt image 2. Click **New dataflow** as indicated by an arrow. The **Set dataflow name** page appears. Alt image 3. Enter the desired name for the dataflow in the **Name** text field. 4. Click **Next** to proceed. The **Choose connector** page appears. Alt image ## Add MRINexcore connector 1. On the **Choose Connector** page, use the **Search** feature to locate and select the **MRINexcore** Connector. 2. Enter the **Display Name** for your dataflow in the provided text field. 3. Optionally, add a **Description** in the designated text area. Alt image 4. Click **Next** to proceed. The **Configuration** page appears. Alt image ## Connect to the MRINexcore account 1. Fill in the required details on the Configuration page. 2. Click **Connect**. Alt image Once the connection is established, the connector can be used to import data from MRINexcore tables. 3. Scroll down to the **Table Details** section, select the checkboxes for the tables you wish to import, and use the dropdown menu to label them as either **Data** or **Metadata.** Alt image By default, all tables in the **Table Details** section are selected. You can choose to import only specific tables that are relevant to your data processing needs. For example, to import customer data, select tables containing details like name, email, address, and contact information. | Item | Description | | :---------- | :-------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data | | Metadata | Loads Metadata | | File name | Displays the name of the file that you imported. | | Entity name | Displays the imported table name by default. You can rename it if required. | 4. Click **Save** to apply the changes. Alt image Congratulations ! for saving the MRINexcore connector dataflow, which appears on the **Dataflow > Imports** page. ## Run, Edit, and Delete the imported data Once the table is imported, you can execute, modify, and remove the imported table from the Dataflow. Follow the below steps: 1. Go to the **Dataflow** > **Imports** page. Alt image | Item | Description | | :---------------- | :------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Connector Name | Displays connector name. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables imported. | | Created Date | Displays date of creation. | | Last Refresh Type | Displays the refresh value: Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Actions | Provides multiple options for managing dataflows. | 2. Select the horizontal ellipsis under the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the dataflow | Select **Run history**. | 3. Click **Run** to execute the dataflow. Once the execution is successful, the data pipeline status will update to **Completed**, as illustrated in the figure below. Alt image In the Dataflow's **Run History Description,** you can view error messages related to data import failures from a data source. Additionally, you can check the status, start time, and end time of the data pipeline execution. ## Next step After completing the data import, start the [Master Data Management (MDM) - Resolve](https://docs.skypointcloud.com/docs/stitch.html) process to create a single, unified view of the data. With Skypoint MDM, you can ensure that your data is accurate, consistent, and reliable, making it easier to use for business processes, analytics, and reporting. # Source: https://docs.skypoint.ai/docs/mysql # Connecting to MySQL MySQL is a reliable, high-performance open-source database with cross-platform support for seamless deployment across operating systems. It offers robust security with SSL and access control, while its compatibility with multiple programming languages enhances its versatility for application development. MySQL seamlessly integrates with SkyPoint AI Platform (AIP) via its inbuilt connector, enabling centralized data synchronization and management. This integration simplifies operations like managing data tables, enhances analytics and governance, breaks data silos, and ensures secure, efficient data handling for improved compliance and productivity. This document will guide you through the process of connecting My SQL to the skyData. ## Prerequisite You need the following details to configure and import data using the MySQL⇗ connector: * Server name * Database name * Port number * Username * Password ## To import data using the MySQL connector Follow the below steps to create and configure a new dataflow for the MySQL import connector: 1. Navigate to **Dataflow > Imports**. Alt image 2. Click **New dataflow** as indicated by an arrow. The **Set dataflow name** page appears. Alt image 3. Enter the desired name for the dataflow in the **Name** text field. 4. Click **Next** to proceed. The **Choose connector** page appears. Alt image ## Add MySQL connector 1. On the **Choose Connector** page, use the **Search** feature to locate and select the **MySQL** Connector. 2. Enter the **Display Name** for your dataflow in the provided text field. 3. Optionally, add a **Description** in the designated text area. Alt image 4. Click **Next** to proceed. The **Configuration** page appears. Alt image ## Connect to the MySQL account 1. Fill in the required details on the Configuration page. 2. Click **Connect**. Alt image Once the connection is established, the connector can be used to import data from MySQL tables. 3. Scroll down to the **Table Details** section, select the checkboxes for the tables you wish to import, and use the dropdown menu to label them as either **Data** or **Metadata.** Alt image By default, all tables in the **Table Details** section are selected. You can choose to import only specific tables that are relevant to your data processing needs. For example, to import customer data, select tables containing details like name, email, address, and contact information. | Item | Description | | :---------- | :-------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data | | Metadata | Loads Metadata | | File name | Displays the name of the file that you imported. | | Entity name | Displays the imported table name by default. You can rename it if required. | 4. Please scroll down to the bottom of the page to ensure all required tables are selected. Alt image 5. Click **Save** to apply the changes. Alt image Congratulations ! for saving the MySQL connector dataflow, which appears on the **Dataflow > Imports** page. ## Run, Edit, and Delete the imported data Once the table is imported, you can execute, modify, and remove the imported table from the Dataflow. Follow the below steps: 1. Go to the **Dataflow** > **Imports** page. Alt image | Item | Description | | :---------------- | :------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Connector Name | Displays connector name. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables imported. | | Created Date | Displays date of creation. | | Last Refresh Type | Displays the refresh value: Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Actions | Provides multiple options for managing dataflows. | 2. Select the horizontal ellipsis under the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the dataflow | Select **Run history**. | 3. Click **Run** to execute the dataflow. Once the execution is successful, the data pipeline status will update to **Completed**, as illustrated in the figure below. Alt image In the Dataflow's **Run History Description,** you can view error messages related to data import failures from a data source. Additionally, you can check the status, start time, and end time of the data pipeline execution. ## Next step After completing the data import, start the [Master Data Management (MDM) - Resolve](https://docs.skypointcloud.com/docs/stitch.html) process to create a single, unified view of the data. With Skypoint MDM, you can ensure that your data is accurate, consistent, and reliable, making it easier to use for business processes, analytics, and reporting. # Source: https://docs.skypoint.ai/docs/netsuite # Connecting to NetSuite This guide provides a step-by-step approach to effortlessly connecting NetSuite with skyData. The NetSuite Connector automates data exchange between NetSuite and platforms like e-commerce, 3PL, and POS systems. It streamlines order, inventory, and customer data syncs with customizable settings, secure authentication, and a user-friendly dashboard boosting efficiency and reducing manual work. The connector is designed to be flexible and scalable, making it ideal for businesses looking to optimize operations across multiple systems. The NetSuite Connector in skyData enables seamless integration with Oracle NetSuite, a cloud based ERP platform used for financial management, accounting, and operations. It allows organizations to ingest and sync financial and account-related data such as transactions, general ledger, and vendor records—using secure APIs. The connector supports service account credentials, automated scheduling, and data transformation to streamline integration into Skypoint’s unified data models, enhancing analytics capabilities and AI-driven financial insights ## Prerequisite You will need the following details to configure and import data using [NetSuite](https://docs.oracle.com/en/cloud/saas/netsuite/ns-online-help/chapter_3963845427.ht) * Account ID * Consumer key * Token ID * Consumer secret * Token secret * User ## To Import data using NetSuite connector Follow the below steps to create a new dataflow for the NetSuite import connector: 1. Go to **Dataflow > Imports**. Alt image 2. Click **New dataflow**. The **Set dataflow name** page appears. Alt image 3. Enter **Dataflow name** in the **Name** text area. 4. Click **Next**. The **Choose connector** page appears. Alt text ## Add NetSuite connector 1. On the **Choose Connector page**, use the Search feature to locate and select the **NetSuite** Connector. Alt image 2. Enter the **Display Name** for your dataflow in the text area. 3. You can add a **Description** in the text area. 4. Click **Next** to proceed. The **Configuration** page appears. Alt image ## Connect to the NetSuite account 1. Fill in the required details on the **Configuration page**. 2. Click **Connect**. Once the connection is established, you can use the connector to import data from a table in NetSuite. Alt image 3. Scroll down to the **Table Details** section, select the checkboxes for the tables you wish to import, and use the dropdown menu to label them as either **Data** or **Metadata**. Alt image In the **Table Details** section, by default, all tables are selected. You can mark only those tables that you want to import and process the data. For example, to import customer data, you can check those tables which contain customer information, such as name, email, address, and contact details. | Item | Description | | :---------- | :-------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data | | Metadata | Loads Metadata | | File name | Displays the name of the file that you imported. | | Entity name | Displays the imported table name by default. You can rename it if required. | 5. Click **Save** to apply the changes. Alt image After saving the connection, the NetSuite connector appears on the **Dataflow** > **Imports** page. ## Run, edit, and delete the imported data Once the table is imported, you can execute, modify, and remove the imported table from the Dataflow. Follow the below steps: 1. Go to the **Dataflow** > **Imports** page. Alt image | Item | Description | | :---------------- | :------------------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables count | Displays the number of tables imported. | | Created Date | Displays date of creation. | | Last refresh type | Displays the refresh value: Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Group by | Option to view the items in a specific group. For example, type, status, tables count, etc. | 2. Select the horizontal ellipsis under the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run history**. | Alt image You can see the error message corresponding to failure while importing data from a data source in the Dataflow under **Run History** > **Description**. Also, you can see the status, start time, and end time for the execution of the data pipeline. ## Next step After completing the data import, start the [Master Data Management (MDM) - Resolve](https://docs.skypointcloud.com/docs/stitch.html) process to create a single, unified view of the data. With SkyPoint MDM, you can ensure that your data is accurate, consistent, and reliable, making it easier to use for business processes, analytics, and reporting. # Platform Overview Source: https://docs.skypoint.ai/docs/overview The Skypoint AI Platform powers 22 purpose-built AI applications that automate every operation across healthcare — sales, marketing, engagement, care, billing, compliance, staffing, and analytics. 250+ integrations. Not suggestions. Not dashboards. Work that gets done. Trusted by 1,100+ senior living communities and healthcare organizations. From 5-community operators to 100+ location portfolios. **Certified for Healthcare:** Skypoint is HITRUST r2 certified, SOC 2 Type 2 certified, and HIPAA compliant, ensuring the highest standards of data security and privacy for healthcare organizations. HITRUST r2 Certified HIPAA Compliant ## Real Results from Real Operators Staff spend 60% of their time on tasks AI agents can handle — scheduling, documentation, billing reconciliation. Operators using Skypoint recover 5-10% in labor efficiency. With industry occupancy approaching 90%, the operators winning are the ones converting and retaining faster. skyAdvisor automates lead response via chat, SMS, and phone — 24/7, no missed inquiries. 100+ hours saved per community per month. Automated reporting, AI briefings, and back-office workflow execution that used to consume entire teams. Bickford Senior Living: \$1M net new revenue per year. Billing reconciliation that took 100+ people consolidated to one. 20x ROI. ## Who We Work With Multi-specialty and primary care networks Independent living, assisted living, and memory care FQHCs and community health centers SNFs and post-acute care facilities Home health and hospice organizations Hospitals and integrated delivery networks Payors and managed care organizations ACOs and risk-bearing entities ## How It Works Skypoint provides the full stack needed to unify your data, build and orchestrate agentic AI, and deploy purpose-built applications for real-world workflows: Connect every clinical, operational, and financial system into a single, trusted foundation via skyData's 250+ bidirectional connectors. Create complete 360° profiles for patients, providers, and organizations with skyMDM's AI-driven match, merge, and dedupe. Start with one of 22 purpose-built AI applications. Solve a real problem. Expand when ready. skyAgent — the AI workforce layer embedded in every application — executes tasks, triggers workflows, and does the admin work so your staff doesn't have to. skyCommand delivers 350+ KPIs with predictive insights, automated alerts, and AI-driven actions across your entire portfolio. ## Unified Data & Governance Layer At the foundation of the Skypoint Platform is a secure, scalable data lakehouse that consolidates structured and unstructured data across EHRs, claims, CRMs, operational systems, and external sources. Power unified analytical and transactional workloads on a single, scalable foundation. Seamlessly integrate and transform data across 250+ source systems. Link and match identities with AI to create accurate, connected records. Protect sensitive data with robust access controls, encryption, and auditability. Enforce schema governance, ensure clean data, and maintain full lineage. This layer ensures your agentic applications always operate on trusted, compliant, and high-fidelity data. ## AI Applications & Orchestration ### Unify Data and Govern AI Applications skyData is the integration engine behind Skypoint — 250+ bidirectional connectors to the systems you already run. skyMDM creates unified 360° records. Together, they power the data foundation for every AI application on the platform. Reasoning, retrieval, tools, and policies Memory and context injection capabilities Testing environments and version control ## 22 Purpose-Built AI Applications skyAgent executes the real work — not suggestions. Embedded in every application across sales, marketing, engagement, operations, workforce, finance, analytics, care, and compliance. Start with one app. Solve a real problem. Expand when ready. ### Platform Foundation
skyAgent skyAgent
The AI workforce layer across your entire operation. Executes tasks, triggers workflows, and does the admin work so your staff doesn't have to.
skyMSP skyMSP
Build custom AI agents and applications with your preferred tools. Deploy in Skypoint's secure, HITRUST r2-certified cloud.
skyData skyData
The integration engine behind Skypoint. 250+ bidirectional connectors to clinical, operational, and financial systems.
skyMDM skyMDM
AI matches, merges, and dedupes data across every system to deliver a connected, real-time 360° view. ### Growth & Engagement
skyAdvisor skyAdvisor
Converts web visitors into tour-ready leads via chat, SMS, phone, and email, 24/7. Tracks every lead from first touch to move-in. Built-in CRM included.
skyVoice skyVoice
Human-like AI that answers and places calls instantly — no hold times, no missed inquiries. Integrated with skyAdvisor to capture every lead.
skyReputation skyReputation
AI-powered reputation management — review aggregation, survey automation, AI-generated responses, and search optimization.
skyMarketIQ skyMarketIQ
AI-powered market intelligence across 45,000+ communities, 7,000 operators, and 1B+ data points. Comp set analysis, pricing benchmarks, and demand forecasting.
skyRFP skyRFP
AI-powered RFP response automation — ingests RFPs, retrieves approved content, and drafts high-quality proposals. Cuts response time from 15–20 hours to a fraction, with built-in compliance guardrails. ### Revenue Cycle
skyAuth skyAuth
End-to-end prior authorization lifecycle automation, reducing manual workload and accelerating patient access.
skyReferral skyReferral
Automated referral tracking and care coordination — reduces leakage by routing referrals to the right provider or facility.
skyAdmissions skyAdmissions
Purpose-built SNF CRM for referral intake, admissions automation, and payor-ready documentation.
skySign skySign
Digital contracts, eSignature, and AI agreement management from initial inquiry to move-in. ### Operations
skyCare skyCare
AI-native care management embedded in EHR workflows — ADLs, meds, care plans, and family portal.
skySupport skySupport
AI-powered helpdesk for community staff — ticketing, service requests, and maintenance resolution in one place.
skyComply skyComply
AI turns deficiency reports, EHR data, and policy violations into correction plans and tasks. State survey readiness automated.
skyPrivacy skyPrivacy
Self-service privacy portal with automated DSR processing for effortless compliance. ### Workforce
skyHire skyHire
Automated recruiting, applicant tracking, and onboarding — purpose-built for healthcare. Compresses time-to-hire and reduces recruiting overhead.
skyLearn skyLearn
AI-powered learning and training management. Accelerates onboarding, maintains regulatory compliance, and reduces turnover costs. ### Analytics & Intelligence
skyCommand skyCommand
Unified operations command center with 350+ KPIs — occupancy, NOI, move-in velocity, length of stay, labor as a percentage of revenue. Historical, predictive, and prescriptive analytics.
skyInsights skyInsights
Real-time clinical, financial, and operational analytics integrated into skyCommand and Microsoft Fabric (Power BI). The reporting layer your CFO and VP of Operations actually want.
skyChat skyChat
Private, secure AI assistant that answers operational questions and creates content from your own data — without exposing resident data outside your environment.
skyNote skyNote
Meeting Intelligence — live transcription with speaker attribution, in-meeting AI assistance, deep search across every conversation, and granular sharing controls. ## Integrate 250+ Systems Your systems. Your data. One connected layer. Connect to EHRs, payors, and other applications seamlessly: ## Forward Deployed Engineering You've been burned by software vendors who sell and disappear. Skypoint's FDE model embeds our engineers alongside your operations team — not in a support queue. They know your workflows, your regulatory landscape, and your operational reality. New capabilities ship every two weeks. FDEs become an extension of your team, delivering new AI capabilities in weeks, not months. Every two weeks, you get product updates tuned to your workflows — from care coordination to billing automation. Tailored implementations that fit your unique operational needs — not generic software you have to work around. # Source: https://docs.skypoint.ai/docs/paychex # Paychex ## Overview The Paychex connector integrates Paychex's payroll and HR platform with skyData, bringing employee and workforce data into your unified data environment. The connector supports PrismHR and Oasis HCM, broadening payroll and workforce data coverage across the Paychex ecosystem. Paychex is a leading provider of integrated human capital management solutions for payroll, benefits, human resources, and insurance services. By connecting Paychex to skyData, organizations gain a unified view of their workforce alongside clinical, operational, and financial data. ## What Data We Extract The Paychex connector brings in key workforce and payroll data including: * **Employees** — Employee demographics, job details, and employment status * **Earnings** — Compensation records, pay rates, and earning categories * **Deductions** — Benefit deductions, tax withholdings, and payroll adjustments * **Benefits** — Health, retirement, and other benefit enrollments * **Time and Attendance** — Hours worked and attendance tracking ## Why It Matters Workforce costs are often the largest expense for healthcare and senior living organizations. Connecting Paychex to skyData enables organizations to: * Analyze labor costs alongside census, acuity, and revenue data * Identify staffing patterns and optimize scheduling based on actual hours worked * Correlate workforce metrics with quality outcomes and resident satisfaction * Build a complete picture of per-patient or per-resident cost of care ## Who Benefits | Role | Value | | :----------------- | :---------------------------------------------------------------------- | | HR Directors | Unified workforce analytics combining payroll with operational data | | CFOs | Labor cost analysis tied to census, revenue, and quality metrics | | Operations Leaders | Staffing optimization through integrated workforce and operational data | ## Get Started Contact your Skypoint representative to configure the Paychex connector for your organization. # Source: https://docs.skypoint.ai/docs/paylocity # Paylocity ## Overview The Paylocity connector integrates Paylocity's payroll and HR platform with skyData, bringing workforce and compensation data into your unified data environment. Paylocity is a cloud-based payroll and human capital management (HCM) platform that helps organizations manage employee records, payroll processing, benefits, and time tracking. By connecting Paylocity to skyData, organizations gain a comprehensive view of their workforce alongside clinical, operational, and financial data. ## What Data We Extract The Paylocity connector brings in key workforce and payroll data including: * **Employees** — Employee demographics, job details, and employment status * **Earnings** — Compensation records, pay rates, and earning categories * **Deductions** — Benefit deductions, tax withholdings, and other payroll adjustments * **Time Punch Records** — Clock-in/out data, hours worked, and attendance tracking ## Why It Matters Workforce costs are often the largest expense for healthcare and senior living organizations. Connecting Paylocity to skyData enables organizations to: * Analyze labor costs alongside census, acuity, and revenue data * Identify staffing patterns and optimize scheduling based on actual hours worked * Correlate workforce metrics with quality outcomes and resident satisfaction * Build a complete picture of per-patient or per-resident cost of care ## Who Benefits | Role | Value | | :----------------- | :---------------------------------------------------------------------- | | HR Directors | Unified workforce analytics combining payroll with operational data | | CFOs | Labor cost analysis tied to census, revenue, and quality metrics | | Operations Leaders | Staffing optimization through integrated workforce and operational data | ## Get Started Contact your Skypoint representative to configure the Paylocity connector for your organization. # Source: https://docs.skypoint.ai/docs/paynw # Connecting to PayNW Learn how to seamlessly integrate PayNW with skyData, covering setup, configurations, and important factors. PayNW provides cloud-based Human Capital Management (HCM) solutions for mid-sized and growing businesses, streamlining payroll, HR, time tracking, benefits administration, and talent acquisition. Key features include accurate payroll processing, automated compliance, self-service tools for employees, and scalable integrations. Businesses benefit from reduced administrative burdens, improved efficiency, and regulatory compliance, enabling them to focus on strategic growth. The skyData Platform (AIP) seamlessly integrates with PayNW via an inbuilt connector, enabling real-time data synchronization for payroll, HR, time tracking, and benefits while enhancing automation, providing AI-driven insights, ensuring compliance, and offering self-service tools,all within a scalable, future-ready solution designed to minimize manual effort and optimize decision-making. ## Prerequisite You need the following details to configure and import data using the PayNW⇗ connector: * Server name * Database name * Port number * Username * Password ## To import data using the PayNW connector Follow the below steps to create and configure a new dataflow for the PayNW import connector: 1. Navigate to **Dataflow > Imports**. Set paynw dataflow on skypoint ai 2. Click **New dataflow** as indicated by an arrow. The **Set dataflow name** page appears. Key in dataflow name for paynw 3. Enter the desired name for the dataflow in the **Name** text field. 4. Click **Next** to proceed. The **Choose connector** page appears. Search to select paynw connector ## Add PayNW Server connector 1. On the **Choose Connector** page, use the **Search** feature to locate and select the **PayNW** Connector. 2. Enter the **Display Name** for your dataflow in the provided text field. 3. Optionally, add a **Description** in the designated text area. Key display name for paynw connector dataflow 4. Click **Next** to proceed. The **Configuration** page appears. Configure paynw connector on Skypoint ai ## Connect to the PayNW account 1. Fill in the required details on the Configuration page. 2. Click **Connect**. Connect paynw account on skypoint ai Once the connection is established, the connector can be used to import data from PayNW tables. 3. Scroll down to the **Table Details** section, select the checkboxes for the tables you wish to import, and use the dropdown menu to label them as either **Data** or **Metadata.** Select tables to import paynw data into skypoint ai By default, all tables in the **Table Details** section are selected. You can choose to import only specific tables that are relevant to your data processing needs. For example, to import customer data, select tables containing details like name, email, address, and contact information. | Item | Description | | :---------- | :-------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data | | Metadata | Loads Metadata | | File name | Displays the name of the file that you imported. | | Entity name | Displays the imported table name by default. You can rename it if required. | 4. Ensure all required tables are selected. 5. Click **Save** to apply the changes. Save paynw dataflow on skypoint ai Congratulations! for saving the PayNW connector dataflow, which appears on the **Dataflow > Imports** page. ## Run, Edit, and Delete the imported data Once the table is imported, you can execute, modify, and remove the imported table from the Dataflow. Follow the below steps: 1. Go to the **Dataflow** > **Imports** page. Modify paynw dataflow on skypoint ai studio | Item | Description | | :---------------- | :------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Connector Name | Displays connector name. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables imported. | | Created Date | Displays date of creation. | | Last Refresh Type | Displays the refresh value: Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Actions | Provides multiple options for managing dataflows. | 2. Select the horizontal ellipsis under the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the dataflow | Select **Run history**. | 3. Click **Run** to execute the dataflow. Once the execution is successful, the data pipeline status will update to **Completed**, as illustrated in the figure below. Data import complete from paynw on skypoint ai In the Dataflow's **Run History Description,** you can view error messages related to data import failures from a data source. Additionally, you can check the status, start time, and end time of the data pipeline execution. ## Next step After completing the data import, start the [Master Data Management (MDM) - Resolve](https://docs.skypointcloud.com/docs/stitch.html) process to create a single, unified view of the data. With Skypoint MDM, you can ensure that your data is accurate, consistent, and reliable, making it easier to use for business processes, analytics, and reporting. # Source: https://docs.skypoint.ai/docs/paynwreports # Connecting to PayNW Reports PayNW's reporting capabilities offer comprehensive dashboards, analytics, and customizable reports that provide real-time insights into payroll, HR, and employee data. You can leverage the built-in PayNW Reports connector in SkyPoint AI Studio to seamlessly import employee timesheet data & more. This streamlines data integration, enhances efficiency, and ensures smooth data management on the platform by enabling organizations to make informed decisions. This document will guide you through the process of connecting PayNW Reports to the skyData. ## Prerequisite You need the following details to configure and import data using the PayNWReports⇗ connector: * Company * Company ID * Username * Password * API Key * Report ID * Client Company ## To import data using the PayNW Reports connector Follow the below steps to create and configure a new dataflow for the PayNW Reports import connector: 1. Navigate to **Dataflow > Imports**. Alt image 2. Click **New dataflow** as indicated by an arrow. The **Set dataflow name** page appears. Alt image 3. Enter the desired name for the dataflow in the **Name** text field. 4. Click **Next** to proceed. The **Choose connector** page appears. Alt image ## Add PayNW Reports connector 1. On the **Choose Connector** page, use the **Search** feature to locate and select the **PayNW Reports** Connector. 2. Enter the **Display Name** for your dataflow in the provided text field. 3. Optionally, add a **Description** in the designated text area. Alt image 4. Click **Next** to proceed. The **Configuration** page appears. Alt image ## Connect to the PayNW Reports account 1. Fill in the required details on the Configuration page. 2. Click **Connect**. Alt image Once the connection is established, the connector can be used to import data from PayNW Reports. 3. Scroll down to the **Table Details** section, select the checkboxes for the tables you wish to import, and use the dropdown menu to label them as either **Data** or **Metadata.** Alt image By default, all tables in the **Table Details** section are selected. You can choose to import only specific tables that are relevant to your data processing needs. For example, to import employee data, select tables containing details like name, email, address, and contact information. | Item | Description | | :---------- | :-------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data | | Metadata | Loads Metadata | | File name | Displays the name of the file that you imported. | | Entity name | Displays the imported table name by default. You can rename it if required. | 4. Click **Save** to apply the changes. Alt image Congratulations ! for saving the PayNW Reports connector dataflow, which appears on the **Dataflow > Imports** page. ## Run, Edit, and Delete the imported data Once the table is imported, you can execute, modify, and remove the imported table from the Dataflow. Follow the below steps: 1. Go to the **Dataflow** > **Imports** page. Alt image | Item | Description | | :---------------- | :------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Connector Name | Displays connector name. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables imported. | | Created Date | Displays date of creation. | | Last Refresh Type | Displays the refresh value: Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Actions | Provides multiple options for managing dataflows. | 2. Select the horizontal ellipsis under the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the dataflow | Select **Run history**. | 3. Click **Run** to execute the dataflow. Once the execution is successful, the data pipeline status will update to **Completed**, as illustrated in the figure below. Alt image In the Dataflow's **Run History Description,** you can view error messages related to data import failures from a data source. Additionally, you can check the status, start time, and end time of the data pipeline execution. ## Next step After completing the data import, start the [Master Data Management (MDM) - Resolve](https://docs.skypointcloud.com/docs/stitch.html) process to create a single, unified view of the data. With Skypoint MDM, you can ensure that your data is accurate, consistent, and reliable, making it easier to use for business processes, analytics, and reporting. # Source: https://docs.skypoint.ai/docs/pcc # Connecting to PointClickCare ## Overview PointClickCare (PCC) connector facilitates the secure transfer of patient information from the Electronic Health Record (EHR) system, ensuring patient data privacy and compliance with regulations. This enables healthcare providers to access and share important patient information across different systems, improving the quality of care and enhancing collaboration among healthcare teams. You can connect the PCC to skyData Studio and perform various tasks such as data ingestion, unification, transformation, and enrichment to gain insights from the data. You can use skyData's built-in connector for importing data from PCC. This document will guide you through the process of connecting PCC to the skyData. ## Prerequisite You will need the following details to configure and import data using the PCC⇗ connector: * Client Id * Client Secret * Application Name * Organization Uuid * Certificate * Certificate Password (optional) ## To import data using the PCC connector Follow the below steps to create and configure a new dataflow for the PCC import connector: 1. Go to **Dataflow > Imports**. 2. Click **New dataflow**. The **Set dataflow name** page appears. Alt image 3. Enter the dataflow name in the **Name** text area. 4. Click **Next**. The **Choose connector** page appears. Alt image ## Add PCC connector 1. In the **Choose connector** page, select **PointClickCare** connector. You can use the **Search** feature to find the connector. Also, the PCC connector can be found under the **Healthcare** category. Alt image 2. Enter the **Display Name** for your dataflow in the text area. 3. You can add a **Description** in the text area. 4. Click **Next**. The **Configuration** page appears. Alt image ## Connect to the PCC account Log in to your PCC account using your username and password. If you do not have an account, you can create one by visiting the PCC website⇗. Go to the PCC developer portal⇗ and register your application to use API. When an application is registered, you will get the credentials to establish configuration. 1. Enter **Client Id** the unique identifier assigned to your application. 2. Enter the **Client Secret** code assigned to your application. 3. Enter the **Application Name** assigned to your application. 4. Enter the **Organization Uuid** assigned to your organization within the PCC system. 5. Upload the **Certificate** used in the SSL/TLS protocol to encrypt the communication between the client and server and establish a secure connection. 6. If necessary, enter the **Certificate Password** associated with the client certificate. 7. Click **Connect**. Once the connection is established, you can use the connector to import data from a table in PCC. Alt image 8. In the **Table details** section, each table is identified by a row. Click the checkbox to select the table for import and use the dropdown to mark it as **Data** or **Metadata**. In the **Table Details** section, all tables are selected by default. You can select only the tables you want to import and process. For example, to import customer data, choose the tables that contain customer information, such as name, email, address, and contact details. | Item | Description | | :------------------------ | :--------------------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | |
Data
| Loads customer data | |
Metadata
| Loads Metadata | | File name | Displays the name of the file that you imported. | | Entity name | Displays the imported table name by default. But you have the option to rename it later. | 9. Click **Save** to apply the changes. After saving the connection, the PCC connector appears on the **Dataflow** > **Imports** page. ## Run, edit, and delete the imported data Once the table is imported, you can execute, modify, and remove the imported table from the Dataflow. Follow the below steps: 1. Go to the **Dataflow** > **Imports page**. Alt image | Item | Description | | :---------------- | :------------------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables count | Displays the number of tables imported. | | Created Date | Displays date of creation. | | Last refresh type | Displays the refresh value: Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Group by | Option to view the items in a specific group. For example, type, status, tables count, etc. | 2. Select the horizontal ellipsis under the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run history**. | You can view the error message for data import failures in the Dataflow under **Run History** > **Description**. Additionally, you can see the status, start time, and end time of the data pipeline execution. ## Next step After completing the data import, start the [Master Data Management (MDM) - Resolve](stitch.md) process to create a single, unified view of the data. With Skypoint MDM, you can ensure that your data is accurate, consistent, and reliable, making it easier to use for business processes, analytics, and reporting. # Source: https://docs.skypoint.ai/docs/platform # Platform Settings ## Overview The Platform Settings module provides comprehensive tools for monitoring, managing, and configuring your skyData environment. It consolidates essential administrative functions into a unified interface with five key areas: Status, Logs, Session, Users, and APIs. ## Status Track the progress and completion status of all platform operations including data imports, exports, and background processes. The Status view provides real-time visibility into task execution, helping ensure completeness of your active workflows. ### Key Features * Monitor task progress and completion status * View execution history and timestamps * Refresh all tasks and processes on demand * Access detailed progress information for individual tasks * Track status types: New, In-Progress, Completed, Failed, and Canceled ## Logs The Logs section provides a comprehensive audit trail of all actions performed on the platform. Monitor user activities, track system events, and maintain visibility into platform operations for compliance and troubleshooting purposes. ### Key Features * View activity streams with timestamps, categories, and descriptions * Track user actions including login, logout, and data operations * Access tenant-level activity logs * Filter logs by time period * Export activity data for compliance reporting ## Session Configure session management settings to balance security requirements with user convenience. Control how long users can remain authenticated and set inactivity timeout thresholds to protect sensitive data. ### Key Features * Set maximum session duration * Configure inactivity timeout periods * Enable automatic session warnings before timeout * Customize timeout notification settings ## Users Manage user access, roles, and permissions across your skyData environment. The Users section supports role-based access control with multiple role types to match your organizational security requirements. ### User Roles * **Tenant Admin** - Full administrative access across the tenant with ability to manage other users and tenant-level settings * **Instance Admin** - Administrative access at the instance level with contributor capabilities * **Contributor** - Authority to perform data-related activities including data ingestion and solution creation * **Viewer** - Read-only access to view data without modification capabilities * **Copilot User** - Limited access to skyData Copilot functionality only ### Key Features * Add and invite users via email * Assign roles and instance access * Whitelist entire domains for simplified user management * Manage user status (Active/Inactive) * Configure multi-tenant and multi-instance access * Unlink or delete users as needed ## APIs Enable programmatic access to skyData capabilities through the platform API. Generate and manage API keys for integrating skyData data and functionality into custom applications and workflows. ### Key Features * Enable/disable API access for your instance * Generate and manage Primary and Secondary API keys * Regenerate keys for security rotation * Access comprehensive API documentation * Test API endpoints directly from the platform ## Who Benefits * **Administrators** - Full visibility and control over platform operations and user management * **Security Teams** - Monitor activity logs and configure session security settings * **Developers** - Access API keys and documentation for custom integrations * **Compliance Officers** - Audit trails and activity logs for regulatory requirements # Source: https://docs.skypoint.ai/docs/reftab # Connecting to Reftab This guide outlines the steps to seamlessly connect Reftab with skyData, covering setup, configurations, and key considerations. Reftab offers seamless asset management integrations with Power BI for reporting, CDW for automated asset imports, and Google Data Studio for custom visualizations, eliminating manual data entry and ensuring real-time accuracy. Connecting Reftab with SkyPoint AI via an inbuilt connector further enhances data synchronization, enabling AI-driven insights for asset tracking, forecasting, and decision-making. This integration streamlines workflows, reduces errors, and provides businesses with a unified, intelligent view of their resources. ## Prerequisite You need the following details to configure and import data using the Reftab⇗ connector: * Url * Public Key * Secret Key ## To import data using the Reftab connector Follow the below steps to create and configure a new dataflow for the Reftab connector: 1. Navigate to **Dataflow > Imports**. Create new dataflow for reftab connector 2. Click **New dataflow** as indicated by an arrow. The **Set dataflow name** page appears. Set reftab connector dataflow name 3. Enter the desired name for the dataflow in the **Name** text field. 4. Click **Next** to proceed. The **Choose connector** page appears. Choose reftab connector ## Add Reftab connector 1. On the **Choose Connector** page, use the **Search** feature to locate and select the **Reftab** Connector. 2. Enter the **Display Name** for your dataflow in the provided text field. 3. Optionally, add a **Description** in the designated text area. Add reftab connector 4. Click **Next** to proceed. The **Configuration** page appears. Configure details to connect reftab connector ## Connect to the Reftab account 1. Fill in the required details on the Configuration page. 2. Click **Connect**. Connect reftab account on skypoint AI Once the connection is established, the connector can be used to import data from Reftab tables. 3. Scroll down to the **Table Details** section, select the checkboxes for the tables you wish to import, and use the dropdown menu to label them as either **Data** or **Metadata.** Select to import table data from reftab By default, all tables in the **Table Details** section are selected. You can choose to import only specific tables that are relevant to your data processing needs. For example, to import customer data, select tables containing details like name, email, address, and contact information. | Item | Description | | :---------- | :-------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data | | Metadata | Loads Metadata | | File name | Displays the name of the file that you imported. | | Entity name | Displays the imported table name by default. You can rename it if required. | 4. Click **Save** to apply the changes. Save the reftab dataflow Congratulations! for saving the Reftab connector dataflow, which appears on the **Dataflow > Imports** page. ## Run, Edit, and Delete the imported data Once the table is imported, you can execute, modify, and remove the imported table from the Dataflow. Follow the below steps: 1. Go to the **Dataflow** > **Imports** page. Execute reftab dataflow on skypoint AI | Item | Description | | :---------------- | :------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Connector Name | Displays connector name. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables imported. | | Created Date | Displays date of creation. | | Last Refresh Type | Displays the refresh value: Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Actions | Provides multiple options for managing dataflows. | 2. Select the horizontal ellipsis under the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the dataflow | Select **Run history**. | 3. Click **Run** to execute the dataflow. Once the execution is successful, the data pipeline status will update to **Completed**, as illustrated in the figure below. reftab dataflow imports data to skyDataP In the Dataflow's **Run History Description,** you can view error messages related to data import failures from a data source. Additionally, you can check the status, start time, and end time of the data pipeline execution. ## Next step After completing the data import, start the [Master Data Management (MDM) - Resolve](https://docs.skypointcloud.com/docs/stitch.html) process to create a single, unified view of the data. With Skypoint MDM, you can ensure that your data is accurate, consistent, and reliable, making it easier to use for business processes, analytics, and reporting. # Source: https://docs.skypoint.ai/docs/salesforce # Salesforce **To integrate Salesforce CRM Connector, below details are to be configured**: 1. User Name 2. Password 3. Security Token 4. Environment URL ## Steps to Configuration 1. Navigate to **Data** > **Dataflows** and click on *+ Add Dataflow* Image 1 2. Under Select **Dataflow name**, mention the connector name. Alt image 3. From **Choose Connector**, click the connector name **Salesforce**. 3 You will be directed to configuration set up as below. 4. Under **General**, below the **Dataflow Name**, enter the name of the connector and mention the description about the connector (optional) and click on Save. 4 5. Click on **Connector**, which is right next to **General**. Under **Configuration**, mention the details as required. * User Name * Security Token * Password * Environment URL and Click on connect 5 4. Once its completed, you will be able to see the imported entities from the Salesforce connector. ## The following properties are supported for the Salesforce connector service 1. **Environment URL specify the URL of the Salesforce instance.** * Default is "[https://login.salesforce.com](https://login.salesforce.com)". * To copy data from sandbox, specify "[https://test.salesforce.com](https://test.salesforce.com)". * To copy data from custom domain, specify, for example, "https\://\[domain].my.salesforce.com". 2. **Username specify a username for the user account.** 3. **Password specify a password for the user account.** 4. **Security Token specify a security token for the user account.** * Note : Salesforce account password expires after **every 90 days**, along with that new security token is generated and mailed. **For instructions on how to get and reset a security token, [Click here](https://help.salesforce.com/articleView?id=user_security_token.htm\&type=5).** **skyData imports Account, Contacts, Leads, Opportunities, all fields including custom fields.** **Please note that Salesforce rate limits and throttles API calls. API Request Limits and Allocations.** **To maintain optimum performance and ensure that the Lightning Platform API is available to all our customers, Salesforce balances transaction loads by imposing two types of limits, [Click here](https://developer.salesforce.com/docs/atlas.en-us.salesforce_app_limits_cheatsheet.meta/salesforce_app_limits_cheatsheet/salesforce_app_limits_platform_api.htm)** # Source: https://docs.skypoint.ai/docs/salesforcemarketingcloud # Salesforce Marketing Cloud Setup Guide Below are needed to configure Salesforce Marketing Cloud Connector in skyData platform. 1. **Client Id** 2. **Client Secret** 3. **Authentication URL** ## Steps to configuration. 1. Navigate to **Data** > **Dataflows** click on *+ Add Dataflows* Image 1 2. Under *Select Dataflow name*, mention the connector name. ![image](https://user-images.githubusercontent.com/96232751/147830805-2fa670ef-ef02-470d-b1e8-b5982b1654cd.png) 3. From *Choose Connector*, click the connector name **Salesforce Marketing Cloud**. ![image](https://user-images.githubusercontent.com/96232751/147830749-296af44e-84c3-4785-9dd6-fabf40f816d5.png) 4. You will be directed to configuration set up a below. 5. Under **General**, below the *Dataflow Name*, enter the name of the connector and mention the description about the connector (optional) and click on Save. ![image](https://user-images.githubusercontent.com/96232751/147830932-0d48a732-0dc6-4e4c-928b-75b917ae6146.png) 6. Click on Connector, which is right next to General. Under Configuration, mention the details as required. ![image](https://user-images.githubusercontent.com/96232751/147831049-9216f113-67cd-48bd-b5f4-31c032683d65.png) Once you enter all the required details, **Click Connect**: * This will fetch all **Subscriber Lists, Campaigns and Data Extensions**. * Upon clicking the Connect button, it will validate your credentials. If it is successful, you will see a notification **Data loaded successfully** else it will show the error message that login has failed. * Upon successful connection, you will see the names of all **Subscriber Lists**, **Campaigns** and **Data Extensions** imported from the source with headers like Name, Type, Entity Name etc. * Select the required entities and save the data flow. * You can **Run** the data flow as and when required by choosing the **Run action** from dataflow screen. * Upon successful run, two entities will be created named **sfmc\_events** from all event activities and **sfmc\_subscribers** from all subscribers from Lists, Campaigns and Data Extensions. ## Configuring Activities Import You need to make selection from below options: 1. **Activity Start Date** : Set start date for range. Leave as dd-mm-yyyy to import activities since beginning. Select entered date and press *delete* to clear. 2. **Activity End Date** : Set end date for range. Leave as dd-mm-yyyy to import activities till today. Select entered date and press *delete* to clear. 3. **Activities** : Select subset of activities to import. By default, all activities are selected. * The **Date Range** is optional. If it's selected it will fetch all activities since beginning till today. * If **Start Date** is provided it will fetch all activities from given start date. * If **End Date** is provided it will fetch all activities till given end date. * Choose any subset of **Activities** from these event types **"BounceEvent, ClickEvent, ForwardedEmailOptInEvent, ForwardedEmailEvent, OpenEvent, SentEvent, SurveyEvent, UnsubEvent"**. * This will fetch all activities from selected event types to generate a entity **sfmc\_events**. The full refresh/first retrieval of entities will take time to be finished as the number of activities will be huge. ## Obtain Client ID and Client Secret from Salesforce Marketing Cloud Perform the following steps to retrieve the **Client ID** and **Client Secret** for a new package: 1. Log in to **Salesforce Marketing Cloud**. 2. Go to **Setup >> Apps >> Installed Packages** 3. Click **New** to create a new package. 4. In the New Package Details window, enter the name and description for the package. To use OAuth 2.0, select **Create with enhanced functionality (recommended)**. 5. Click **Add Component**. 6. Select **API Integration** as the component type. 7. Click **Next**. 8. Select **Server-to-Server** as the integration type. 9. Click **Next**. 10. Select the following **Permissions for the Server-to-Server properties:** For both import and export integrations workflows of skyData and Salesforce Marketing Cloud, client keys will need the following access permissions: | Workflows | Permissions | | ------------------------------- | ----------- | | Contacts - List and Subscribers | Read, Write | | Hub - Campaigns | Read, Write | | Data - Data Extensions | Read, Write | | Data - Tracking Events | Read, Write | | Provisioning - Accounts | Read, Write | | Automation - Journeys | Read, Write | | Contacts - Audiences | Read, Write | | Channels - Email | Read, Write | | Marketing Cloud Connect | Read, Write | 11. Click **Save**. The Components section displays the Client ID and Client Secret for the package. Perform the following steps to retrieve the Client ID and Client Secret for an existing package: 1. Go to **Setup >> Apps >> Installed Packages** The Installed Packages page lists all the packages installed in the Salesforce Marketing Cloud account. 2. Select a package and go to the **Components** section. * To add a new component, click **Add Component**. * To edit existing component, click **Edit**. The Components section displays the Client ID and Client Secret for the package. 3. Click **Save**. If an existing package uses OAuth 1.0, you cannot edit the package to use OAuth 2.0. You must create a new package to use OAuth 2.0. skyData recommends that you upgrade to OAuth 2.0 before Salesforce Marketing Cloud drops support for OAuth 1.0. For information about configuring installed packages and API integration, see the following URL: [https://developer.salesforce.com/docs/atlas.en-us.noversion.mc-app-development.meta/mc-app-development/index.htm](https://developer.salesforce.com/docs/atlas.en-us.noversion.mc-app-development.meta/mc-app-development/index.htm) Select API Integration as the component type. ## Dealing with **Data Extensions** A data extension in Marketing Cloud Email Studio is a table within the application database that holds your data. It has certain constraints: the name of the data extension and its column names cannot start with an underscore (\_) or it will cause an error. Refer below links for data extension creation and its constraints. [Data Extension Creation](https://help.salesforce.com/articleView?id=mc_es_create_data_extension_classic.htm\&type=5) [Data Extension Name Constarints](https://help.salesforce.com/articleView?id=mc_es_data_extension_error_messages.htm\&type=5) # Source: https://docs.skypoint.ai/docs/schedule # Schedule ## Overview The Schedule module in skyData enables automated data refresh operations for your dataflows and platform processes. By configuring scheduled jobs, you can ensure your data stays current without manual intervention, supporting timely analytics and decision-making. ## Key Capabilities ### Automated Refresh Set up recurring schedules to automatically refresh data imports and run platform processes. Choose from daily, weekly, or monthly intervals to match your data freshness requirements. ### Flexible Scheduling Options * **Daily** - Refresh data at specific times each day * **Weekly** - Select specific days of the week for scheduled runs * **Monthly** - Choose specific days of the month for refresh operations ### Time Zone Support Configure schedules in your preferred time zone to align with business hours and operational requirements. ### Multiple Refresh Times Add multiple refresh times within a single schedule to support different data freshness needs throughout the day. ### Refresh Types * **Full Refresh** - Complete refresh of all data in the dataflow * **Incremental Refresh** - Update only data that has changed since the last refresh ### Master Data Scheduling Schedule identity matching and resolution processes to keep your unified profiles current and accurate. ## Business Benefits * **Data Currency** - Ensure analytics and reports reflect the latest information * **Operational Efficiency** - Eliminate manual refresh tasks and reduce operational overhead * **Consistency** - Maintain predictable data update cycles across your organization * **Resource Optimization** - Schedule intensive operations during off-peak hours ## Who Benefits * **Data Engineers** - Automate routine data refresh operations * **Analytics Teams** - Rely on consistently fresh data for reporting * **Operations Teams** - Reduce manual workload through automation * **Business Users** - Access up-to-date information for decision-making # Source: https://docs.skypoint.ai/docs/sendgridexport # Exporting Data to SendGrid ## Overview SendGrid is a customer communication platform that allows you to send an email and manage recipient lists. You can export your data from skyData with built-in data Lakehouse to SendGrid and use them for email marketing and campaigns. ## Prerequisite * You have a [SendGrid account](https://sendgrid.com/) and an [API key](https://docs.sendgrid.com/ui/account-and-settings/api-keys). * You have created an [Audience](audiences.md) or [Profile](profiles.md) to export entities. If you need help and support, click **Need help?** button in the right corner of the page. For more information regarding raising a new ticket, refer [Help+Support](help%2Bsupport.md) section. ## To set up a SendGrid connection 1. In the left pane, go to **Dataflow** > **Exports**. The **Exports** window appears. By default, the **Export gallery** tab opens. Alt text You can add the SendGrid connection in two ways. However, the procedure is similar in both cases. These are as follows: * In the **Export Gallery** tab, click the **Set up** button in the **SendGrid** widget. * In the **Destinations** tab, click **Add Export** to configure with the SendGrid connector. Alt text 2. To establish a SendGrid connection through **Export Gallery**, click the **Set up** button in the **SendGrid** widget. Alt text Alt text 3. Enter **Name** and **Display name**. You can choose a name that explains the purpose of the connection. 4. Enter your SendGrid **API Key**. 5. Click **Next**. Alt text 6. Select the entities that you want to export and click **Next**. Alt text 7. Select the attributes that you want to export and click **Create**. Custom attribute allows you to add more attributes that you want to export. You can do mapping and export only the selected attributes. The custom fields are completely customizable to the use cases and user information that you need. You can also delete the added attributes. * To delete an added attribute, click the **Delete** icon. After creating an export connection, it appears in the **Destinations** tab. Alt text | Item | Description | | :------------ | :---------------------------------------------------------------------------------------------------------------------------- | | Name | Name of the Export connection. | | Display name | Displays the name of the exported data. It can be different from the Export connection name. Also, you can change this later. | | Type | Displays the connector type. | | Created | Displays date of creation. | | Last exported | Displays last modified date. | | Status | Indicates whether the data is exported successfully. | | Actions | Option to perform activities after completion of configuration, for example, edit, run. | 8. Select the horizontal ellipsis in the **Actions** column and do the following: | If you want to | Then | | :----------------------------------------------------------------------------- | :--------------------------- | | Modify the SendGrid connection | Select **Edit**. | | Execute the export process | Select **Run**. | | Cancel the execution of the data export | Select **Cancel Run**. | | Configure a refresh schedule to add new or updated data in the SendGrid export | Select **Schedule**. | | Delete the SendGrid export connection | Select **Remove** > **Yes**. | | See the history of the SendGrid export | Select **History**. | ## Edit the SendGrid connection You can update the exported data for the existing SendGrid export account. 1. In the **Destinations** tab, select the SendGrid connection that you want to update. 2. In the **Actions** column, click the horizontal ellipsis and select **Edit**. Alt text 3. You can change the display name and select **Next** to modify entities and attributes. 4. Click **Update** and **Run** to execute modification. ## Schedule the SendGrid export skyData provides you to set a time for new or updated data to include in the exported file. The export process runs whenever the system refreshes data or system refresh is triggered. With the **Schedule** feature, you can customize the refresh schedule. By default, export runs as part of every scheduled system refresh run for the particular instance. The system refresh schedule is shown on the **Settings** page. You can check through **Settings** > **Schedule**. 1. In the **Destinations** tab, select the SendGrid connection that you want to schedule. 2. In the **Actions** column, click the horizontal ellipsis and select **Schedule**. The **Schedule** window appears. Alt text 3. To activate the scheduled export automatically, click on the **Schedule run** button to set **On**. If turned off, the export will only run if activated manually. 4. Choose a **Recurrence** value to refresh the exported data automatically. 5. Click **Save** to apply your changes. # Source: https://docs.skypoint.ai/docs/settings # Settings ## Overview The Settings module in skyData provides centralized administration and configuration capabilities for managing your platform environment. From here, administrators can control platform behaviors, manage users, configure schedules, and oversee billing and tenant operations. ## Key Capabilities ### Platform Management Configure and monitor core platform operations including system status, activity logs, session management, user administration, and API access. The Platform settings give administrators full visibility into system health and user activities. ### Scheduling Automate data refresh operations by setting up scheduled jobs that run at daily, weekly, or monthly intervals. Keep your data synchronized and up-to-date without manual intervention. ### Billing Management View and manage your subscription plans, track usage, apply promotional codes, and access billing history. Monitor resource consumption across tenants and instances. ### Instance Administration Create and manage multiple instances within your tenant, each acting as a distinct environment for organizing data and configurations. Copy settings from existing instances or create new ones from scratch. ### Tenant Configuration Set up and configure tenant-level settings including storage accounts, Azure Data Lake Gen2 connections, Azure AI Search, and database configurations. ## Who Benefits * **Administrators** - Full control over platform configuration, user management, and system monitoring * **IT Teams** - Manage security settings, API access, and infrastructure configurations * **Finance Teams** - Access billing information and usage reports * **Operations Teams** - Configure schedules and monitor platform health # Source: https://docs.skypoint.ai/docs/sfmcexport # SalesForce Marketing Cloud Export lets you extract data from the Lakehouse and load into Salesforce Marketing Cloud. ## Steps to Export 1. Navigate to **Action** > **Export**. You will see all the differenet categories of storages that can be used for export. Alt text 2. Go to **Destinations** and click on ***Add Export***. 3. Fill up the name and the display name of the export and click on the dropdown column to select one of the options. Here we select Salesforce Marketing Cloud. Alt text 4. As soon as we select it, there will be a choice to either select an existing dataflow or to create a new one. 5. In case you are choosing to create a new one, fill up the **clientid** , **clientsecret** and the **Authentication URL** and then click on NEXT. 6. You will be able to see all the Lakehouse data available infront of you entity wise. Choose all the entities that you will need to export. 7. Click on **Save**. Your export will be started. # Source: https://docs.skypoint.ai/docs/sftp # Connecting to SFTP (Simple File Transfer Protocol) ## Overview SFTP (Simple File Transfer Protocol) features hierarchical folders and file management. It supports user authentication that requires the user to enter username and password to login into the server. You can implement SFTP by opening a TCP connection to the remote host’s port. SFTP consists of 11 commands and supports features like user access control, file transfers, directory listing, directory changing, file renaming, and deleting. It supports three types of data transmission, namely: * American Standard Code for Information Interchange (ASCII): ASCII bytes are taken from the source system file, sent over the connection, and saved in the destination system file. * Binary: The 8-bit bytes are extracted from the source system file, sent through the connection, and saved in the target system file. * Continuous: The bits are taken from the original system file and transmitted over the network in 8-bit bytes without respecting word boundaries. Without any word boundaries, the bits are continuously received by the target system. ## Prerequisite You will need the following details to configure and import data: * Host * Port * Host Key Fingerprint * Username * Password * Storage path. ## Limitation * Simple File Transfer Protocol (SFTP), which uses the Transmission Control Protocol port number 115, is an unencrypted, simplified form of File Transfer Protocol (FTP). It is less powerful than FTP and contains certain helpful features that are missing from Trivial FTP (TFTP). ## Import data using SFTP connector Follow the below steps to create a new dataflow for the SFTP import connector: 1. Go to **Dataflow > Imports**. 2. Click **New dataflow**. The **Set dataflow name** page appears. Alt text 3. In the **Set dataflow name** page, type dataflow name in the **Name** text area. 4. Click **Next**. The **Choose connector** page appears. Alt text ## To add SFTP connector 1. In the **Choose connector** page, select **SFTP** connector. You can also use the Search feature to find the connector. The **SFTP** connector is located under the **Marketing** category. Alt text 2. Enter **Display Name** for your dataflow in the text area. 3. Enter **Description** for your dataflow in the text area. 4. Click **Next**. The **Connect to SFTP** page appears. Alt text ## To configure SFTP Follow the below steps to configure the connection to SFTP: 1. Enter your credentials such as **Host**, **Port**, **Host Key Fingerprint**, **User Name** and **Password** to connect with SFTP. 2. Click the folder icon on Storage Path to select the storage path folder. Once you select your Storage path, the **Table Details** columns appear. Alt text 3. Enter the **Table details** to process the data. | Item | Description | | :------------------ | :-------------------------------------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data. | | Metadata | Loads Metadata. | | File Name | Displays the name of the file that you imported. | | Table Name | Displays the imported table name. | | Datetime format | Displays a number of **Datetime Formats** and skyData is set to automatically detect them. | | Delimiter | Displays available separators for the variables in the imported data. | | First Row as Header | Check the box for the system to automatically collect the data according to the Header Contents. | | Advanced Settings | Select the options to modify the default settings. It gives more flexibility to apply advanced use cases. | 4. If necessary, select the **Advance settings** option to modify the default settings. The **Advanced settings** pop-up appears. Alt text | If you want to | Then | | :----------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------- | | Modify data types such as fixed or variable data types. | Select from the **Compression type**. It allows you to reduce the size of data by removing the number of bits. | | Change the delimiter | Click **Row delimiter**. By default, a column delimiter is selected, and each row is separated with a comma. | | Change information or instruction | Choose from the **Encoding** list. By default, UTF-8 encoding is selected. | | Modify the escape character such as backslash (\\) or slash (/) | Select from the **Escape character**. | | Apply different quote characters such as Single quote (') or Double quote ("). | Select from the **Quote character**. | 5. Click **Save** to apply the changes. ## Run, edit, and delete the imported data After saving the connection, imported data appears on the Dataflow page. Also, you can see a list of the created tables in the [Databases](entities.md) section. Alt text | Item | Description | | :---------------- | :------------------------------------------------------------------------------------------------------- | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables. | | Created Date | Displays date of creation. | | Last refresh type | Displays the refresh value. You can see if the value is Full or Incremental after the last data refresh. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Group by | Option to view the items in a specific Group (For example, name, type, status). | * Select the horizontal ellipsis in the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run** history. | ## Next step After completing the data import, start the [Master Data Management (MDM) - Stitch](stitch.md) process to develop a unified view of your customers. # Source: https://docs.skypoint.ai/docs/sharepoint # How to configure SharePoint on skyData Studio This guide walks you through the steps to connect SharePoint with skyData, covering setup, configuration, and key considerations. Microsoft SharePoint is a collaboration and document management platform that enables teams to securely store, organize, and share content. Integrated with Microsoft 365, it streamlines workflows, enhances productivity, and supports enterprise content management. ## Prerequisite You need the following credentials to configure and import data from SharePoint: * Client Secret * Application (Client) ID * Directory (Tenant) ID * SharePoint site URL Users can setup SharePoint datasource on skyData studio by following the below steps: * **Azure App Registration:** Register an app in Azure AD and generate credentials for SharePoint access. * **skyData Studio Setup:** Configure SharePoint as a data source using the app credentials and site URL. ## Azure App Registration 1. Sign in to the [Azure Portal](https://portal.azure.com/). login to azure portal 2. Search for **App Registration** in the search bar, as indicated with an arrow. Search for app registration 3. Next, click on the **New Registration** button located at the top left corner, as highlighted. Click on new registrations 4. Enter the display name for the application following a standard naming convention, ensuring it begins with a letter and contains only letters or numbers. Enter display name for application registration 5. Select the account type by choosing the radio button next to **Accounts in this organizational directory only (Skypoint only – single tenant).** 6. Next, click the **Register** button at the bottom. The **Application Registration** page will appear: 7. Navigate to the **Manage** tab and select **API Permissions** from the dropdown menu options. Click on the manage tab and then api permissions 8. Then, click on **Add a permission,** as highlighted. Add a permission This action triggers a pop-up that appears on the right side of the webpage. 9. Click on **Microsoft Graph** to continue. Click on microsoft graph 10. Select **Application Permissions.** Select application permissions 11. Use the search bar to look up for **Sites.** Look up for sites 12. Click on **Sites** to expand the menu options, and select the checkboxes for the required permissions. Click on sites 13. To grant read access permission to skyDataP on the SharePoint datasource, select the checkbox as illustrated in the figure below: grant read access permission 14. Next, click on **Add Permissions.** 15. The permissions will be added and displayed under the **Configured permissions** as shown in the figure below: Permissions gets added and displayed 16. If the permissions' status displays as **Not granted for Skypoint,** users should request approval from a global administrator. Permission not granted for skypoint 17. To grant admin consent for Skypoint, users with group admin access should: a. Click the designated tab indicated by the arrow. b. Then by clicking on the **Yes** button, confirm the changes and grant admin consent. Granting Admin consent 18. Post granting admin consent, status gets updated as shown in the figure below: API permission granted 19. Next, navigate to the **Certificates & secrets** module and select **New client secret** under the **Client secrets** tab. Adding new client secret 20. A form pops up to the right to add/generate a client secret value. Provide a description or just type **Client Secret** in the description field and set the expiration period according to your requirements or select the recommended duration of 180 days from the drop down. Adding description and expiry date to client secret 21. Click the **Add** button to generate the client secret. 22. Copy the **Client Secret** by clicking the copy icon under the **Value** tab, as illustrated in the figure below. * Ensure to immediately copy the client secret value, as it will be hidden once you navigate away from the page and cannot be retrieved later. * If you forget to copy the client secret value, you can delete the existing client secret and generate a new one by repeating the above steps. copy the client secret value by clicking on the copy icon 23. Next, navigate to the **Overview** module to copy credentials such as the **Application (Client) ID and Directory (Tenant) ID.** collect client id and tenant id from overview module 24. Then, copy The **SharePoint site URL** up to the identifiable **site name,** to be used as the site URL in the form when setting up SharePoint on skyData Studio. (Refer the figure below) For example, if the SharePoint site URL is structured as follows: [https://ffdinc.sharepoint.com/sites/SharepointTLD/shared%39Documents?Forms?altitems.aspx](https://ffdinc.sharepoint.com/sites/SharepointTLD/shared%39Documents?Forms?altitems.aspx) Use the SharePoint site URL up to the identifiable site name: [https://ffdinc.sharepoint.com/sites/SharepointTLD](https://ffdinc.sharepoint.com/sites/SharepointTLD) copy sharepoint site url up to the identificable site name Copy and secure the **Client Secret**, **Application (Client) ID**, **Directory (Tenant) ID** from the **Azure Portal**, along with the **SharePoint site URL**, for connecting SharePoint data in **SkyPoint AI Studio**. ## skyData Studio Setup Users can setup SharePoint datasource on skyData studio by following the below steps: ### A. Initiate setting up SharePoint on skyData Studio 1. Sign in to **skyData Studio.** 2. Navigate to **Dataflow** and choose **Connections.** 3. Click on **Set up** for SharePoint datasource. Navigate to dataflow, click on connections 4. Fill in the required connection details and validate credentials. Setup sharepoint by filling the required credentials 5. Click **SAVE** button to confirm the changes, once the credentials are verified. Post credentials getting verified click save button **Congratulations!** You have successfully set up SharePoint data connections. ### B. Add SharePoint data source to ingestion pipeline on skyData Studio 1. Go to **AI Agents** and choose **Unstructured Data.** 2. Click on **Add Source.** Go to ai agents and then to unstructured data to click on add source 3. Choose SharePoint as the type of data source. Select sharepoint as datasource 4. Add name and display name. 5. Select connection from the drop-down menu setup earlier. Add name and display name 6. Select the storage path by clicking on the folder icon. 7. Click the drop-down menu to select the ingestion mode. 8. Next, hit the **SAVE** button. Select storage path and ingestion mode 8. View saved SharePoint datasource under AI Agents as highlighted in the figure below. 9. Right click on the horizontal ellipsis against the saved data source to complete data ingestion. Check status for data ingestion **Awesome!** You have successfully configured the SharePoint datasource on skyData. # Shopifyimport Source: https://docs.skypoint.ai/docs/shopifyimport # Connecting to Shopify # Import data ## Overview Shopify is an eCommerce platform for online stores and retail point-of-sale systems. You can use Skypoint AI's built-in connector for importing data from Shopify. This document will guide you through the process of connecting Shopify to Skypoint AI. ## Prerequisite You must have the following details to configure and import data using Shopify import connector: * [Shopify Account](https://accounts.shopify.com/lookup?rid=b2c3149a-a288-42ff-9916-0ba6717332d0) * URI * API password * API key. You can refer to the Shopify Setup guide document to learn more about the Prerequisites. ## Import Data using Shopify connector 1. In the left pane, go to **Dataflow** > **Imports**. The **Dataflow** window appears. Alt text 2. Click **New Dataflow** to create dataflow. Alt text 3. Enter a **Name** to identify your Dataflow. The name must start with a letter and can only include letters and numbers, with no spaces between them. 4. Click **Next**. ## Add Shopify Connector 1. In the **All categories** or **Ecommerce** tab, select the **Shopify** connector to start the configuration. 2. Also, you can use the **Search** function to find the **Shopify** connector. Alt text 3. Select the Shopify connector to start the configuration. Alt text 4. Enter the **Display Name** to identify your Dataflow. 5. In the **Description** box, you can enter an explanation of the connector. 6. Click **Next**. Alt text ## To configure Shopify Follow the below steps to configure the connection to Shopify: 1. Enter your credentials such as **Uniform Resource Identifier (URI)**, **API Password**, and **API Key** to connect with Shopify. 2. Click **Connect**. Alt text 3. After configuration, you can enter the **Table Details** to process the data. In the **Table Details** column, you can select only those data fields that you want to import and process. | Item | Description | | :------------------------ | :---------------------------------------------------------- | | Purpose | Option to assign a purpose (Data, Metadata) for each table. | |
Data
| Loads customer data. | |
Metadata
| Loads Metadata. | | File Name | Displays the name of the file that you imported. | | Table Name | Displays the imported table name. | 4. Click **Save** to apply the changes. ## Run, edit, and delete the imported data 1. After saving the connection, the Shopify connector appears on the Dataflow page. Also, you can see the list of created tables in the [Databases](https://skypointcdpdocs.z22.web.core.windows.net/docs/entities.html) section. Alt text | Item | Description | | :----------- | :------------------------------------------------------------------------------ | | Name | Displays the name of the Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables. | | Created Date | Displays date of creation. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Group by | Option to view the items in a specific Group (For example, name, type, status). | 2. Select the horizontal ellipsis in the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run History**. | In the Dataflow, you can view the error message for data import failures from the data source under **Run History** > **Description**. # Setup guide ## Overview This document will help you gather all credentials for connecting [Shopify](https://accounts.shopify.com/lookup?rid=9a773bd0-16b4-425b-95d0-a6767c33b4a7) with Skypoint AI. ## Prerequisite You require an active Shopify store and relevant [permissions](https://shopify.dev/apps/auth/admin-app-access-tokens#permissions-required-to-assign-scopes-to-a-custom-app) to create a custom app as well as assign API scopes. ## Enable Custom app development Follow the below steps to enable custom app development in your **Shopify admin**: 1. Go to **Settings > Apps and sales channels**. 2. Click **Develop apps for your store**. Alt text 3. Click **Allow custom app development**. Alt text 4. Click **Create an app** to integrate your store’s data with external services or build a custom storefront. Alt text 5. A **Create an app** pop-up appears. Alt text * Type your **App name** in the text area. * Select the **App developer** from the drop-down list. 6. Click **Create app**. Your app gets created. Alt text 7. Click **Configure Admin API scopes**. 8. Select your preferred **Admin API access scopes**. Alt text 9. Configure the **Storefront API scopes**. 10. Click **Save** and then click **Install app**. A confirmation pop-up appears. Alt text 11. Click **Install**. ## To find the Configuration credentials Follow the below step to find the required credentials for configuring Shopify import connector: * Go to **Apps and sales channels > API credentials** to find the **API key**, **API access token**, and **URI**. Alt text You can use the Admin API access token as the API password while configuring your Shopify import connector. ## Next step After completing the data import, start the [Master Data Management (MDM) - Stitch process](stitch.md) to develop a unified view of your customers. # ERD SHOPIFY ERD # Source: https://docs.skypoint.ai/docs/sitelligence # Connecting to Sitelligence This guide provides a step-by-step approach to effortlessly connecting Sitelligence with skyData. Sitelligence connector is an API endpoint designed to integrate Sitelligence’s Resident Portal system with property management platforms. This integration facilitates key functionalities such as maintenance requests, payment processing, and resident communications. Access to the API typically requires authentication, and detailed documentation including capabilities, request/response formats, and integration guidelines is generally provided to authorized partners. The Sitelligence connector in skyData enables seamless integration with Sitelligence’s Resident Portal platform, commonly used in senior living and property management communities. It allows organizations to efficiently ingest and sync data from the Sitelligence system using secure API methods. With support for authentication, automated scheduling, and data transformation, the connector streamlines data integration into Skypoint’s unified models empowering enhanced analytics, resident engagement insights, and AI-driven applications. ## Prerequisite You need the following details to configure and import data using the Sitelligence⇗ connector: * API Partner * API Route * API ID * Load Records From (Days) ## To import data using the Sitelligence connector Follow the below steps to create and configure a new dataflow for the Sitelligence import connector: 1. Navigate to **Dataflow > Imports**. Alt image 2. Click **New dataflow** as indicated by an arrow. The **Set dataflow name** page appears. Alt image 3. Enter the desired name for the dataflow in the **Name** text field. 4. Click **Next** to proceed. The **Choose connector** page appears. Alt image ## Add Sitelligence connector 1. On the **Choose Connector** page, use the **Search** feature to locate and select the **Sitelligence** Connector. 2. Enter the **Display Name** for your dataflow in the provided text field. 3. Optionally, add a **Description** in the designated text area. Alt image 4. Click **Next** to proceed. The **Configuration** page appears. Alt image ## Connect to the Sitelligence account 1. Fill in the required details on the Configuration page. 2. Click **Connect**. Alt image Once the connection is established, the connector can be used to import data from Sitelligence tables. 3. Scroll down to the **Table Details** section, select the checkboxes for the tables you wish to import, and use the dropdown menu to label them as either **Data** or **Metadata.** Alt image By default, all tables in the **Table Details** section are selected. You can choose to import only specific tables that are relevant to your data processing needs. For example, to import customer data, select tables containing details like name, email, address, and contact information. | Item | Description | | :---------- | :-------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data | | Metadata | Loads Metadata | | File name | Displays the name of the file that you imported. | | Entity name | Displays the imported table name by default. You can rename it if required. | 4. Click **Save** to apply the changes. Alt image Congratulations ! for saving the Sitelligence connector dataflow, which appears on the **Dataflow > Imports** page. ## Run, Edit, and Delete the imported data Once the table is imported, you can execute, modify, and remove the imported table from the Dataflow. Follow the below steps: 1. Go to the **Dataflow** > **Imports** page. Alt image | Item | Description | | :---------------- | :------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Connector Name | Displays connector name. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables imported. | | Created Date | Displays date of creation. | | Last Refresh Type | Displays the refresh value: Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Actions | Provides multiple options for managing dataflows. | 2. Select the horizontal ellipsis under the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the dataflow | Select **Run history**. | 3. Click **Run** to execute the dataflow. Once the execution is successful, the data pipeline status will update to **Completed**, as illustrated in the figure below. Alt image In the Dataflow's **Run History Description,** you can view error messages related to data import failures from a data source. Additionally, you can check the status, start time, and end time of the data pipeline execution. ## Next step After completing the data import, start the [Master Data Management (MDM) - Resolve](https://docs.skypointcloud.com/docs/stitch.html) process to create a single, unified view of the data. With Skypoint MDM, you can ensure that your data is accurate, consistent, and reliable, making it easier to use for business processes, analytics, and reporting. # Source: https://docs.skypoint.ai/docs/skyadmissions skyAdmissions skyAdmissions ## Overview skyAdmissions provides automated referral intake, scoring, eligibility checks, and reimbursement optimization for SNFs and post-acute providers. It uses unified data and AI to ensure facilities admit the right patients and respond quickly, directly affecting census, NOI, and hospital relationships. ## What It Does Aggregates referrals from multiple channels: hospitals, payers, referral portals, and fax/email feeds via connectors. Uses AI to extract clinical and financial data, score referrals (clinical fit, reimbursement, LOS risk, payer mix), and prioritize which patients to accept. Automates eligibility checks and documentation required for SNF/post-acute admissions. Ties into value-based and post-acute workflows for comprehensive care coordination. ## Why It's Needed in the Market SNFs and post-acute providers operate on thin margins and are flooded with referrals, but can't manually analyze every case for clinical appropriateness and financial viability. **skyAdmissions uses unified data + AI** to ensure facilities admit the "right" patients (clinical fit, payer mix, capacity) and respond quickly, which directly affects: * Census and occupancy rates * Net operating income (NOI) * Hospital and referral partner relationships ## Key Metrics It Moves Better referral prioritization drives higher, more stable census. Improves payer mix and reimbursement optimization. Faster time to accept/decline, improving referral partner satisfaction. Frees staff from manual chart reviews. # Source: https://docs.skypoint.ai/docs/skyadvisor skyAdvisor skyAdvisor ## Overview skyAdvisor is the AI Sales Agent Suite that turns website visitors and inbound inquiries into qualified, meeting-ready leads using web chat, SMS, WhatsApp, phone, and guided surveys — with seamless real-time collaboration between your human sales team and AI. Industry-specific behavior is delivered through solution modules for Senior Living, Provider Groups, Health Plans, Health Tech, SNF, Health Systems, Community Health, and Home Health. High-consideration sales have structural friction. Most organizations lose qualified prospects to missed after-hours calls, slow web response, generic chatbots, incomplete lead data, and fragmented systems. skyAdvisor ensures every visitor and inquiry receives instant, personalized engagement while your sales team focuses on building relationships with prospects who are ready to convert. **Part of the Skypoint AI Platform "365" Product Suite** ## Why skyAdvisor Drives More Move-Ins Every website visitor gets immediate, personalized engagement. No more lost leads due to slow response times or after-hours gaps. AI asks the right questions about care needs, timeline, budget, and decision-makers so you wake up to qualified leads, not cold inquiries. Purpose-built for senior living with knowledge of care levels, amenities, and pricing. Answers questions accurately without generic chatbot responses. AI detects urgency, emotion, and complexity. High-stakes moments like a recent fall or memory concerns trigger immediate human connection. ## How It Works AI responds within seconds via web chat, SMS, WhatsApp, or phone — 24/7, including nights and weekends. Guided surveys and intelligent conversation capture care needs, timeline, budget, urgency, and decision-maker context. AI escalates to your human sales team at the right moment with a complete lead briefing — conversation history, care needs, urgency level, and suggested next steps. Tour is scheduled, lead is synced, and your team has everything needed for a meaningful first conversation. ## Key Capabilities ### AI-Powered Lead Engagement Embed skyAdvisor on your website to greet visitors, answer questions about your communities, assess care needs and timeline, and identify families ready to tour. Configure branding, welcome messages, and community-specific knowledge in minutes. ### Hybrid AI + Human Model Unlike fully automated chatbots, skyAdvisor knows when AI should step aside for human connection: | AI Handles | Humans Handle | | :----------------------------------------------------- | :--------------------------------------------------- | | Instant response and initial engagement | High-emotion family moments (falls, memory concerns) | | FAQ answers about communities and care | Complex care questions requiring expertise | | Structured qualification (care type, timeline, budget) | Relationship building with serious prospects | | Lead scoring and prioritization | Final tour conversations | | Tour scheduling with calendar integration | Sensitive follow-ups and financial discussions | ### Complete Lead Briefing When your sales team takes over, they receive a full summary including conversation history, care needs, urgency level, and suggested next steps. No cold handoffs or missing context. ### Real-Time Dashboards Track every conversation, lead score, and conversion across all communities. See what's working, where to focus, and how to improve lead engagement across your portfolio. ## Product Modules ### Core Engagement Embeddable chatbot with instant engagement, intelligent qualification, and intent detection for website visitors. Real-time human + AI collaboration with seamless takeover, AI conversation summaries, and response suggestions. Inbound and outbound SMS with AI-powered responses, multi-number support, delivery tracking, and unified inbox. WhatsApp messaging with AI responses integrated into the unified inbox. 24/7 inbound call coverage with lead capture, tour intent detection, call transcription, and recordings. Callers can be routed directly to a preferred representative at the greeting, bypassing queues. Voice flows support AI blocks for natural conversation and can email captured URLs to callers. Review call history, recordings, and full transcriptions in a dedicated interface. ### Lead Management Lightweight CRM with lead tracking, scoring, notes, activities, and external CRM sync. AI scoring (0-100), conversion prediction, urgency detection, and sentiment analysis throughout conversations. Two-way sync with HubSpot — bulk lead export, inbound contact import, per-lead sync, and auto-enable on new leads. Two-way sync with Zoho CRM with webhook-based inbound support for real-time contact updates. ### Surveys & Assessments Needs assessment capturing care type, timeline, ADLs, and decision-maker context. Adaptive surveys with conditional display/skip logic based on previous answers. Guided assessments with motivational interviewing, multi-dimensional scoring (readiness, care level, financial, cognitive), path selection, and branded PDF reports with care recommendations. ### Tours & Scheduling AI-assisted booking with calendar integration for streamlined tour coordination. Automated reminder system with SMS and email to reduce no-shows. Automatic follow-up and rebooking for missed tours. Automated review request generation after completed tours. ### Marketing Automation Multi-step automated SMS/email campaigns with enrollment and execution engine. Custom landing pages with section management, forms, and lead capture. Downloadable content (eBooks, guides) for lead capture. Variant testing for modals and widgets, plus behavior-based triggers (scroll, time, exit-intent, click). ### Analytics & Training KPI metric cards, activity trend charts, conversion funnel, lead scoring, and hot leads tracking. End-to-end UTM attribution, source/channel tracking, and campaign analytics. Role-play practice with AI prospect simulation, session scoring, and performance tracking for sales reps. ### Content Management Structured Q\&A pairs with categories and keyword matching for instant responses. Video gallery, photo gallery, floor plans, staff profiles, amenities, testimonials, and pricing management. Time-sensitive promotional messages with scheduling and markdown support. Per-staff, per-type, and per-channel subscriptions with custom branded templates, icon and color customization, tappable phone numbers on mobile, and inline markdown links. AI escalation alerts can target a single staff member for end-to-end reliability. Custom roles with granular navigation and feature permissions, scoped per location or instance. ## Business Impact Average response time to website inquiries Lead engagement coverage including nights and weekends More leads qualified per day Increase in tour bookings ## Who Benefits Most Turn website visitors into tour-ready leads with AI that qualifies, engages, and schedules. Get real-time alerts when high-intent families need immediate attention. Never miss a lead again. AI handles initial engagement while you focus on building relationships with families who are ready to tour and make decisions. See lead engagement across all communities in one dashboard. Track conversion rates, response times, and sales team performance with actionable insights. Maximize ROI on marketing spend with instant lead capture and qualification. Get clear attribution from first click to scheduled tour across all channels. ## Trust & Security skyAdvisor is built with enterprise-grade security for healthcare and regulated industries: * **HITRUST r2 Certified** - Protected health information handled securely * **SOC 2 Type 2** - Enterprise-grade security controls * **Zero Data Training** - Your conversations never train our models * **Audit Logs** - Complete conversation history for compliance * **Human Escalation** - AI knows when to step aside for human empathy ## Platform Integration skyAdvisor is part of Skypoint's composable platform, connecting seamlessly with: * **skyData** — Unified data foundation and workflow orchestration * **skyMDM** — Master data for accurate resident and family records * **skyVoice** — Voice AI for phone-based lead engagement * **skyChat** — Conversational AI across digital channels * **skyReputation** — AI-powered reputation management for review and sentiment context ## Get Started skyAdvisor is available as part of the Skypoint platform. Contact your Skypoint representative to configure skyAdvisor for your organization's sales operations. # Source: https://docs.skypoint.ai/docs/skyagent skyAgent skyAgent ## Overview skyAgent is the AI workforce layer across your entire operation, embedded in every Skypoint application. skyAgent executes tasks, triggers workflows, and does the admin work so your staff doesn't have to. It powers the autonomous, agentic capabilities across the entire platform — enabling products like skyAuth, skyAdvisor, skyCare, skyCommand, and others to reason, act, and orchestrate workflows without manual intervention. skyAgent is the execution engine that turns insights into actions across sales, marketing, engagement, operations, workforce, finance, analytics, care, and compliance. ## Key Capabilities skyAgent independently reasons through multi-step workflows, making decisions and taking actions based on real-time data and context. Not suggestions — work that gets done. Coordinates actions across multiple Skypoint products — triggering prior authorizations, care plans, referrals, scheduling, compliance tasks, and more from a single agentic workflow. skyAgent is not a standalone tool. It is embedded inside skyAdvisor, skyVoice, skyCare, skyCommand, skyAuth, skyHire, skyComply, and every other Skypoint application — powering their autonomous capabilities. Purpose-built for healthcare operations with understanding of clinical workflows, payer requirements, regulatory constraints, and care coordination patterns across senior living, post-acute, and health systems. Automates the admin work that consumes staff time — scheduling, documentation, billing reconciliation, reporting, compliance tracking, and back-office coordination. Operates within Skypoint's HITRUST r2-certified environment with full audit trails, role-based access, and PHI-safe processing. SOC 2 Type 2 and HIPAA compliant. ## Where skyAgent Works skyAgent powers autonomous capabilities across the entire resident and patient lifecycle: | Stage | Applications Powered | What skyAgent Does | | ----------------- | ----------------------------------------- | ------------------------------------------------------------------------------ | | **Awareness** | skyReputation | AI-generated review responses, survey automation, search optimization | | **Consideration** | skyAdvisor, skyVoice | 24/7 lead engagement via chat, SMS, phone, and email; real-time sales coaching | | **Decision** | skyAdvisor, skySign | Deposit processing, unit assignment, digital contracts, eSignature workflows | | **Move-In** | skySign, skyCare | Move-in checklists, care assessments, care level determination | | **Resident Life** | skyCare, skyReputation, skySupport | ADL tracking, medication management, family engagement, helpdesk resolution | | **Operations** | skyCommand, skyInsights, skyChat | KPI monitoring, AI briefings, forecasting, policy lookups, content generation | | **Workforce** | skyHire, skyLearn, skySchedule, skyComply | Recruiting automation, training compliance, shift planning, survey readiness | ## Business Impact Staff spend 60% of their time on tasks AI agents can handle — scheduling, documentation, billing reconciliation. Operators using Skypoint recover 5-10% in labor efficiency. 100+ hours saved per community per month. Automated reporting, AI briefings, and back-office workflow execution that used to consume entire teams. ## Who Benefits | Role | Value | | -------------------- | ----------------------------------------------------------------------------------------- | | Operations Leaders | Automated, reliable execution of complex multi-step workflows across the entire portfolio | | Clinical Teams | AI-assisted care coordination and decision support embedded in existing EHR workflows | | Sales Teams | 24/7 lead engagement, follow-up automation, and real-time coaching without manual effort | | Finance Teams | Automated billing reconciliation, reporting, and revenue cycle task execution | | IT & Engineering | Extensible agent framework for building custom agentic applications via skyMSP | | Executive Leadership | Measurable operational efficiency gains across every product powered by skyAgent | # Source: https://docs.skypoint.ai/docs/skyauth-payor skyAuth skyAuth ## Overview skyAuth for Payors streamlines the prior authorization review and decision process for health plans. It automates medical necessity reviews, reduces turnaround times, and improves consistency in authorization decisions. ## Key Capabilities AI-powered medical necessity determination Apply evidence-based clinical guidelines consistently Support reviewers with relevant clinical information ## Features ### Automated Determination Apply clinical criteria automatically to straightforward cases, reducing reviewer workload. ### Clinical Guidelines Integration Integrate with evidence-based guidelines like InterQual, MCG, and custom criteria. ### Reviewer Workbench Provide reviewers with all relevant information in a streamlined interface. ### Performance Analytics Track authorization metrics, turnaround times, and decision patterns. ## Use Cases * **Medical Necessity Review**: Automate routine medical necessity determinations * **Utilization Management**: Manage inpatient and outpatient utilization reviews * **Pharmacy Authorization**: Process prescription prior authorization requests * **Appeals Processing**: Streamline the appeals review process # Source: https://docs.skypoint.ai/docs/skyauth-provider skyAuth skyAuth ## Overview skyAuth is an agentic prior authorization platform that automates the entire PA lifecycle — from intake and eligibility verification through submission, payer follow-up, and decision writeback. It connects directly with major payers and EHR systems, uses AI to gather clinical evidence and complete payer-specific forms, and gives care teams real-time visibility through a dashboard and analytics suite. ## How It Works Tasks are pulled automatically from your EHR portal. Patient demographics, clinical notes, procedure codes, and supporting documents are extracted and validated. A confidence score indicates data completeness before the workflow proceeds, flagging any missing information for review. skyAuth checks the appropriate payer portal to determine whether prior authorization is required, not required, or already decided — eliminating unnecessary submissions and ensuring your team only works on cases that truly need authorization. When authorization is required, skyAuth assembles the complete clinical package — including AI-generated clinical letters, completed payer-specific forms, and supporting documents — and submits directly to the payer. Instant decisions are captured immediately. For pending cases, skyAuth automatically monitors payer portals on a recurring schedule, checking for status updates so your team does not have to log in and check manually. The final outcome — approved, denied, or not required — is captured and written back to your EHR automatically, closing the loop without manual data entry. > **AI at Every Step** — skyAuth uses AI to read clinical charts, extract required data fields, detect whether PA is needed, pre-fill payer-specific forms, generate medical necessity letters, answer payer questionnaires, and upload clinical documents automatically. ## What It Does Orchestrates the complete prior auth workflow: eligibility checks, criteria detection, gathering clinical evidence, form completion, submission to payers, and status monitoring. Uses AI to read charts, extract required fields, prefill payer-specific forms, and generate clinical letters. Supports multi-tenant architectures so large IDNs and groups can run high-volume PA at scale with isolated data and configuration per organization. Bi-directional integration with ModMed (Modernizing Medicine). Reads patient data, clinical notes, and documents from your EHR and writes back authorization decisions and status updates automatically. Real-time dashboard showing status distribution, workflow funnel, daily trends, top denial reasons, high-denial CPT codes, and date range filtering for targeted analysis. View, edit, and manage clinical letters and PA forms directly within the platform. AI-generated medical necessity letters can be reviewed and refined before submission. Duplicate detection prevents redundant PA requests with configurable handling (block, warn, or allow). Confidence scoring flags cases with missing or incomplete information before submission. Create PA tasks on demand by entering a patient MRN and procedure code. Mark any task as completed at any stage. Retry failed submissions with one click. ## Why It's Needed in the Market Prior auth is one of the most painful, labor-intensive workflows in healthcare, contributing heavily to clinician burnout and delayed patient care. Many solutions only digitize forms without truly automating data gathering and reasoning. skyAuth is an **AI-first, agentic PA stack** that connects directly with payer portals and EHR systems to automate data gathering, eligibility checks, submission, and follow-up — unlocking **up to 30% more care capacity** by slashing manual effort in PA and related back-and-forth. ## Key Metrics It Moves Up to 30% more care capacity by reducing time spent on PA. Fewer days from order to decision via automated data prep and submission. Improved completeness and alignment with payer criteria can lower avoidable denials. Fewer staff hours per case through end-to-end automation of data extraction, eligibility checks, submissions, and status monitoring. Automated eligibility checks identify cases where PA is not required or has already been decided, eliminating wasted effort on submissions that were never needed. Dashboard and analytics give leadership immediate insight into PA volume, bottleneck stages, denial patterns, and team workload for data-driven operational decisions. ## Supported Integrations United Healthcare (UHC), CareOregon, PacificSource, Availity clearinghouse, and OneHealthPort. The platform is designed to onboard additional payers as your network grows. ModMed (Modernizing Medicine) with bi-directional integration — read patient data, clinical notes, and documents; write back authorization decisions and status updates automatically. ## Enterprise Ready Supports large health systems, IDNs, and multi-practice groups with isolated data and configuration per organization. Distributed processing handles high-volume PA workloads across multiple payer portals simultaneously. Configurable user roles and permissions to match your organization's structure. Email and SMS alerts keep staff informed of status changes and required actions. Export PA data for reporting, compliance, and integration with other tools. # Source: https://docs.skypoint.ai/docs/skycare-seniorliving skyCare skyCare ## Overview skyCare for Senior Living is a point-of-care AI assistant that overlays senior-living EHRs such as PointClickCare and AugustHealth to surface real-time resident insights—including demographic data, clinical context, and fall-detection events captured by third-party wearables such as SafelyYou and CarePredict. It eliminates the need for care coordinators to switch between multiple platforms during resident interactions by consolidating EHR data + fall data into one unified and actionable view. ## Why It's Needed in the Market Care coordinators in senior living communities operate in highly fragmented environments: * Resident clinical data lives inside EHRs (AugustHealth, PCC, etc.) * Fall-detection data lives inside separate third-party systems (SafelyYou, CarePredict) * There is no native integration between SafelyYou and most senior living EHRs * During a resident interaction, coordinators must toggle between EHR tabs, dashboards, and external portals to understand the context of a fall This creates significant cognitive load, slows down response time, and increases the risk of missing critical fall-event details. **skyCare solves this fragmentation** by showing SafelyYou fall events inside the EHR workflow, powered by Lia, Skypoint's point-of-care AI overlay. This reduces workflow friction and ensures that caregivers have immediate access to fall event data at the bedside—improving safety, awareness, and quality of response. ## What It Does skyCare surfaces fall-event intelligence directly inside senior living EHR workflows: Detects when a care coordinator opens a resident chart. Extracts EHR demographic fields from the active browser DOM. Queries Patient360 (with SafelyYou fall extensions) to locate matching resident data. Displays resident profile + fall history in a clean, actionable sidebar (Lia). **Result:** Care coordinators maintain context, reduce errors, and respond more effectively at the bedside. ## Key Capabilities Overlays Lia onto AugustHealth/PCC without disrupting the EHR. Automatically extracts resident demographics directly from the EHR browser session. Fetches fall events from SkyPoint 360, enriched with SafelyYou data. Displays recent falls in reverse chronological order with severity, category, timestamp, and duration. Shows photo or initials, demographics, room/unit, PCP, insurance, age, DOB, and last incident date. Automatically loads upon navigating to a resident chart. Displays user identity, EHR connections, customer/tenant info, and versioning. Supports logout and future controls. ### Fall Event Integration (SafelyYou) * Fetches fall events from SkyPoint 360, enriched with SafelyYou data * Displays recent falls in reverse chronological order (default filter: last 2 months) * Shows severity, category, timestamp, duration, and source ("SafelyYou") * Future enhancement: display fall images captured by devices ## Key Metrics It Moves Eliminates the need to navigate between EHR + SafelyYou portal. Improves time-to-awareness at point of care. Immediate display of fall history improves decision-making and triage. Reduces delays caused by fragmented system lookups. Better understanding of fall patterns and severity supports proactive care planning. Increased accuracy of incident documentation. Reduces cognitive load. Supports consistent, reliable workflows during high-intensity moments. ## Use Cases Care coordinators see the resident's most recent fall events instantly upon entering the chart—without opening a separate system. Fall history (severity, duration, timestamp) is available during incident documentation workflows. Lia can surface recent trends ("3 falls in last 30 days"), guiding escalation or care-plan updates. Eliminates dependence on SafelyYou's standalone dashboard by embedding the data within the EHR environment. Care coordinators can quickly validate if a newly admitted resident has previous falls recorded in SafelyYou. During an urgent situation, coordinators no longer need multiple system logins; the critical data is in one place. # Source: https://docs.skypoint.ai/docs/skycare-vbc skyCare skyCare ## Overview skyCare for Value-Based Care is an EHR-agnostic, point-of-care AI agent that delivers real-time, patient-specific clinical insights directly inside healthcare workflows. It surfaces Patient 360 data—including demographics, vitals, medications, encounters—and later versions add advanced gap assessment and risk stratification models to help prioritize interventions and close clinical gaps. skyCare for Value-Based Care runs as a lightweight Chrome extension that auto-detects patient context (ID, name, DOB) from any EHR screen and displays consolidated insights with zero clicks. It transforms fragmented value-based care workflows into a unified, intelligent, point-of-care experience. ## Why It's Needed in the Market Value-based care teams—care coordinators, population health managers, case managers, physicians—struggle with fragmented data and manual workflows: * Clinical diagnoses, vitals, encounter notes, and claims data sit in multiple systems * Care gap identification requires manual review of guidelines (HEDIS, USPSTF, CMS Stars) * Risk stratification is inconsistent or absent across care teams * Providers must switch repeatedly between EHR, BI tools, payer portals, and spreadsheets * No real-time prompts exist at the point of care, causing delayed interventions **skyCare for Value-Based Care solves this** by bringing all insights directly to the clinician within the EHR, eliminating lookup time and enabling immediate action. It enhances quality measures, supports proactive care, and strengthens performance under value-based care contracts. ## What It Does Detects patient context (ID, name, DOB) automatically from any EHR view. Fetches consolidated Patient 360 via Skypoint APIs including demographics, vitals, medications, and encounters. Surfaces open care gaps aligned to HEDIS/USPSTF/CMS Stars guidelines. Shows risk scores for prioritizing interventions and targeting high-risk patients. Presents timeline-based clinical activity for rapid orientation during encounters. Supports proactive decision-making by alerting providers during the clinical encounter. **Result:** Reduces the need for manual review across multiple systems and enables immediate clinical action. ## Key Capabilities Demographics, vitals, medications, encounters, claims-derived context—all retrieved from Skypoint's unified Patient 360. Highlights overdue or incomplete screenings, labs, immunizations, chronic disease management tasks based on guideline models. Displays risk scores derived from claims, diagnoses, and encounter history. Auto-detects patient details using DOM parsing—no EHR integration required. Data appears instantly when chart loads, reducing search and navigation time. OAuth2/JWT-secured calls fetch insights from Cosmos DB. High-risk alerts, coding gaps, chronic condition indicators appear inside the extension. Organized views: Agenda, Alerts, Analytics for streamlined navigation. ## Key Metrics It Moves Higher completion of preventive screenings, chronic disease measures, and coding gaps. Rapid surfacing of high-risk members at point of care. Zero-click insights reduce workflow time; Patient 360 loads within \~2 seconds. Supports HEDIS and CMS Stars performance improvement. Better targeting of high-risk individuals increases intervention effectiveness. ## Use Cases Displays all open care gaps the moment the chart loads. Highlights high-risk patients who require immediate focus. Supports diabetes, HTN, COPD programs with relevant vitals and medication cues. Ensures screenings, immunizations, labs, and quality measures are not missed. Providers get full context without leaving the EHR. Enables stratification and intervention planning across patient populations. # Source: https://docs.skypoint.ai/docs/skychat skyChat skyChat ## Overview skyChat is the Secure and Private AI Assistant Built for Senior Living, part of the Skypoint AI for Senior Living 365 Suite. Senior living organizations operate with thousands of policies, SOPs, regulatory requirements, HR processes, and community workflows. Staff often lose valuable time searching through SharePoint, binders, and emails to find the information they need. skyChat delivers fast, accurate, and private AI assistance designed specifically for senior living operators. It connects your organization's knowledge, documents, and data into one secure conversational assistant that every team member can trust. With four specialized AI agents, multiple knowledge sources, built-in analytics, voice interaction, and multilingual support, skyChat helps communities work faster, stay compliant, improve consistency, and elevate resident and family experiences. ## Designed for Senior Living Use Cases Quickly find guidance on state regulations, emergency preparedness, infection control, incident reporting, medication protocols, and documentation requirements. Access move-in/move-out procedures, communication templates, dining standards, transportation schedules, activity planning, and daily checklists. Find floor plans, rate information, competitive talking points, follow-up templates, brand materials, and CRM guidelines instantly. Streamline employee handbook questions, onboarding steps, benefits guidance, training materials, and interview support. ## Why skyChat? | Challenge | skyChat Solution | | :------------------------------------------------------------------ | :---------------------------------------------------------------------------- | | Policies and SOPs scattered across systems | Unified access across all communities and departments | | Staff waste time looking for answers | Instant AI-powered responses with accurate citations | | Generic AI tools charge per user and not suitable for senior living | Agents designed specifically for senior living operations | | Risk of incorrect or unauthorized information | Enterprise-grade security with role-based access | | Slow adoption of complex software without support | Simple chat interface anyone can start using immediately with premium support | | Data scattered across SharePoint and Google Drive | Unified search across multiple knowledge sources with automatic sync | | No visibility into AI adoption and usage | Built-in analytics dashboard with usage metrics, trends, and export | | Inconsistent access controls across teams | Role-based access with granular agent and feature visibility controls | ## AI Agents for Your Teams General AI assistant supporting day-to-day operations, web search, sales, and administrative questions. Features web search capability for up-to-date information and an advanced reasoning mode for complex questions requiring deeper analysis. Searches, analyzes, and extracts insights from policies, emergency plans, SOPs, safety guidelines, HR documents, and compliance files. Provides answers with inline source citations so teams can verify information and trace it back to the original document. Query operational data using natural language including occupancy, census, staffing levels, financial KPIs, and performance metrics. Ask questions in plain English and receive structured data results with configurable display options. Query organizational data using natural language through Microsoft Fabric. Leverages your existing Microsoft credentials for secure, permission-aware data access. Ideal for teams already using Microsoft Fabric for business intelligence and analytics. ## Knowledge Sources & Integration Automatically sync and search your SharePoint libraries. Select specific folders to keep your knowledge base focused and relevant. Incremental sync keeps documents up-to-date automatically as changes are made. Sync and search your Google Drive libraries. Automatically converts Google Docs, Sheets, and Slides into searchable format. Supports organizational shared drives for team-wide knowledge access. Compatible with PDF, Word, Excel, PowerPoint, Google Docs, Google Sheets, Google Slides, and more. Uses keyword, semantic, and vector matching to improve accuracy across large document stores. Answers include source citations for easy verification and traceability. ## Features That Improve Community Productivity ### Real-Time Streaming Responses Teams receive answers immediately as they are generated, enabling faster decision-making. ### Voice-Enabled Conversations Ideal for mobile directors, care teams, and multitasking staff who need hands-free access. Choose from multiple voice options for a personalized interaction experience. Voice mode includes web search capability, admin-configurable voice behavior and guidelines, and visual indicators for thinking, speaking, and searching states. ### Document Upload and Analysis Upload policies, audits, training guides, or compliance binders. skyChat reads, interprets, and answers questions instantly. ### Conversation Management * Bookmark important conversations * Pin conversations for quick access * Share conversations with colleagues via secure links * Search across your full conversation history * Smart suggestions that anticipate your next question * Export conversations for documentation and audits ### Multilingual Support skyChat is available in English, Spanish, and French. The interface adapts to the selected language, making it accessible for diverse teams across your communities. ### Personalization Choose between dark and light themes, select your preferred voice model, and customize chat preferences to match your workflow. ## Admin Control Center Corporate and IT teams maintain full control of the AI environment: **Access & Permissions** | Capability | Description | | :--------------- | :----------------------------------------------------------------------------------------------------------------------- | | User Management | Add users and assign roles across instances, including bulk role assignment for efficient onboarding of large user bases | | Role Management | Create custom roles with specific permissions for each team | | Agent Assignment | Control which AI agents each role can access | | Pipeline Setup | Configure data pipelines with clearly marked required fields to reduce setup errors | **Branding & Customization** | Capability | Description | | :------------------ | :--------------------------------------------------------------------------- | | Appearance | Upload logos (light and dark variants), brand images, and theme colors | | Voice Configuration | Select default voice model, set voice instructions and behavioral guidelines | | Language | Set default language (English, Spanish, or French) | | SEO & Metadata | Configure page title, description, keywords, and favicon | **Knowledge Management** | Capability | Description | | :---------------------- | :--------------------------------------------------------------- | | SharePoint Connectors | Connect and sync SharePoint document libraries | | Google Drive Connectors | Connect and sync Google Drive folders and shared drives | | Sync Management | Monitor sync status, view sync history, and trigger manual syncs | **Insights & Analytics** | Capability | Description | | :---------------------- | :-------------------------------------------------------------------- | | Usage Overview | Track active users, conversations, and message trends over time | | Agent Analytics | See which agents are most used and effective across your organization | | Prompt & Topic Analysis | Understand what teams are asking about to identify knowledge gaps | | Export | Download analytics data for reporting and stakeholder presentations | | Date Range Filtering | View metrics for 7, 30, or 90 day periods | **Platform Administration** | Capability | Description | | :------------- | :-------------------------------------- | | Domain Mapping | Map custom domains to skyChat instances | ## Business Benefits for Senior Living Operators Directors, corporate teams, and staff get accurate answers in seconds. Regulatory and policy information stays accurate, accessible, and consistent. New hires learn faster with instant access to reliable information. Ensure every community follows the same standards and procedures with centralized role-based configuration and uniform agent access. skyChat captures and distributes institutional knowledge across all regions and communities. Track AI adoption, measure agent effectiveness, and identify training opportunities with built-in analytics and exportable reports. ## Help & Support Get assistance directly from within skyChat: * Submit and track support tickets without leaving the application * Categorize requests by type for faster routing * Attach files and screenshots to provide context * Communicate with support through comment threads on each ticket * Track ticket status from open through resolution ## Security You Can Trust Microsoft Entra ID or Google SSO authentication Multi-tenant data isolation for complete security HITRUST r2 and SOC 2 Type 2 certified Users only see the agents and data their role permits. Secure conversation sharing via encoded links. No training on your data — your information stays private. Session timeout protection and automatic token refresh. Built on Microsoft Azure and Google Cloud with enterprise-grade encryption for data in transit and at rest. ## The skyChat Advantage skyChat is purpose-built for senior living organizations that need fast, accurate, and secure AI assistance. With four specialized AI agents, multiple knowledge sources including SharePoint and Google Drive, built-in analytics, role-based access control, voice interaction, multilingual support, and in-app help and support — skyChat is the most complete AI assistant for senior living operations. # Source: https://docs.skypoint.ai/docs/skycommand skyCommand skyCommand ## Overview skyCommand is a centralized, cross-enterprise AI command center that unifies 350+ clinical, financial, operational, and quality KPIs into a real-time, actionable decision layer. It merges predictive analytics with embedded workflows, enabling leaders to not only identify risk but immediately act on insights—launching tasks, interventions, and agentic workflows directly from KPIs. skyCommand brings together five dedicated analytics domains—Occupancy, Marketing, Labor, Care, and Financial—with AI-generated executive briefings, conversational analytics through SkyAgent, multi-model forecasting, and what-if scenario planning. Leaders can move from a portfolio-wide view down to an individual community or resident in seconds, with AI surfacing the insights that matter most. ## Why It's Needed Health systems, senior living operators, and payers often run their organizations on disconnected dashboards, spreadsheets, and vendor-specific reports. Traditional BI tools stop at insight—they do not close the gap between data, decision, and action. Operational teams lose hours navigating multiple systems, replicating work, and manually coordinating interventions. Without predictive capabilities, leaders react to problems instead of preventing them. skyCommand eliminates this fragmentation by combining analytics, AI-driven predictions, governance, and execution in one continuous loop. AI briefings summarize what is happening and why. Forecasting models project what will happen next. Conversational analytics let leaders ask questions and get answers instantly. And built-in alerts, tasks, and initiative tracking ensure that insights translate into measurable action. ## Key Capabilities 350+ KPIs across census, staffing, quality, revenue cycle, hospitalizations, LOS, referrals, and more. Organized across five operational domains — Occupancy, Marketing, Labor, Care, and Financial — with industry benchmarking, historical trends, period-over-period comparisons, and explanatory tooltips on every chart and key metric so stakeholders always know what they're looking at. AI-generated executive briefings analyze KPI trends and deliver actionable recommendations. Multi-model forecasting projects occupancy, staffing, care needs, and financial outcomes up to 365 days ahead. Resident risk scoring and exit prediction identify at-risk residents before issues escalate. Trigger actions such as prior auth, outreach, care-management reviews, or compliance tasks directly from the dashboard. Built-in alert monitoring detects when KPIs hit warning or critical levels. Task management lets teams create, assign, and track follow-up actions with automated reminders. Assign owners, track completion, and standardize operational playbooks. Strategic initiative tracking measures ROI of capital improvements, marketing campaigns, staffing programs, and technology investments with milestone monitoring and progress reporting. Compare performance across communities, markets, service lines, and regions with root-cause analytics. Portfolio-level dashboards aggregate data across all communities. What-if scenario planning lets leaders build and compare alternative strategies side by side. Tailored views for executives, community leaders, clinicians, finance teams, and quality directors. Each role sees the KPIs, dashboards, and insights most relevant to their responsibilities — from portfolio summaries to resident-level analytics across all five domains. ## Features Recommends which issues to address first based on financial, clinical, and operational impact. Resident risk scoring identifies individuals at elevated risk of health deterioration, while exit prediction models flag residents likely to move out within 30, 60, or 90 days. Standardize how teams respond to dips in census, staffing anomalies, or quality issues. Playbooks ensure consistent, repeatable responses supported by configurable alerts that automatically detect when intervention is needed. Move from enterprise to region to facility to resident in seconds. The portfolio-to-community-to-resident hierarchy lets leaders examine individual resident details, care assessments, and risk profiles — all within the same experience. Start workflows in skyAuth, skyAdmissions, skyCare, or skyChat from inside a KPI. When a metric signals a problem, leaders can immediately launch the appropriate workflow without switching systems. Smart reminders, escalations, and initiative scoring to keep teams accountable. Track progress on strategic initiatives with milestone monitoring, expected vs. actual ROI comparisons, and automated status updates. On-demand, AI-generated executive summaries that analyze KPIs, identify trends, surface strategic implications, and recommend specific actions. Available for individual communities or entire portfolios. Leaders can customize briefing focus areas with specific instructions. A conversational AI assistant purpose-built for senior living analytics. Ask questions in natural language and receive instant, data-driven answers. SkyAgent remembers context across conversations and can retrieve real-time metrics on demand. Forecast occupancy, staffing needs, care revenue, marketing leads, and financial outcomes using multiple statistical and machine learning models. Configurable horizons from 30 to 365 days. Health-adjusted forecasting incorporates resident acuity data for more accurate predictions. Build alternative scenarios for occupancy, financial, and staffing decisions. Compare scenarios side by side, calculate the financial impact of each option, and evaluate trade-offs before committing resources. Initiative ROI tracking ties scenario outcomes to real-world results. Set KPI thresholds and receive notifications when metrics hit warning or critical levels. Configure alert rules based on thresholds, trends, anomalies, or forecast deviations. Delivered in real time and via email with customizable severity levels and cooldown periods. Compare assessed levels of care with the care actually being provided to identify gaps. Track reconciliation status across communities, surface residents with mismatched care levels, and analyze the revenue impact of care gaps. ## Use Cases Detect predicted occupancy dips using multi-model forecasting and proactive alerts. Automatically trigger outreach tasks or referral follow-ups. Health-adjusted forecasting accounts for resident acuity alongside market dynamics. Use care reconciliation to identify gaps between assessed and provided levels of care. Surface mismatched care levels, recover missed revenue, and enable proactive intervention with resident risk scoring and exit prediction. Pinpoint residents at elevated risk using predictive risk scoring and exit prediction models. Track care assessment trends, monitor acuity changes, and identify communities with care reconciliation gaps — all from a single dashboard. AI briefings and portfolio dashboards provide a single source of truth for regional and enterprise-level operations. SkyAgent lets leaders ask ad-hoc questions and get instant answers during rounds. Multi-site operators standardize interventions and measure outcomes in one place. Compare community performance with cross-facility benchmarking, build what-if scenarios to evaluate strategic options, and track initiative ROI across the entire portfolio. # Source: https://docs.skypoint.ai/docs/skycomply skyComply skyComply ## Overview skyComply is the Corrective Action and Compliance App built for Senior Living, part of the Skypoint AI for Senior Living 365 Suite. Senior living operators face increasing pressure to stay survey ready, meet evolving regulatory requirements, and maintain consistent documentation across communities. Yet many still manage corrective actions and Plans of Correction with paper logs, spreadsheets, emails, and verbal reminders. skyComply eliminates these risks by delivering automated, evidence-first compliance workflows inside Skypoint Senior Living 365. Corrective actions become guided digital workflows, tasks are routed automatically, and evidence is validated before closure. Leadership gains real-time visibility across every community. The result is predictable execution, faster survey readiness, and confidence that every corrective action is completed accurately and on time. ## The Problem: Manual Compliance Is Breaking Down Senior living operators consistently face the same challenges: * Corrective actions tracked manually in spreadsheets, binders, or email threads * Follow-ups handled informally and based on memory * Documentation scattered across shared drives, paper files, and local storage * Staff turnover causing loss of institutional knowledge and missing evidence * Survey prep becoming a last-minute sprint instead of a controlled process * Corporate and regional leaders operating with limited or outdated visibility This creates a recurring last-mile gap where deficiencies are noted but proof of correction is inconsistent. Licenses, revenue, and reputation remain at risk. ## Key Capabilities Upload citations, inspection reports, survey findings, complaint letters, or internal audits. skyComply automatically reads survey language, identifies cited tags, extracts required corrective actions, and suggests structured Plans of Correction. Historical Insights imports support custom titles for clearer survey identification. Drop in dozens of surveys, audits, or inspection reports at once. End-to-end bulk upload extracts structured compliance insights from many documents in a single pass — ideal for backlog processing or multi-community uploads. Automatically build complete, regulatory-aligned Plans of Correction that follow state expectations and match how inspectors expect to see them. Default tasks are created automatically for each POC to accelerate response times. Compliance teams collaborate live on Plans of Correction with real-time updates to review status, comments, and assignments — no page refreshes needed. Reassignments trigger notifications and emails to keep everyone aligned, and review actions are restricted to the assigned approver to enforce clear ownership. Assign corrective actions based on role, deficiency type, or community without email chains or manual coordination. Leadership sees overdue tasks, stalled items, and systemic issues instantly with full portfolio visibility. Require photos, logs, invoices, training records, or updated policies before an action can be marked complete. All documentation is stored in one secure, searchable location for staff, regional teams, and auditors. Monitor compliance performance, identify trends, and catch repeat deficiencies early across all communities. Ensure consistent corrective action workflows regardless of staffing changes or turnover. When processing uploaded survey reports, skyComply automatically determines the severity level of each deficiency based on regulatory language in the document. Terms like "Immediate Jeopardy" or "Actual Harm" are mapped to normalized levels — Critical, High, Medium, or Low — for consistency across all reports. Receive email notifications for task assignments, overdue tasks, approaching deadlines, evidence reminders, escalations, and stalled items. Choose immediate delivery, daily digest, or weekly digest — and unsubscribe with one click from any email. Tasks created from a Plan of Correction automatically carry the severity level from the source deficiency. Each task shows an AI Extracted Regulatory Data section with the tag code, severity, and violation type pulled directly from the original survey report. Administrators can configure how skyComply interprets severity terminology used in state survey reports. Each mapping links a framework-specific term to a normalized severity level, ensuring accurate and consistent severity assignment during document extraction. ### AI Survey and Citation Ingestion Upload citations, inspection reports, survey findings, complaint letters, or internal audits as PDF or document files. skyComply automatically: * Reads and interprets survey and inspection language * Identifies cited tags, regulatory references, and focus areas * Extracts required corrective actions and timelines * Suggests structured Plans of Correction for review and approval This turns complex survey documents into clear, actionable workflows in minutes instead of days. ## Built for Senior Living Regulations skyComply is aligned with the real frameworks used across senior living — both federal and state-level: **Federal Frameworks** * **CMS F-Tags** — Federal nursing facility survey tags (default framework) * **Emergency Preparedness** — Federal emergency preparedness requirements * **Life Safety Code (LSC)** — Federal life safety code requirements **State Frameworks** | State | Framework | | :--------------- | :---------------------------------------------------- | | **Illinois** | AL/SH Establishment Code (77 Ill. Adm. Code 295) | | **Indiana** | Health Facility Licensure (IC 16-28, 410 IAC 16.2) | | **Iowa** | Assisted Living Programs (481 IAC 69) | | **Michigan** | Adult Foster Care (R 400.601–400.690) | | **Minnesota** | 144G Assisted Living | | **Missouri** | ALF II (19 CSR 30-86) | | **Nebraska** | Assisted Living Facilities (175 NAC 4) | | **North Dakota** | Basic Care Assistance (NDCC 23-09.3, NDAC 33-03-10.1) | | **Ohio** | RCF Licensure (OAC 3701-16, ORC 3721) | | **Pennsylvania** | ALR (55 Pa. Code Ch. 2800) | | **Virginia** | Assisted Living Facilities (22VAC40-73) | | **Wisconsin** | CBRF (Wis. Stats. §50, DHS 83) | Corrective actions are mapped to state-specific tags, evidence is organized in survey-ready formats, and PoCs match how inspectors expect to see them. ## Why Now The urgency to modernize compliance is growing: State agencies are increasing scrutiny and documentation requirements. Staff turnover creates gaps in compliance knowledge and missing evidence. Operators expanding across multiple sites need standardized visibility. Rising litigation pressure makes documentation essential. Skypoint Senior Living 365 provides the foundation. skyComply operationalizes compliance inside that ecosystem. ## ROI and Business Impact ### Immediate Benefits * Administrators save hours each week by removing manual follow-up * Regional and corporate teams streamline survey preparation * Follow-through becomes consistent, reducing repeat deficiencies and fines ### Operational Improvements * Lower regulatory and liability risk through verifiable documentation * Clear portfolio-wide oversight enables proactive management * New administrators onboard faster with centralized compliance history ### Financial Upside * Avoiding a single citation, resurvey, or Stop Placement order pays for skyComply many times over * Reduced administrative burden creates measurable savings across communities **Bottom line:** skyComply gives senior living operators something manual workflows never can: certainty. ## Security You Can Trust Microsoft Entra ID or Google Single Sign-On authentication. Multi-tenant data isolation keeps your data secure and separate. HITRUST r2 and SOC 2 Type 2 certified infrastructure. Role-based permissions at region, community, and team levels. Complete audit logs for compliance and investigations. Built on Microsoft Azure and Google Cloud with enterprise-grade security. ## The skyComply Advantage skyComply is purpose-built for senior living operators who need a consistent, defensible, and modern way to manage compliance across every community. By turning survey documents into guided workflows, centralizing evidence, and delivering real-time oversight, skyComply ensures continuous readiness. **skyComply. Modern compliance for senior living. Predictable execution. Stronger inspections. Lower risk.** # Source: https://docs.skypoint.ai/docs/skydata skyData skyData # skyData ## Overview skyData is a healthcare data integration and operations platform that connects your clinical, financial, and operational systems into a single, governed environment. It handles data import, transformation, export, AI configuration, and pipeline automation — all without requiring engineering work for every new integration. For data unification — matching, merging, and resolving duplicate records into unified profiles — see [skyMDM](/docs/skymdm), which provides the full identity resolution and master data management layer. skyData is the underlying "fabric" that connects 250+ systems and powers all Skypoint agents and apps — handling integration, governance, and data modeling so teams can focus on outcomes, not plumbing. ## What You Can Do with skyData Connect and import data from clinical systems, EHRs, CRMs, data warehouses, cloud storage, and more using Enhanced or Classic connectors. Clean, standardize, and reshape imported data using SQL and dbt-based transformations before it flows downstream. Send data to cloud storage destinations and healthcare interoperability systems like FHIR, SFTP, and BigQuery. Schedule end-to-end data runs — imports, transformations, and exports — on a recurring basis without manual intervention. Configure copilots, document search, and Power BI–backed insights powered by your data. Browse your Bronze, Silver, and Gold data layers and preview records directly in the Lakehouse Explorer. Track data health, pipeline status, and system activity from a central dashboard with real-time updates. Reduces risk vs. bespoke ETL stacks by operating within a HITRUST r2-certified environment. ## What It Does Connects and normalizes data from 250+ systems (EHRs, claims, HR, finance, CRM, marketing, operations) into an AI-ready, governed data layer. Manages FHIR and healthcare data models, security policies, and access controls. Identity resolution and master data management are handled by [skyMDM](/docs/skymdm). Exposes data to analytics (skyCommand), agents (skyAuth, skyAdmissions, skyCare), and custom apps (skyApps, skyAgents) with consistent semantics and lineage. Reduces risk vs. bespoke ETL stacks by operating within a HITRUST r2-certified environment. ## Why It's Needed in the Market Most healthcare AI projects fail because data is siloed, messy, and non-standard. **skyData is the underlying "fabric"** that makes Skypoint's agentic apps possible — handling integration, governance, and data modeling so teams can focus on outcomes, not plumbing. It also reduces risk vs. bespoke ETL stacks by operating within a **HITRUST r2-certified environment**. ## Key Metrics It Moves Compresses projects from months to weeks by reusing a unified data layer. Fewer bespoke integrations and pipelines to maintain. More consistent longitudinal records across care settings. # Source: https://docs.skypoint.ai/docs/skyhire skyHire skyHire ## Overview skyHire is the AI-Powered Recruiting, Hiring & Applicant Tracking System built for healthcare organizations, part of skyWorkforce by Skypoint AI. Healthcare is facing a workforce crisis. With 45+ days average time-to-hire, 60% of applicants abandoning lengthy processes, and 80% of recruiter time spent on admin tasks, legacy ATS platforms have made hiring digital—but not faster. They're databases that require constant human intervention. skyHire changes that. One system. One candidate record. Zero handoffs. From job posting to first day on the floor, skyHire automates the entire hiring workflow so your team can focus on what matters: finding and keeping great talent. ## Why skyHire Delivers Better Outcomes AI handles screening, scheduling, and communication. Recruiters focus on decisions, not data entry. Designed for regulated environments with credential-dependent hiring, compliance requirements, and high-volume needs. Agentic AI that executes recruiting tasks autonomously. Not a chatbot—a teammate that works. Recruiting and onboarding in one platform. No handoffs, no gaps, no delays. ## Key Capabilities ### skyAgent: AI That Does the Work Not a chatbot. AI that executes recruiting tasks autonomously. Assign tasks to skyAgent like you would a teammate—screening, scheduling, follow-ups. It gets done. | Task | What skyAgent Does | | :---------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Screening** | Screen all applicants for top fit. AI-powered resume analysis with candidate scoring and summaries — supports PDF, DOCX, and DOC formats with no manual conversion | | **Scheduling** | Schedule interviews for the best candidates. Send invitations and confirmations automatically | | **Follow-ups** | Follow up with hiring managers who haven't submitted feedback | | **Communication** | Send personalized outreach emails to candidates — individually or in bulk | | **Analysis** | Identify bottlenecks in your hiring pipeline with funnel analytics | | **Resume Review** | Analyze resumes and generate AI summaries highlighting key qualifications | | **Pipeline Management** | Move candidates through pipeline stages. Bulk status updates across applicants | | **Offer Tracking** | View and manage job offers by status — draft, sent, accepted, declined | | **Reporting** | Generate presentation-ready reports with recruiting data and pipeline metrics | | **Context Memory** | Remembers your previous conversations for faster, more relevant assistance | skyAgent provides full reasoning for complex scenarios, tool use across systems, multi-step task execution, and context awareness—with human oversight for sensitive decisions. ### skyJobs: Career Pages That Convert Branded, mobile-first career pages that convert candidates: * **Multiple Career Portals** — Create and manage separate branded career sites for different locations or brands * **Custom Domains** — Use your own domain name for a seamless candidate experience * **Full Brand Customization** — Match your brand with custom colors, logos, favicon, hero images, and fonts * **Mobile-First Design** — Optimized for conversion on any device * **Multi-Language Support** — Serve career pages in multiple languages to reach diverse candidates * **SEO Optimized** — Built-in meta tags, titles, descriptions, and keywords for search visibility * **Application Source Tracking** — Know exactly which portal or job board each candidate came from * **Portal-Specific Jobs** — Assign different jobs to different career portals based on location or brand ### Recruiting Automation End-to-end workflow automation from application to offer: * **Multi-channel job distribution** across job boards * **Automated applicant screening** with AI-powered ranking * **Customizable pipelines** for different roles and locations * **Interview scheduling** with calendar coordination * **Hiring manager collaboration** with feedback collection * **Offer management** with automated generation * **Real-time analytics** for pipeline visibility * **AI-Generated Screening Questions** — Automatically generate relevant screening questions for each job posting * **Customizable Application Forms** — Configure which fields are visible and required for each job, including custom fields * **File Attachments in Applications** — Candidates can upload certifications, portfolios, or other files with their application * **Dynamic Consent Management** — Set up job-specific consent agreements (privacy policy, data retention, SMS) with required vs. optional options * **Application Source Filtering** — Filter and view applicants by their source — career portal, Indeed, LinkedIn, or other channels * **Bulk Applicant Actions** — Batch screening, bulk status updates, and multi-select operations for high-volume hiring * **Resume Digest** — Automated email digests summarizing new applicants with AI-powered scoring * **Interview Types** — Support for phone screen, video, on-site, panel, and technical interviews * **Interview Feedback** — Structured feedback forms with scoring for each interviewer * **Interviewer Assignment** — Route candidates to specific hiring team members * **Video Cover Letter Reupload** — HR can request a fresh video cover letter via email or SMS, with auto-resume capability and automatic re-analysis on reupload * **External Client Collaboration** — Invite outside partners (agencies, recruiting firms, hiring consultants) as External Clients with restricted access scoped to specific applicants and jobs. Permanent deletion of client accounts is reserved for super administrators, and disabled clients can be re-enabled or permanently removed from the client portal ### Onboarding From accepted offer to floor-ready — fast. | Feature | Description | | :----------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------- | | **Digital Onboarding Packets** | Bundle offer letters, compliance documents, and tasks into a single packet sent to new hires. Track packet status from sent through completion | | **E-Signatures** | Legally binding electronic signatures on all documents with automatic records of who signed and when | | **Document Collection** | Collect I-9s, W-4s, direct deposit forms, and custom documents with automatic reminders for incomplete items | | **Offer Letter Templates** | Create reusable offer letter templates with dynamic fields that auto-fill candidate and job details | | **Approval Workflows** | Multi-level offer approval process — draft, pending approval, approved, and sent — with status tracking | | **Policy Acknowledgment** | Track which policies each hire has reviewed and accepted | | **Completion Analytics** | Dashboard showing total packets, completion rates, average completion time, and document signing rates | | **Packet Expiration** | Set expiration dates on onboarding packets to keep the process moving | | **HRIS & Payroll Integration** | Push data automatically to your existing systems | ### Candidate Portal A self-service portal where candidates manage their own hiring journey. | Feature | Description | | :------------------------- | :------------------------------------------------------------------------------------------------------------------ | | **Self-Service Dashboard** | Candidates track their own applications, view status updates, and see next steps — without contacting the recruiter | | **Easy Registration** | Simple signup to access the portal with downloadable credentials | | **Application Tracking** | Candidates see where they are in the hiring pipeline in real time | | **Interview Management** | View and confirm scheduled interviews directly from the portal | | **Onboarding Access** | Complete onboarding packets, sign documents, and upload required files — all from within the portal | | **Mobile-Friendly** | Fully responsive design that works on any device | ## The Complete Candidate Journey | Stage | What Happens | | :------------ | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Attract** | Publish to branded career portals, Indeed, LinkedIn, and job boards. SEO-optimized listings with multi-language support | | **Apply** | Candidates apply with customizable forms, AI-generated screening questions, file attachments, and job-specific consent agreements. Self-service via the Candidate Portal | | **Screen** | AI-powered resume analysis, candidate scoring, and bulk screening. Filter by source, status, or pipeline stage | | **Interview** | Schedule phone, video, on-site, panel, or technical interviews. Microsoft Teams and Google Calendar integration. Collect structured feedback with scoring | | **Offer** | Generate offers from templates with approval workflows. Track offer status from draft to accepted | | **Onboard** | Digital packets with e-signatures, document collection, compliance tracking, and completion analytics. Candidates complete from their self-service portal | ## Business Impact Modern architecture delivers enterprise-grade capability without enterprise pricing. Automation eliminates manual bottlenecks that slow down hiring. Mobile-first applications and timely communication reduce abandonment. Live in days, not months. Get hiring faster with minimal setup. ## Who Benefits Handle constant hiring needs for CNAs, caregivers, and nursing staff with credential awareness built in. Manage high-volume, compliance-intensive hiring with audit trails and regulatory reporting. Recruit and onboard distributed workforces across multiple service areas. Centralized visibility with location-level control. Shared talent pools across properties. Track application sources across portals. Manage separate branded career sites per location. ## Part of skyWorkforce skyHire is part of skyWorkforce—the complete workforce management suite. Start with skyHire and add what you need: | Product | Purpose | | :-------------- | :--------------------------------------------------- | | **skyHire** | Recruiting, applicant tracking, and onboarding | | **skyLearn** | AI-powered learning management and certification | | **skyVerify** | License, credential, and certificate verification | | **skySchedule** | Shift scheduling, labor pools, and agency management | ## Platform Integration skyHire connects with 150+ systems through Skypoint's composable platform: * **Indeed** — Automatic job posting and applicant sync via feed integration * **LinkedIn** — Job distribution and candidate sourcing * **Google Calendar** — Interview scheduling and event synchronization * **Microsoft Teams & Outlook** — Meeting creation and calendar coordination * **WelcomeHome CRM** — Senior living-specific CRM integration for applicant and interview management * **skyData** — Unified data foundation and workflow orchestration * **skyMDM** — Master data for accurate employee records * **HRIS Systems** — Bidirectional sync with existing HR platforms * **Payroll** — Automatic data push for new hires * **Background Checks** — Integrated screening workflows ## Get Started skyHire is available as part of the Skypoint platform. Contact your Skypoint representative to see how skyHire can help you hire faster, onboard smarter, and build the workforce your organization needs. # Source: https://docs.skypoint.ai/docs/skyinsights skyInsights skyInsights ## Overview skyInsights delivers real-time financial and operational analytics integrated into Power BI. It transforms complex healthcare data into actionable insights that drive better decision-making across your organization. ## Key Capabilities Seamless integration with Microsoft Power BI Live data updates for timely decision-making Tailored views for different roles and needs ## Features ### Pre-Built Healthcare Dashboards Access ready-to-use dashboards designed specifically for healthcare operations, finance, and clinical analytics. ### Real-Time Data Refresh Connect to live data sources for up-to-the-minute insights without manual data pulls. ### Role-Based Views Provide each user with relevant insights based on their role, from executives to department managers. ### Advanced Analytics Leverage AI-powered analytics for trend analysis, forecasting, and anomaly detection. ## Use Cases * **Financial Performance**: Track revenue, costs, and profitability in real time * **Operational Efficiency**: Monitor census, staffing, and utilization metrics * **Clinical Quality**: Analyze quality measures and outcomes data * **Executive Reporting**: Deliver board-ready reports and dashboards # Source: https://docs.skypoint.ai/docs/skylearn skyLearn skyLearn ## Overview skyLearn is the AI-powered learning management system built for healthcare operators, part of the Skypoint AI skyWorkforce Suite. Healthcare operators face a relentless training burden: high staff turnover means constant re-onboarding, state and federal regulations demand up-to-date certifications, and most organizations are running a patchwork of disconnected tools — a legacy LMS here, a spreadsheet tracker there, a manual email workflow for certificates. The result is compliance risk, costly admin overhead, and learners who fall through the cracks. Accreditation deadlines get missed. Audits become fire drills. skyLearn replaces that patchwork with a single, automation-first platform. It manages the complete training lifecycle — from course creation to certification issuance — and uses AI to handle the operational work that teams currently do by hand: scheduling reminders, tracking completions, sending follow-ups, and surfacing compliance gaps before they become problems. ## Why skyLearn Delivers Better Outcomes Legacy LMS platforms track what happened. skyLearn acts on what needs to happen next. AI handles content delivery, assessment follow-ups, re-enrollment triggers, and compliance alerts — without manual intervention from learning managers. Learning and certification live in the same platform. There is no export-to-spreadsheet step, no separate credentialing tool, and no manual handoff between training completion and certificate issuance. Completion triggers certification automatically. skyLearn is HITRUST r2 certified and designed for the compliance demands of healthcare operations. Audit-ready records, expiration monitoring, and credential tracking are built into the core — not bolted on as an add-on module. Enterprise-grade capabilities without enterprise-grade pricing. Multi-tenant architecture and cloud-native infrastructure mean organizations get full LMS functionality, AI-powered operations, and certification management at a fraction of the cost of traditional platforms. ## Key Capabilities Create structured courses with units, prerequisites, and categories. Compose multi-course learning paths with enforced sequencing. AI-assisted course generation can draft content, quizzes, and assessments from a prompt — cutting authoring time significantly. skyAgent is agentic AI that autonomously executes learning operations: schedules training, sends personalized learner communications, evaluates competencies, follows up with managers, and surfaces knowledge gaps. Every action is gated for human approval before execution. Manage learner enrollment directly, through group assignments, or via automation rules. Track unit-level progress, quiz scores, and completion status in real time. Completion events automatically trigger downstream workflows — notifications, gamification updates, and certificate issuance. Issue digital certificates upon course or path completion, with configurable templates and e-signature support. Track expiration dates, automate renewal reminders, and maintain audit-ready records of every credential issued — across all locations and learner types. Schedule and manage instructor-led sessions with capacity controls, timezone support, and learner registration. Record attendance and tie ILT outcomes to overall training completion and certification workflows. Build quizzes, assignments, and competency assessments with configurable scoring. Collect learner feedback through LMS surveys. Grading workflows support reviewer queues, manual scoring, and automated pass/fail outcomes. Drive learner engagement with points, badges, levels, and leaderboards. Group-based cohorts and discussion threads foster peer learning. Automated re-engagement nudges keep learners on track without manual follow-up from managers. Monitor completion rates, average scores, certification status, and learning activity across every location and cohort. Export enrollment time series, top-course stats, and group-level rollups. Draft courses are included in report builder queries for comprehensive reporting. Compliance stakeholders get the audit trail; operations leaders get the KPIs. Generate course imagery and voiceover scripts with improved image quality, stronger topic relevance, and validated voiceover content — ready to drop into lessons without manual production work. The course player keeps learners in-experience by preventing documents from opening in new tabs, ensuring accurate progress tracking. Certificate issuance requires explicit course selection to prevent incomplete records. An admin API enables renaming of tenant and instance names without manual intervention, simplifying administration for growing organizations and multi-brand operators. ## Business Impact Automated workflows from enrollment to certificate issuance eliminate the manual steps that delay credentialing. New staff reach compliance-ready status faster, reducing operational risk during onboarding. Expiration monitoring, automated renewal reminders, and audit-ready records mean organizations are always prepared for inspections and accreditation reviews — without scrambling at deadline time. AI handles scheduling, learner communications, re-engagement, and reporting. Learning managers focus on content and outcomes — not chasing completions, sending reminder emails, or manually issuing certificates. Automated nudges, personalized progress updates, and group-based accountability keep learners engaged and on track. skyAgent identifies at-risk learners and intervenes before they fall behind. ## Who Benefits Build and publish courses faster with AI-assisted authoring. Assign training to individuals or entire cohorts. Monitor completion and certification status across all locations from a single dashboard — without maintaining spreadsheets. Access assigned training through a mobile-friendly portal, available 24/7. Track personal progress, view earned certificates, and get timely reminders — all in one place, without navigating multiple systems. Get real-time visibility into certification validity, expiration timelines, and completion rates across the organization. Audit-ready records and exportable reports make regulatory reviews straightforward. Tie training completion directly to onboarding milestones and workforce readiness. Group-based assignments align training delivery to locations, roles, and departments without requiring manual coordination for each new hire. ## Platform Integration skyLearn is part of Skypoint's composable platform, connecting seamlessly with: * **skyData** — Skypoint's unified data foundation with 250+ clinical, operational, and financial integrations (EHR, HRIS, payroll, and learning platforms). skyLearn inherits enterprise-grade data connectivity, analytics, and governance from skyData without requiring separate integration work. * **skyVerify** — Credential and license verification with ongoing monitoring and expiration alerts. skyLearn handles internal training certifications; skyVerify handles external professional credentials. Together they give operators a complete picture of workforce compliance. * **skySchedule** — Shift scheduling and labor pool management. Training completion and certification status from skyLearn inform scheduling eligibility in skySchedule, ensuring only qualified and credentialed staff are placed in compliant shifts. * **skyAgent** — Agentic AI operations layer shared across the skyWorkforce suite. skyAgent executes learning workflows — scheduling, communications, follow-ups — using the same agent framework that powers operations across the broader platform. ## Get Started skyLearn is available as part of the Skypoint platform. Contact your Skypoint representative to configure skyLearn for your organization's healthcare workforce training and certification needs. # Source: https://docs.skypoint.ai/docs/skymdm skyMDM skyMDM ## Overview skyMDM (Master Data Management) provides AI-driven match, merge, dedupe, and real-time 360° data capabilities. It creates a single source of truth across your healthcare organization by resolving identities and maintaining accurate, connected records. ## Key Capabilities Intelligent algorithms that identify and link related records across systems Automatically consolidate duplicate records while preserving data integrity Unified, up-to-date view of patients, providers, and other entities ## Features ### Identity Resolution Use machine learning to match and link records across disparate systems, creating accurate golden records even when data quality varies. ### Survivorship Rules Define customizable rules to determine which data values survive the merge process, ensuring your golden records contain the most accurate information. ### Real-Time Updates Keep your master data synchronized in real-time as source systems change, ensuring downstream applications always have current information. ### Data Lineage Track the origin and transformation of every data point, maintaining full auditability for compliance requirements. ### Role-Based Access Control (RBAC) Enforce fine-grained user roles across the application with workflows aligned to the rest of the Skypoint suite. Viewer access extends into Platform Settings for role-appropriate visibility, while administrators retain full control over Profiles, Merge configuration, and the Review Queue. ### Standardized Match & Merge Experience Match and Merge workflows are driven from clean action menus instead of inline Run buttons, with a grid-based pipeline-stage table active by default for clearer at-a-glance review. Job status, progress, and outcomes are surfaced consistently across communities. ### Operational Diagnostics Searchable correlation IDs and end-to-end telemetry across data catalog operations make it fast to trace, reproduce, and resolve issues. Diagnostic messages surface actionable error details so data stewards can act without escalating to engineering. ### Help & Support An always-on Help & Support page lets data stewards submit, track, and resolve issues directly inside skyMDM without leaving the workflow. ### Branding & UI Consistency Branded login and splash screens, refined dashboard palette, and consistent primary call-to-action styling deliver a polished, on-brand experience for every user across pipelines, dashboards, and Match/Merge jobs. ## Use Cases * **Patient Matching**: Create accurate patient identities across EHRs, claims, and operational systems * **Provider Data Management**: Maintain accurate provider directories and credentialing information * **Member 360**: Build complete views of health plan members across enrollment, claims, and engagement data * **Facility Management**: Consolidate facility and location data across multiple systems # Source: https://docs.skypoint.ai/docs/skymdm-clean # Clean Transform raw data into reliable, standardized information ready for matching and analysis. The Clean module applies intelligent data cleansing rules that fix inconsistencies, standardize formats, and remove duplicates at scale. ## Why Data Cleaning Matters Healthcare data comes from many sources, each with its own formats, conventions, and quality issues. Patient names might be all uppercase in one system and mixed case in another. Phone numbers might include dashes, parentheses, or no formatting at all. These inconsistencies create problems: * Duplicate patient records that fragment care history * Failed matching that misses related records * Inaccurate analytics and reporting * Compliance risks from poor data quality skyMDM's Clean module addresses these challenges with rule-based, scalable data transformation. ## What You Can Do Configure rules to trim whitespace, standardize case, format phone numbers, and more. See exactly how your data will change before committing transformations. Apply multiple cleaning operations in sequence for comprehensive data standardization. Monitor cleaning jobs in real-time with progress updates and detailed logs. ## Cleaning Operations skyMDM supports a comprehensive set of cleaning operations: Trim whitespace, convert case (upper, lower, title), remove special characters Standardize phone numbers, dates, and addresses to consistent formats Replace specific values, handle nulls, and apply conditional transformations Remove duplicate records based on configurable matching criteria Validate and standardize email address formats Define custom transformation logic for organization-specific requirements ## Key Capabilities ### Rule-Based Configuration Define cleaning rules visually without writing code. Select columns, choose operations, and set parameters through an intuitive interface. ### Scalable Processing Powered by Databricks, cleaning jobs process millions of records efficiently. Large datasets are handled with optimized Spark transformations. ### Version History Every cleaning rule change is tracked with full version history. Roll back to previous configurations when needed. ### Column Profiling After cleaning completes, skyMDM automatically profiles your data—showing null counts, unique values, and data types for each column. ## Business Impact Standardized data dramatically improves identity matching accuracy. Automate repetitive data cleanup that would take analysts weeks. Clean data produces reliable reports and insights for decision-making. ## Who Benefits * **Data Stewards**: Maintain data quality standards across the organization * **Clinical Teams**: Access accurate patient information for better care decisions * **Revenue Cycle**: Reduce claim denials caused by data quality issues * **Compliance Officers**: Meet regulatory requirements for data accuracy # Source: https://docs.skypoint.ai/docs/skymdm-connect # Connect Establish secure connections to your data sources and create pipelines that unify your healthcare data. Connect brings together clinical, operational, and financial systems into a single platform for Master Data Management. ## Why Connect Matters Healthcare organizations operate with data spread across dozens of systems—EHRs, claims platforms, scheduling tools, and more. Without a unified connection layer, you're left with fragmented views of patients, providers, and operations. skyMDM's Connect module solves this by: * Providing secure, managed connections to your data warehouses * Supporting enterprise platforms like Databricks, Microsoft Fabric, and Snowflake * Enabling real-time data pipelines that keep your master data current ## What You Can Do Build data pipelines that connect your source systems to the MDM platform with configurable data flows. Securely store and manage credentials for Databricks, Fabric, Snowflake, and other data platforms. Browse your data catalogs and select the tables you want to include in your MDM process. Track pipeline status, execution history, and data freshness from a central dashboard. ## Key Capabilities ### Multi-Platform Support Connect to leading data platforms including Databricks Unity Catalog, Microsoft Fabric Lakehouse, and Snowflake. Each connection type is optimized for its platform's unique capabilities. ### Secure Credential Management Credentials are encrypted and stored securely in your tenant's isolated database. Connection testing validates access before pipelines run. ### Pipeline Orchestration Create pipelines that define the complete data journey—from source selection through cleaning, validation, matching, and merging. ### Catalog Discovery Automatically discover available catalogs, schemas, and tables from your connected data sources. No manual configuration required. ## Business Impact Connect new data sources in minutes instead of weeks with pre-built connectors. Secure credential management and tenant isolation protect sensitive data. Bring all your data sources together for a complete picture of your operations. ## Who Benefits * **Data Engineers**: Quickly set up and manage connections without complex infrastructure * **IT Administrators**: Maintain security and compliance with centralized credential management * **Analytics Teams**: Access unified data from multiple sources for comprehensive reporting * **Operations Leaders**: Gain visibility into data flowing across the organization # Source: https://docs.skypoint.ai/docs/skymdm-enrich # Enrich Enhance your data with additional context and information from external sources and lookup tables. The Enrich module adds valuable attributes that improve matching accuracy and provide richer insights. ## Why Enrichment Matters Your source data often contains only what was captured at the point of entry. But effective Master Data Management requires additional context: * Geographic data from ZIP codes (city, state, county, timezone) * Demographic insights for population health * Standardized codes and classifications * Reference data from authoritative sources skyMDM's Enrich module fills these gaps automatically, transforming basic data into comprehensive, analysis-ready records. ## What You Can Do Expand ZIP codes into full geographic context including city, state, county, and timezone. Match your data against reference tables to add standardized codes and categories. Calculate new fields based on existing data—ages from birth dates, categories from values. Connect to external data sources to enrich records with third-party information. ## Enrichment Sources Comprehensive US ZIP code data with demographics, coordinates, and regional information Identify carrier, line type (mobile/landline), and geographic origin USPS-standardized addresses with delivery point validation ICD, CPT, LOINC, and other healthcare code crosswalks and descriptions Your organization's lookup tables and reference data Connect to external enrichment services for specialized data ## Key Capabilities ### Automated Enrichment Rules Configure which source columns should be enriched and what additional data to append. Rules apply automatically during pipeline execution. ### Confidence Scoring Enrichment matches include confidence scores, so you know how reliable each enriched value is. ### Incremental Processing Only new or changed records are enriched, minimizing processing time and costs for large datasets. ### Audit Trail Track which records were enriched, what sources were used, and when enrichment occurred for compliance and debugging. ## Business Impact Enriched data provides more attributes for accurate identity resolution. Geographic and demographic data enables population health insights. Automated enrichment eliminates manual data lookup and entry. ## Who Benefits * **Population Health Teams**: Analyze patient populations with geographic and demographic context * **Marketing Teams**: Target outreach based on enriched demographic data * **Analytics Teams**: Build more insightful reports with complete data * **Clinical Research**: Stratify patient populations for research and quality initiatives # Source: https://docs.skypoint.ai/docs/skymdm-map # Map Apply semantic labels to your data columns to create a common language across all your source systems. The Map module transforms disparate column names into standardized semantic terms that enable accurate matching and unified reporting. ## Why Semantic Mapping Matters Every system has its own naming conventions. One system calls it "PatientFirstName," another calls it "first\_nm," and a third uses "fname." Without a common vocabulary, these systems can't communicate effectively. Semantic mapping solves this by: * Creating a unified vocabulary across all data sources * Enabling accurate matching by aligning equivalent fields * Supporting consistent reporting and analytics * Preserving the relationship between source and canonical data skyMDM's Map module provides an intuitive interface for assigning semantic labels to your source columns. ## What You Can Do Map source columns to standardized labels like FirstName, LastName, DateOfBirth, SSN. Designate which column uniquely identifies records in each source table. Drag and drop labels onto columns with an intuitive visual interface. Apply labels to multiple columns at once for faster configuration. ## Semantic Label Categories FirstName, LastName, MiddleName, Suffix, DateOfBirth, SSN, MRN Email, Phone, MobilePhone, Address, City, State, ZipCode Gender, Race, Ethnicity, Language, MaritalStatus DiagnosisCode, ProcedureCode, ProviderNPI, FacilityID InsuranceID, MemberID, GroupNumber, PayerName Organization-specific labels for unique data elements ## Key Capabilities ### Intelligent Suggestions skyMDM analyzes column names and sample data to suggest likely semantic labels. Columns named "dob" or "birth\_date" are automatically suggested for DateOfBirth. ### Label Validation The system validates that mapped labels are compatible with the underlying data types and provides warnings for potential mismatches. ### Cross-Source Alignment View how labels are mapped across all your source tables to ensure consistency and identify gaps in your semantic model. ### Version Control Mapping configurations are versioned, allowing you to track changes and roll back if needed. ## Business Impact Semantic alignment ensures matching algorithms compare the right fields. Consistent labels enable cross-source analytics and reporting. New data sources are integrated faster with standardized mapping. ## Who Benefits * **Data Architects**: Design and maintain the enterprise semantic model * **Data Engineers**: Quickly map new data sources to existing standards * **Analytics Teams**: Query across sources using consistent field names * **Integration Teams**: Reduce mapping errors when connecting systems # Source: https://docs.skypoint.ai/docs/skymdm-match # Match Identify related records across your data sources using intelligent matching algorithms. The Match module finds duplicates within systems and links records across systems to build a complete view of each entity. ## Why Matching Matters Organizations typically have the same people, accounts, or entities represented in multiple systems. Without matching: * Records are fragmented across operational, billing, and administrative systems * Duplicate records create confusion and operational gaps * Analytics undercount or overcount unique individuals * Teams lack the complete information needed for effective coordination skyMDM's Match module uses advanced algorithms to accurately identify when two records represent the same real-world entity. ## What You Can Do Define which fields to compare and how much weight each field carries in match scoring. Select from exact matching or fuzzy matching approaches based on your data quality needs. Examine potential matches with detailed comparison views before accepting. Configure score thresholds for auto-match, manual review, and non-match decisions. ## Matching Approaches Match records on exact field values like SSN, account numbers, or unique identifiers. Best for high-confidence identifiers. Handle variations in names, addresses, and other fields using similarity algorithms. Best for real-world data with typos and inconsistencies. ## Match Attributes Handle nicknames, misspellings, name changes, and cultural naming conventions Match despite formatting differences, abbreviations, and address changes Exact matching on SSN, account IDs, and unique identifiers with handling for typos Match dates across different formats with tolerance for data entry errors Match contact information with normalization and validation Include any field in your matching strategy based on your data ## Key Capabilities ### Match Scoring Every potential match receives a confidence score based on how well records align across configured fields. Higher scores indicate stronger matches. ### Transitive Matching If Record A matches Record B, and Record B matches Record C, skyMDM recognizes that all three may represent the same entity. ### Block and Compare Efficiently process large datasets by first blocking records into candidate groups, then running detailed comparisons only within blocks. ### Match Audit Trail Every match decision is logged with the score, contributing fields, and timestamp for compliance and quality review. ## Business Impact Link records across systems for a comprehensive view of each individual or entity. Know exactly how many unique individuals or accounts you serve. Connect teams with complete information for better decision-making. ## Who Benefits * **Operations Teams**: Access complete records for planning and coordination * **Quality Teams**: Accurate attribution for quality measures and reporting * **Finance Teams**: Proper counting for revenue and cost analysis * **Compliance Teams**: Maintain accurate records for regulatory reporting # Source: https://docs.skypoint.ai/docs/skymdm-merge # Merge Create golden records by intelligently combining matched records into a single, authoritative view. The Merge module applies survivorship rules to determine which values become the master record, producing the trusted data your organization needs. ## Why Merging Matters After matching identifies related records, you need to combine them into a single, reliable golden record. But which values should survive? * Should the most recent address win, or the most complete? * What if one system has the SSN and another has the email? * How do you handle conflicting values for the same field? skyMDM's Merge module answers these questions with configurable survivorship rules that produce consistent, trustworthy master records. ## What You Can Do Configure which source values survive the merge based on recency, completeness, source priority, or custom logic. See exactly how records will be merged before committing changes. Define how to resolve conflicting values—take most recent, most complete, or flag for review. Track which source records contributed to each golden record for full traceability. ## Survivorship Strategies Values from the most recently updated source record win Records with the most populated fields contribute values Define a hierarchy of trusted sources—EHR beats claims beats scheduling Useful for names and addresses where more detail is better When multiple sources agree, that value wins Define field-specific logic for unique business requirements ## Key Capabilities ### Field-Level Survivorship Configure different survivorship strategies for different fields. Use recency for addresses, source priority for clinical data, and completeness for demographics. ### Golden Record Creation Produce a single master record for each matched entity with the best available values from all contributing sources. ### Link Preservation Maintain bidirectional links between golden records and their source records. Always know where data came from. ### Real-Time 360° Views Access the complete, merged view of any entity instantly through the platform or via API. ### Delta Processing Only re-merge records affected by new or changed source data, keeping golden records current without full reprocessing. ## Business Impact One authoritative record for each patient, provider, or member. Trustworthy data enables confident business and clinical decisions. Eliminate time spent reconciling conflicting data across systems. ## Who Benefits * **Clinical Teams**: Access complete, accurate patient records for care delivery * **Executive Leadership**: Trust the data behind dashboards and reports * **IT Teams**: Provide a single API endpoint for master data across the organization * **Compliance Teams**: Demonstrate data accuracy and lineage for audits * **Analytics Teams**: Build reliable models and reports on trusted golden records ## The MDM Journey Complete Merge represents the culmination of the skyMDM pipeline: 1. **Connect** - Bring data together from disparate sources 2. **Clean** - Standardize and correct data quality issues 3. **Validate** - Ensure data meets quality thresholds 4. **Enrich** - Add context and additional attributes 5. **Map** - Apply semantic labels for consistency 6. **Match** - Identify related records across sources 7. **Merge** - Create authoritative golden records The result is a unified, trustworthy view of your healthcare data—ready to power better decisions, better care, and better outcomes. # Source: https://docs.skypoint.ai/docs/skymdm-validate # Validate Ensure your data meets quality standards before it flows into downstream systems. The Validate module applies intelligent validation rules to detect invalid emails, malformed phone numbers, incorrect dates, and other data quality issues. ## Why Validation Matters Clean data isn't necessarily valid data. A phone number might be properly formatted but contain an invalid area code. An email might look correct but use an impossible domain. A date might be formatted correctly but represent an impossible value. Validation catches these issues before they cause problems: * Invalid contact information leads to failed patient outreach * Incorrect dates create scheduling conflicts and compliance issues * Bad data propagates through systems, compounding errors skyMDM's Validate module provides comprehensive data validation at scale. ## What You Can Do skyMDM automatically suggests validation types based on column names and data patterns. Apply email, phone, date, ZIP code, and custom validations to your columns. See the percentage of valid records for each column and validation type. Monitor how validation scores change over time as data quality improves. ## Validation Types Verify email format, domain validity, and deliverability indicators Check phone number format, valid area codes, and number type (mobile, landline) Validate date formats, reasonable ranges, and logical consistency Verify ZIP codes exist and match expected geographic regions Parse and validate address components for deliverability Define regex patterns for organization-specific validation needs ## Key Capabilities ### Intelligent Column Detection skyMDM analyzes column names and sample data to automatically suggest appropriate validation types. Columns named "email" or "phone" are detected and pre-configured. ### Validation Statistics After validation runs, detailed statistics show exactly how your data performed—valid counts, invalid counts, null counts, and percentage breakdowns. ### Column Profiling Integration Validation builds on the column metadata from cleaning, providing a complete picture of data quality from basic statistics to semantic validation. ### Scalable Execution Validation jobs run on Databricks, handling millions of records with efficient parallel processing. ## Business Impact Validated contact information increases successful patient communications. Catch data issues before they impact clinical or operational workflows. Stakeholders trust data backed by validation metrics and quality scores. ## Who Benefits * **Patient Access Teams**: Ensure contact information is valid for appointment reminders and outreach * **Marketing Teams**: Improve campaign effectiveness with validated email and phone data * **Quality Assurance**: Track and report on data quality metrics across the organization * **Data Engineers**: Identify data quality issues at the source for upstream fixes # Source: https://docs.skypoint.ai/docs/skymsp skyMSP skyMSP ## Overview skyMSP is Skypoint's HITRUST r2 certified managed cloud — the compliant infrastructure that every Skypoint AI agent and application runs on, and the same platform our customers use to build, deploy, and operate their own healthcare AI. skyMSP gives developers the infrastructure, integrations, and security they need to build custom healthcare AI — and ships with **skyInfra™** and **skyOps™**, pre-built agentic apps that automate infrastructure management and IT operations from day one. Less overhead. More shipping. Building custom healthcare AI without skyMSP means solving compliance, integrations, and infrastructure yourself — before writing a single line of business logic. skyMSP removes that friction so your team focuses on what your business actually needs. ## Why skyMSP HIPAA, HITRUST r2, SOC 2 Type II, audit logging, PHI scoping — handled by the platform, not your engineering team. 250+ pre-built bidirectional integrations with major healthcare and senior living systems, provisioned as part of your environment. Purpose-built for healthcare AI — pre-certified, pre-connected, and pre-loaded with skyAgent and the Skypoint platform. ## What's Included skyMSP combines two pre-built operations apps with the full developer surface of the Skypoint platform. Pre-built agentic app for automated infrastructure management — provisioning, scaling, and lifecycle operations across your HITRUST r2 environment. Pre-built agentic app for IT operations automation — monitoring, incident response, and day-to-day operations handled by AI agents from day one. ## Core Capabilities Use Claude Code, Cursor, GitHub Copilot, or any AI coding tool with skyMSP. Push to deploy and your app is live in skyMSP's HITRUST r2 certified cloud — HIPAA compliant, zero infrastructure setup. Python SDK, TypeScript SDK, and REST API included. Every app deploys into a certified, HIPAA-compliant environment. No security reviews. No infrastructure work. Access EHR, payer, and operational data through skyData via API, CLI, and MCP. Healthcare-specific connectors maintained by Skypoint. Version control, staging, monitoring, and rollback — all in the deploy pipeline. Every custom app gets the full Skypoint agentic platform — execution engine, memory bank, skills, and agents. Skypoint FDEs work directly alongside your team — helping you architect, build, and deploy custom healthcare AI apps faster than your team could alone. Embedded support, not a ticket queue. ## skyMSP vs Public Cloud | Feature | skyMSP™ | Public Cloud | | :--------------------------- | :-----: | :----------: | | HITRUST r2 certified cloud | ✅ | ❌ | | HIPAA compliance built in | ✅ | ❌ | | 250+ healthcare integrations | ✅ | ❌ | | AI coding tool support | ✅ | Manual | | skyAgent & platform access | ✅ | ❌ | | Deploy in days, not months | ✅ | ❌ | | Forward-Deployed Engineering | ✅ | ❌ | ## Key Metrics Deploy to compliant cloud Pre-built bidirectional integrations From idea to production ## Security & Compliance The gold standard for healthcare information protection — certified, not self-attested. Independently audited security, availability, and confidentiality controls. Your data and conversations never train external models or leave your environment. Complete, queryable audit trail for every action across your environment. ## Pricing skyMSP is available as a standalone offering. Pricing is structured as: * **Base platform fee** — covers the managed HITRUST r2 environment, skyInfra, skyOps, and platform access * **Pass-through cloud cost** — actual underlying compute, storage, and network consumption * **20% management fee** — applied to the cloud cost for ongoing operations, security, and compliance management Contact your Skypoint representative for a quote tailored to your workloads. ## Who Benefits | Role | Value | | :--------------------- | :--------------------------------------------------------------------------------------------------- | | Engineering Teams | Build custom healthcare AI without managing security, integrations, or compliance infrastructure | | Product Teams | Rapidly prototype and ship new agentic workflows in days instead of months | | Partners & Consultants | Extend the Skypoint platform with custom solutions for clients, all within the certified environment | | IT Leaders | Custom apps stay inside a governed, HITRUST r2 environment — no shadow IT, no compliance risk | | Executive Leadership | Accelerate AI initiatives with a platform pre-built for healthcare scale and regulation | ## FAQ skyMSP works with any AI coding agent and IDE — Claude Code, Cursor, GitHub Copilot, and others. There's no lock-in to a specific coding agent. No. skyMSP handles the HIPAA compliance layer for you — including PHI scoping, encryption, audit logging, and environment provisioning. Your app deploys into a pre-certified environment. You write the business logic; Skypoint handles the compliance. skyData includes 250+ pre-built bidirectional integrations with all major healthcare and senior living systems. Integrations are provisioned as part of your environment setup. Yes. Every app and agent that operates on skyMSP runs natively inside the Skypoint HITRUST r2 environment with full access to skyAgent and other Skypoint apps via API, CLI, and MCP. Most teams ship their first custom app in under two weeks with Forward-Deployed Engineering support. If you start with skyInfra or skyOps as a template, you can be in production even faster — sometimes in days. A Managed Service Provider (MSP) platform gives your team the infrastructure, tools, and managed services to build and run software without owning the underlying stack. skyMSP goes further — it's purpose-built for healthcare AI, so it comes pre-certified for HIPAA and HITRUST r2, pre-connected to 250+ EHRs, and includes skyInfra and skyOps so you start with working automation, not a blank canvas. ## Get Started Your team has the ideas. skyMSP gives them the platform to ship. Contact your Skypoint representative to provision your HITRUST r2 environment and onboard your engineering team. # Source: https://docs.skypoint.ai/docs/skynote skyNote skyNote ## Overview skyNote is Skypoint's Meeting Intelligence platform — turning every meeting into searchable, shareable, and actionable knowledge. Live transcription, in-meeting AI assistance, deep search across every conversation, and granular sharing controls give teams a complete record of what was said, what was decided, and what needs to happen next. skyNote joins calls automatically, transcribes in real time with speaker attribution, and produces clean, refinable summaries — all wired into the chat and collaboration tools teams already use. ## Why skyNote Real-time transcription with speaker names and continuously updating captions captures every meeting accurately — no scribbled notes, no missed details. Comprehensive in-transcript search with deep-linking lets teams jump directly to the exact moment something was discussed, across any past meeting. skyAgent runs in a dedicated side panel during live meetings, providing real-time AI assistance — answering questions, surfacing context, and suggesting next steps as the conversation happens. Granular permission tiers (view, comment, edit) give owners precise control over who can access each meeting and what they can do with it. ## Key Capabilities Speaker-attributed live transcription with continuously updating captions. Pause and resume captions on demand, with partial captions preserved when dismissed. Behind the scenes, live captions switch seamlessly between providers for higher reliability and lower latency. Search across every meeting and every transcript. Deep-link directly into the exact moment of any discussion to share context without rewinding hours of audio. A dedicated side panel during live meetings delivers real-time AI assistance — context lookups, decision summaries, and instant Q\&A — without leaving the call. Regenerate and customize AI-generated meeting summaries to match the audience: executives, project teams, customers, or compliance reviewers. Per-meeting permission tiers (view, comment, edit) let owners share precisely the right level of access with internal teams and external collaborators. Interact with skyNote via `/sn` commands directly from your chat tools, with a compact message format optimized for Microsoft Teams compatibility. A dedicated "Scheduled" tab shows every upcoming bot-joined meeting at a glance, so teams know exactly which calls will be captured. The live meeting detail page uses a two-column layout designed for operator focus — captions on one side, controls and context on the other. Automatic calendar sync keeps meeting coverage current, with a mobile-friendly Sync button for immediate event refresh on the go. Share emails route recipients directly to the meeting page, with the skyNote logo embedded as an inline attachment for consistent rendering across email clients. Custom email templates apply correctly to share notifications. Meeting notes are always cleared when the note-taker is turned off — even after errors — preventing stale or unintended content from being retained. Recordings are stored in tenant-isolated storage, strengthening data governance and ensuring each customer's meeting content remains fully separated. ## Who Benefits Catch up on missed meetings in minutes with refinable AI summaries, or search across every prior conversation to recall exactly what was decided. Capture every customer call with speaker-attributed transcripts, share relevant moments with internal stakeholders, and let skyAgent surface follow-ups in real time. Turn standups, planning sessions, and reviews into searchable knowledge. Use deep links to share exact decisions instead of rewriting them. Maintain accurate, tenant-isolated records of every recorded meeting with granular sharing controls and complete transcripts ready for review. ## Business Impact Every meeting produces a searchable transcript, an AI summary, and clear follow-ups — turning conversation into momentum. Deep search and refinable summaries mean institutional knowledge stays accessible long after the call ends. Instant conversation tab switching, faster skyAgent chat retrieval, and chat-tool bot commands keep teams moving without waiting on the platform. Tenant-isolated recording storage and granular sharing permissions give compliance and IT confidence in every meeting captured. ## Mobile Experience skyNote is polished for mobile use throughout — including video sharing and meeting URL stability — so teams can capture, review, and share meetings from any device. ## Get Started skyNote is available as part of the Skypoint platform. Contact your Skypoint representative to enable skyNote for your organization's meetings. # Source: https://docs.skypoint.ai/docs/skyprivacy skyPrivacy skyPrivacy ## Overview skyPrivacy provides a self-service privacy portal with automated DSR (Data Subject Request) processing for effortless compliance. It empowers individuals to exercise their privacy rights while reducing the burden on privacy teams. ## Key Capabilities Enable individuals to submit and track privacy requests Streamline DSR fulfillment with intelligent automation Generate compliance reports and audit trails ## Features ### Privacy Request Portal Provide a branded self-service portal for data subjects to submit access, deletion, and correction requests. ### Automated Workflow Automatically route requests, gather data from connected systems, and prepare responses. ### Identity Verification Verify requester identity through configurable verification workflows. ### Audit Trail Maintain complete records of all privacy requests and actions for regulatory compliance. ## Use Cases * **HIPAA Compliance**: Manage patient access requests under HIPAA * **State Privacy Laws**: Comply with CCPA, CPRA, and other state privacy regulations * **Data Access Requests**: Provide individuals with copies of their data * **Deletion Requests**: Process and verify data deletion requests # Source: https://docs.skypoint.ai/docs/skyreferral skyReferral skyReferral ## Overview skyReferral is an end-to-end referral lifecycle automation platform that transforms unstructured medical documents into payor-ready referrals — faster, with fewer denials, and without adding work for your team. Built on the HITRUST r2 certified Skypoint platform, skyReferral uses AI to interpret medical records, extract clinical data, verify insurance eligibility, and move referrals through a configurable multi-stage workflow — all while keeping your team in control with human-in-the-loop approvals and full audit trails. * **From fax to first visit faster** — AI models interpret unstructured medical records against complex payor criteria, creating payor-ready documentation with fewer delays and denials. * **High-conversion pipeline** — Automated classification, routing, and eligibility checks accelerate intake while giving referring providers full visibility without adding work for your team. * **Operational resilience** — Human-in-the-loop approvals and auditable workflows keep you in control while automation handles the repetitive steps. ## How It Works Documents arrive by fax, email, or direct upload (PDF, PNG, JPG, TIFF). AI automatically classifies each page — identifying referrals, insurance records, and clinical documents — and routes them to the correct referral. Multi-patient packets are separated and duplicates are merged. AI reads medical documents and extracts structured data including patient demographics, referring provider details, diagnoses, insurance information, medications, allergies, lab results, and surgical history. The system checks your EHR for existing records and automatically requests any missing documentation. Insurance eligibility is verified by identifying the correct payor and plan. Benefit investigations and prior authorization processing are initiated automatically. Clinical criteria are evaluated against your organization's guidelines — with your team reviewing and approving before any decision is finalized. Patients are contacted for scheduling, appointments are booked with the target provider or facility, and post-visit follow-up ensures both the patient and referring provider are kept informed of outcomes. ## Key Capabilities Centralized inbox for all incoming referral documents. Upload directly or receive by fax and email. AI classifies each document automatically, and your team can review, tag, and convert documents into referrals with a single action. Combined documents can be split into individual referrals and reclassified without re-upload. Process large referral batches with confidence using bulk ingestion with scheduling, inbox processing, and automated failure handling — ideal for high-volume intake and backlog migration. AI reads unstructured medical records and populates structured referral data — patient demographics, diagnoses and ICD codes, insurance details, medications, allergies, lab results, visit notes, medical and surgical history — replacing hours of manual data entry. AI-powered matching of patients to optimal providers based on specialty, location, availability, and patient preferences. Prioritize in-network providers to reduce leakage. Weighted scoring tracks referral readiness in real time across five categories — patient information, clinical details, provider information, documents, and insurance — so your team knows exactly what needs attention. Connects to your electronic health record system to look up existing patients, create new records, add providers, and write back medications and allergies — keeping your EHR current without manual re-entry. Identifies the correct payor and plan, runs eligibility checks, and surfaces coverage details including copay, coinsurance, deductible remaining, plan type, and prior authorization requirements. Benefit investigations automated end-to-end. AI evaluates referral documentation against your organization's clinical criteria to assess medical necessity. Highlights whether each requirement is met, partially met, or missing — giving reviewers a clear summary to approve or escalate. Automatically reaches out to referring providers to retrieve missing documentation via fax, email, or portal. Separate communication stages for provider and patient outreach keep every stakeholder informed. Book appointments with target providers and facilities directly from within the referral workflow, ensuring referrals convert to scheduled visits without leaving the platform. Centralized view of all referrals with filtering by urgency level (Routine, Urgent, Stat), workflow stage, patient name, due date, and more. Search across referrals and track progress at a glance. Maximize in-network referral rates. Track referrals from submission through completion with automated follow-up and complete visibility into referral status and outcomes. Track referral volume, processing times, team performance, referral patterns, leakage rates, and provider performance. Identify bottlenecks and measure conversion rates. ## Task Management & Workflow Referrals are automatically organized into work queues by workflow stage. Tasks are created as referrals progress, with priority-based assignment ensuring the most urgent work surfaces first. Every AI-automated action can be reviewed and approved by your team before it takes effect — extracted data, eligibility determinations, clinical review findings. Your staff has full visibility and final say. Workflow stages can be enabled or disabled to match your referral process. AI agents can be turned on or off for each stage. Upload your own clinical criteria documents to customize clinical reviews. Each task tracks its full history of actions and status changes, maintaining compliance and providing complete accountability across the referral lifecycle. ## Use Cases | Use Case | Description | | :------------------------------ | :---------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **High-Volume Referral Intake** | Organizations receiving hundreds of faxed referrals daily automate classification, data extraction, and triage — processing referrals in minutes instead of hours | | **Referral Leakage Prevention** | Track every referral from document receipt through scheduled appointment, ensuring no referral falls through the cracks | | **Prior Authorization** | Automate eligibility checks, benefit investigations, and clinical necessity reviews to reduce authorization delays and denials | | **Multi-Site Coordination** | Manage referrals across multiple facilities with configurable workflows tailored to each site's processes and requirements | | **Specialty Referrals** | Route patients to appropriate specialists within your network based on specialty, location, and availability | | **Facility Transfers** | Coordinate transfers between facilities efficiently with complete documentation and handoff tracking | ## Platform Integration skyReferral is part of Skypoint's composable platform, connecting seamlessly with: * **skyData** — Unified data foundation and workflow orchestration * **skyMDM** — Master data for accurate patient and provider records * **skyAuth** — Prior authorization automation for seamless handoff * **skyCare** — Clinical workflow integration for care coordination ## Get Started skyReferral is available as part of the Skypoint platform. Contact your Skypoint representative to configure skyReferral for your organization's referral management operations. # Source: https://docs.skypoint.ai/docs/skyreputation skyReputation skyReputation ## Overview skyReputation is the AI Reputation & Review Growth Engine built for Senior Living, part of the Skypoint AI for Senior Living 365 Suite. Families begin their search online, comparing communities based on reviews, star ratings, and how professionally communities respond. Industry trend data shows that more than 80% of senior living consumers start their search on Google and ChatGPT, and most read multiple reviews before ever calling a community. Reputation now directly influences inquiry volume, conversion rates, and move-ins. skyReputation gives senior living operators a unified, AI-powered platform to strengthen online visibility, attract more high-intent families, and ensure every community presents its best story online. With review aggregation, AI insights, integrated response management, surveys, review request campaigns, and listings optimization, marketing and sales leaders can meaningfully improve ratings, increase inquiries, and accelerate move-ins. ## Why skyReputation Drives More Leads Higher star ratings directly improve visibility in Google Search, Maps, ChatGPT and local rankings, click-through rates on referral and directory sites, and family trust at first impression. Aggregate and monitor reviews from Google, Yelp, Caring.com, A Place for Mom, Facebook, Glassdoor, Indeed, and more across all communities. AI reveals what families and prospects care about most—staff, communication, safety, cleanliness, dining, activities, value, and environment. Fast, personalized, and consistent responses build credibility and convert more leads into move-ins with AI-generated draft responses. Collect direct feedback from residents, families, and employees through customizable surveys distributed via email, SMS, or shareable links — with AI-powered analysis of open-ended responses. Manage and optimize Google Business Profiles across all communities. Track search visibility, monitor engagement metrics, and keep location information accurate and up to date. ## Key Capabilities Aggregate reviews from Google, Yelp, Caring.com, A Place for Mom, Facebook, Glassdoor, Indeed, and more. Real-time view of every community's reputation across brands and regions with sentiment tracking. Extracts sentiment, intent, urgency, and emotions across Care Quality (staff, communication, safety, cleanliness) and Experience (dining, activities, value, environment) categories. Trend insights highlight where improvements create the greatest occupancy lift. Review inbox with Google response publishing, AI-generated draft responses in multiple tones (Professional, Empathetic, Warm, Formal, Apologetic), assignment and SLA tracking, internal notes, flagging, and full audit logging. Role-based assignment, automated conversation closure, assignee notifications, an "Assigned to me" toggle, and direct links to Google review URLs streamline triage. Organize communities into custom regions or cohorts and filter reviews, insights, and analytics by group. Ideal for multi-market operators comparing brands, geographies, or program cohorts side by side. Comprehensive score per community based on star rating, review volume, recency, AI-analyzed sentiment, and source spread. Score levels (Excellent, Good, Fair, Poor) with trend tracking and customer vs. employee comparison. No-code survey builder with 10+ question types, multi-page support, and distribution via email, SMS, or shareable links. Collector management, templates for common scenarios, and AI-powered summarization that now spans open-ended, rating, NPS, and multiple-choice questions. Multi-collector filtering and side-by-side comparison reveal how different programs perform against each other. Send review requests via email and SMS with personalized templates. Direct recipients to Google, Yelp, Caring.com, or A Place for Mom. Track delivery status and monitor campaign performance. Connect and manage Google Business Profiles with analytics dashboard (search views, map views, clicks, calls), photo management, content sync, and profile health scoring. Topic sentiment heatmap across communities, strength/weakness identification, trend indicators, drill-down to underlying reviews, community benchmarking against portfolio average, and geographic performance visualization. Auto-surfaces reviews needing immediate action, prioritized by risk level, urgency, and sentiment. Grouped by community for efficient triage of negative experiences and urgent complaints. Average rating and review volume trends with weekly, monthly, or quarterly granularity. Customer vs. employee comparison, NPS trend analysis, and seasonal pattern identification. Scheduled data ingestion from all review sources on configurable schedules. On-demand manual sync, job history with status tracking, and processing monitoring. Location management with review source linking, operator and owner profiles, user invitations with domain whitelisting, granular role-based access control, and Google Business Profile configuration. ## Who Benefits Most Improve ratings and online visibility while strengthening competitive positioning. Track performance by community, region, and brand with comprehensive analytics. Optimized for search and AI tools like ChatGPT, driving higher-quality traffic to community websites and tours. ## The skyReputation Advantage skyReputation is purpose-built for senior living operators and capital partners who want a simpler, smarter, AI-native reputation platform without the complexity or high price tags of traditional reputation software. * **Improve online reputation** across your entire portfolio * **Increase qualified leads** with higher star ratings and better visibility * **Boost occupancy** by converting more families through trust and credibility **skyReputation: Boost reviews. Build trust. Convert more families.** # Source: https://docs.skypoint.ai/docs/skysign skySign skySign ## Overview **Upload. Sign. Complete.** skySign is Skypoint's enterprise B2B eSignature platform with complete DocuSign IAM Professional feature parity. It streamlines document signing workflows across healthcare, senior care, health plans, and HR organizations with enterprise-grade security, compliance audit trails, and seamless integration capabilities — available as full SaaS or self-managed deployment. **Part of the Skypoint AI Platform "365" Product Suite** *** ## The Problem We Solve | Challenge | Impact on Your Organization | | :--------------------------------------------------------- | :------------------------------------------------------- | | Paper-based or fragmented signing workflows | Slow turnaround, lost documents, frustrated signers | | Manual contract tracking in shared drives and spreadsheets | Missed renewals, expired agreements, revenue leakage | | Lack of audit-ready evidence | Compliance exposure during surveys, audits, and disputes | | Disconnected signing and contract systems | Duplicate data entry and broken handoffs between teams | | Inconsistent governance across multi-site operations | Uneven processes and inconsistent customer experience | | Stale, expired, or orphaned envelopes | Clutter, confusion, and unintended signing risk | ## Our Solution | Capability | Benefit | | :---------------------------------------------------------- | :------------------------------------------------------------- | | End-to-end eSignature with DocuSign IAM Professional parity | Replace DocuSign without losing features | | Contract Lifecycle Management with SharePoint import | Bring existing contract folders into one governed repository | | AI-powered field detection and signer suggestions | Prepare envelopes in seconds, not minutes | | Scheduled sending for single and bulk envelopes | Dispatch high-volume sends at the right time | | Renewal alert ladder and 90-day expiry visibility | Catch renewals early and protect revenue | | Restated-contract amendments with approval workflows | Cleaner negotiation cycles with full change-management control | | Counterparty negotiation panel with audit trails | Move negotiations forward without leaving the platform | | Audit certificates with ISP and geolocation | Defensible, court-admissible evidence on every envelope | *** ## How It Works Upload PDF documents to create a signing envelope. Add signer email addresses with sequential or parallel routing order. Place signature, initials, date, text, and checkbox fields — or let AI suggest placements automatically. Send via email, SMS, or WhatsApp with personalized signing links. Signers review and sign from any device with a mobile-responsive experience. Audit trail is sealed and the document is securely archived with configurable retention. *** ## Key Capabilities One platform for senior living move-ins, provider agreements, patient consent forms, and business documents. Configure workflows per segment without custom development. HIPAA compliant with HITRUST r2 infrastructure. ESIGN Act compliant signatures with audit trails, PAdES digital signatures, audit certificates, and configurable document retention. AI-suggested field placements, document classification, signer recommendations, plain-language summaries, and anomaly detection for smarter document workflows. Bulk send, PowerForms, web forms, visual workflow builder, payment collection, identity verification, collaborative comments, and scheduled sending for both single and bulk envelopes. Import entire contract folders from SharePoint with a guided experience and ongoing re-import support. Dashboard tiles and quick-filter chips surface contracts expiring within 90 days, and a renewal alert ladder on each contract page lets owners acknowledge or snooze upcoming renewals inline. Amendments are handled as a restated-contract flow with real-time status, approval workflows, and dedicated status states. A counterparty negotiation panel in the public signing experience supports counter-proposal uploads, round-open notifications, audit trails, webhooks, and permission gates. Reach signers through email, SMS, and WhatsApp with automated reminders across all channels. Central agreement storage with AI extraction of parties, dates, amounts, and key terms. Automated renewal and expiry alerts. Coordinate signatures across residents, family members, POA holders, physicians, and staff with sequential or parallel routing. Connect with 250+ healthcare systems through skyData, including EHRs, senior living platforms, and CRM systems. *** ## Use Cases by Segment | Segment | Key Use Cases | | :------------------------ | :-------------------------------------------------------------------------------------------- | | **Senior Living** | Move-in packets, residency agreements, physician orders, POA tracking, care team coordination | | **Provider Groups** | Patient consent forms, provider contracts, credentialing, insurance documentation | | **Health Plans** | Participant enrollment, authorized representative agreements, enrollment packets | | **Home Health & Clinics** | Patient intake, care plans, HIPAA consent, treatment authorizations | | **HR & Onboarding** | Offer letters, onboarding documents, credentialing, compliance training | | **General Use** | Business contracts, vendor agreements, internal policies, multi-location management | *** ## AI-Powered Intelligence Gemini-powered AI is woven throughout skySign to reduce prep time, catch errors, and surface meaning in long documents. Automatically detect signature, initial, date, and text fields on uploaded documents — no manual tagging required. Preview suggested field placements before committing them, so senders stay in control of the final layout. Classify uploaded documents by type to route, label, and store them consistently across the organization. Recommend appropriate recipients based on document content and prior signing patterns. Generate plain-language summaries of complex contracts so signers know what they are agreeing to. Flag unusual clauses, missing fields, or out-of-pattern terms before envelopes are sent. Extract parties, effective dates, renewal terms, amounts, and key clauses from imported and executed agreements. *** ## Compliance & Governance skySign is built for organizations where every signature must stand up to a survey, an audit, or a court. Set retention policies per envelope type or tenant to align with regulatory and corporate requirements. Full ESIGN Act support including paper-copy options and required disclosures. Deliver consent disclosures in the signer's preferred language for inclusive, compliant signing experiences. Every completed envelope includes a Certificate of Completion with timestamps, ISP attribution, and geolocation for enhanced auditability. PAdES-compliant digital signatures provide long-term, tamper-evident proof of signing. Track every notification, reminder, and delivery event for a complete communication audit trail. Opt-in policy automatically voids expired envelopes to improve compliance posture and reduce stale-envelope clutter. Administrators manage suppression lists per tenant for precise control over deliverability and bounce handling. *** ## Enterprise Features Full DocuSign IAM Professional parity, with the scale and governance enterprises expect. Send personalized envelopes to thousands of recipients with role-based field assignment, senders acting as signers, and full witness recipient support. End-to-end email delivery is reliable, and cancelled envelopes are correctly invalidated. Schedule single envelopes or full bulk sends for future dispatch — ideal for enrollment windows, renewals, and time-zone-sensitive sends. Publish self-service signing links for high-volume, on-demand documents with no manual sending required. Embed signing experiences directly into websites and portals for seamless intake. Design multi-step routing, approvals, and conditional logic with a drag-and-drop builder. Collect payments and authorizations during signing for orders, deposits, and co-pays. Verify signer identity with government ID checks before high-trust signatures. Reviewers and signers can comment inline to resolve questions without leaving the envelope. Show or hide fields dynamically based on signer responses for cleaner, smarter forms. Require checklists and accept attachment uploads directly within the signing flow. A redesigned dashboard with KPI tiles and a "Today's Queue" view gives senders and administrators an at-a-glance operating picture. A maintenance banner with live polling provides clear visibility during planned system updates. *** ## Business Impact Eliminate paperwork prep and chasing. Teams focus on residents, not documents. Complete paperwork before arrival for a smooth start. All documents securely stored and instantly searchable in the cloud. Family and POA holders sign from anywhere, anytime. Court-admissible audit trails with every signature timestamped. Data flows seamlessly across skySign and other sky\* applications. ## Who Benefits Streamline admissions, reduce move-in time, and maintain compliance across assisted living, memory care, and independent living communities. Accelerate patient intake, simplify credentialing, and reduce front-office burden with integrated document workflows. Standardize document signing across departments and facilities with enterprise-grade security and centralized management. Gain visibility into document status, reduce administrative costs, and ensure consistent compliance across the organization. *** ## Platform Integration skySign is part of Skypoint's composable platform, connecting seamlessly with: * **skyData** — Unified data management and workflow orchestration * **skyMDM** — Master data for accurate resident and patient records * **skyCare** — Clinical workflow integration * **skyAdmissions** — Automated referral intake and eligibility * **skyComply** — Compliance tracking and corrective actions * **skySupport** — Integrated in-app help desk and ticket creation *** ## Get Started skySign is available as part of the Skypoint platform. Contact your Skypoint representative to configure skySign for your organization's specific document signing needs. # Source: https://docs.skypoint.ai/docs/skysupport skySupport skySupport ## Overview skySupport is the AI-Powered Enterprise Helpdesk & Support Ticketing Platform built for Healthcare, Senior Living, and HR organizations, part of the Skypoint AI Suite. Modern support expectations have evolved dramatically. Customers expect instant responses, personalized service, and seamless experiences across every channel. For healthcare and senior living organizations, the stakes are even higher—support interactions directly impact patient outcomes, family trust, and regulatory compliance. skySupport gives organizations a unified, AI-powered platform to deliver exceptional support experiences, resolve issues faster, and ensure every interaction meets the highest standards of care. ## Why skySupport Delivers Better Outcomes AI-powered ticket classification, smart routing, and suggested responses help agents resolve issues faster while maintaining quality and compliance. Manage support channels from one unified workspace with complete customer context and interaction history. AI reveals what customers are asking about most, surfaces relevant knowledge base articles, and provides agents with real-time recommendations. Purpose-built for healthcare and senior living with complete audit trails, data isolation, and access controls to support regulatory requirements. ## Key Capabilities ### Intelligent Ticket Management Full lifecycle ticket management with enterprise-grade capabilities: * **Email-based intake** with automatic ticket creation and threading * **AI classification** for automatic categorization, priority, and urgency detection * **Smart routing** to the right team based on skills, availability, and workload * **SLA management** with configurable policies, escalation rules, and breach alerts * **Business rules** including triggers, automations, and macros for consistent handling * **Merge & Split** — Combine duplicate tickets into one or split complex tickets into separate child items when work spans multiple teams * **Work Item Relationships** — Link tickets with Blocks/Blocked By, Relates To, and Duplicates/Cloned relationships to manage multi-ticket workflows * **Multiple Views** — Switch between List, Kanban board, Timeline (Gantt-style), and Calendar views without losing filter or column preferences * **Saved Views** — Save custom filter and column combinations as named views for quick access to frequent perspectives like "My Open Critical Items" or "Overdue This Week" Support teams gain complete visibility into every ticket from creation to resolution. Track what customers are asking, understand bottlenecks, and identify where improvements will create the greatest impact. ### AI-Powered Knowledge Base Self-service that actually works: | Category | Capabilities | | :----------------- | :----------------------------------------------------------------- | | Content Management | Hierarchical categories, versioned articles, approval workflows | | AI Features | Generative search, direct answers with citations, related articles | | Multi-brand | Separate help centers for different brands or audiences | skySupport extracts intent, context, and urgency from customer questions, helping surface the most relevant content and reducing ticket volume through effective self-service. ### Agent Workspace Everything agents need in one unified interface: * **Unified inbox** with queue-based ticket views * **Customer context sidebar** with full interaction history * **AI-suggested responses** for faster, consistent replies * **Internal notes and collaboration** for complex issues * **Ticket history** with complete audit trail * **@mentions** — Tag colleagues in internal notes to loop them in; they receive in-app and email notifications * **Collision Detection** — Alerts both agents when two people open the same ticket simultaneously, preventing conflicting updates * **Macros** — Admins define one-click action bundles (e.g., set status, add tag, post comment) for consistent, repeatable handling * **Inline Images** — Paste, drag-and-drop, or attach images directly in ticket descriptions and comments with full-screen preview Agents work smarter with AI assistance that suggests responses, surfaces relevant knowledge, and predicts outcomes—all while maintaining the human touch that healthcare and senior living require. ### Request Types & Service Catalog Organized service offerings with a full form builder for complete control over what information is collected: * **Categorized services** with clear descriptions and SLAs * **Custom request types** — Incident, Service Request, Change, Problem, Question, or custom types * **Form builder** with Text, Number, Date, Select, Multi-Select, Checkbox, URL, and User Picker fields * **Required/optional fields** with default values, field grouping, and form preview before publishing * **Approval workflows** for sensitive requests * **Fulfillment tracking** from request to completion Organizations can offer a consumer-grade request experience while maintaining control over service delivery and compliance. ## Product Modules Complete ticket lifecycle management with email-based intake, SLA tracking, and intelligent automation. AI-powered self-service that reduces ticket volume with accurate, relevant answers. Unified workspace with tickets, customer context, AI assistance, and collaboration tools. Organized service offerings with a form builder for custom request types, approval workflows, and fulfillment tracking. Intelligent assistance that suggests responses, classifies tickets, summarizes conversations, detects customer sentiment, and powers conversational self-service with configurable AI agents per portal. Role-based access control at platform and portal levels with Owner, Admin, Contributor, Viewer, and Customer roles — plus custom portal roles with granular permission matrices. Pre-built reports for Created vs Resolved, SLA Compliance, Agent Performance, Request Type Distribution, Resolution Time, CSAT, and NPS — all with configurable time periods. A custom report builder with live preview, date filtering, and a "Ticket list" drill-down block lets teams move from any aggregate straight to the underlying tickets. Deliver custom and built-in reports to inboxes automatically. Configure cadence, recipients, and rich email content so leaders, ops, and stakeholders get the right view of support performance without logging in. A comprehensive REST API with bearer-token authentication connects skySupport to external systems for ticket workflows, knowledge base operations, and reporting. Endpoints cover workflow discovery, ticket comments, knowledge base search, and more — enabling end-to-end automation across your stack. Automatic satisfaction surveys on ticket closure with configurable rating scales. Separate NPS surveys measure customer loyalty per portal. Results tracked per ticket and in reporting. Searchable customer directory with organization grouping, email domain auto-association, full ticket history per contact, and volume metrics per organization. In-app and email notifications for assignments, mentions, replies, SLA breaches, and approvals. Per-user channel preferences and customer notification delivery logs. Reach customers via web portal, email, SMS, WhatsApp, and voice. Channel availability is configured at the instance level. Lightweight widget for any external website — customers access the Knowledge Base, submit requests, and track tickets without leaving the page. Customizable per portal. ## Business Impact Faster, more personalized support leads to happier customers and families. AI-powered automation and self-service reduce support workload. Audit trails and access controls meet regulatory requirements. Multi-tenant architecture grows with your organization. ## Who Benefits Improve resolution times and customer satisfaction while reducing agent burnout with AI-assisted workflows. Support regulatory compliance with complete audit trails, access controls, and data isolation. Streamline employee support with self-service knowledge bases and automated workflows. Gain visibility into support operations with metrics that connect support performance to business outcomes. ## Platform Integration skySupport is part of Skypoint's composable platform, connecting seamlessly with: * **skyData** — Unified data management and workflow orchestration * **skyMDM** — Master data for accurate customer and patient records * **skyCare** — Clinical workflow integration * **skyVoice** — Voice AI for phone-based support * **skyChat** — Conversational AI across digital channels * **HubSpot** — Two-way contact sync, ticket-to-deal association, and property mapping * **SharePoint** — Sync SharePoint documentation into the Knowledge Base for AI-powered search ## Get Started skySupport is available as part of the Skypoint platform. Contact your Skypoint representative to configure skySupport for your organization's support operations. # Source: https://docs.skypoint.ai/docs/skyvoice skyVoice skyVoice ## Overview skyVoice is a human-like AI that manages inbound and outbound calls instantly. It delivers verticalized, PHI-aware voice automation with built-in IVR menus, intelligent call routing, lead capture, and CRM integration — reducing hold times and manual phone work while keeping humans in the loop for complex cases. ## What It Does Voice AI agent that answers and places calls for common workflows: intake and screening, insurance verification, appointment reminders, referrals, billing questions, and basic triage. Each agent supports configurable intake questionnaires tailored to your workflows, with customizable voice selection so different departments can have distinct AI personalities. Configurable touch-tone (DTMF) phone menus and AI-powered virtual receptionist agents that detect a caller's reason for calling and route them to the right department — no hold time and no human operator needed. Supports multi-level routing alongside conversational AI routing for a natural caller experience. Callers can also be routed directly to a preferred representative at the greeting, bypassing queues entirely. Automatically creates leads from intake calls and syncs them to your CRM — including Salesforce, HubSpot, and Dazos. Includes customizable lead status pipelines, rep and territory assignment, and bulk CSV import. Every lead entry includes a validated caller phone number with standardized normalization across all forms — placeholder or contact-less entries are blocked at the source, keeping CRM data clean and follow-up reliable. Launch AI-powered outbound calling campaigns with uploaded contact lists, flexible scheduling, and automatic retry logic. Pre-load caller context so the AI agent picks up where a previous conversation left off — no repeated questions, just personalized follow-up at scale. Escalates to human staff via warm transfer with full context — the AI patches the caller through while staying connected as a silent listener, so nothing is lost in the handoff. If a human patch handoff fails, the caller is routed to voicemail instead of being silently disconnected. After-hours calls are handled with customizable voicemail and overflow actions are honored immediately, and crisis situations are detected in real time and immediately routed to the 988 Suicide & Crisis Lifeline. Connected to unified data in the Skypoint platform for real-time information access during calls, including insurance eligibility verification, patient records, and appointment data. The AI agent pulls and validates information on the fly to resolve calls faster. Handles intent detection, data lookups (e.g., coverage, appointment data), and workflows (e.g., capturing pre-visit details) without a human operator. ## Why It's Needed in the Market Call centers and front offices in provider groups, health systems, and senior living communities struggle with: * High call volume overwhelming staff * Staffing shortages affecting service levels * Inconsistent caller experiences * Missed after-hours calls and lost leads * Lack of marketing attribution for phone calls Generic call bots don't understand healthcare context (MRNs, payer rules, care plans). **skyVoice delivers verticalized, PHI-aware voice automation**, reducing hold times and manual phone work while keeping humans in the loop for complex cases. ## Key Metrics It Moves Reduced wait times and dropped calls. AI agents answer instantly with no hold queues, handling routine calls end-to-end and freeing staff for complex cases. Higher rates via data-aware, AI-driven interactions. Real-time access to patient data, insurance eligibility, and appointment information means more issues resolved on the first call. Fewer manual calls per staff member. AI handles intake, screening, routing, and follow-up — letting your team focus on cases that need a human touch. Smoother phone experiences improving CSAT/NPS scores. Callers get immediate answers, consistent service, and seamless handoffs when they need a real person. Automated lead capture and CRM sync ensure no caller falls through the cracks. Every intake call becomes a tracked lead with rep assignment and follow-up workflows. Dynamic number insertion links web campaigns to phone calls for closed-loop attribution. Track which channels drive calls and measure true campaign ROI across digital and voice. # Source: https://docs.skypoint.ai/docs/snowflakeconnector # Connecting to Snowflake Snowflake connector enables you to connect to the Snowflake data warehouse. You can connect the Snowflake data warehouse to skyData Studio and perform various tasks such as data ingestion, unification, transformation, and enrichment to gain insights from the data. You can use skyData's built-in connector for importing data from Snowflake. This document will guide you through the process of connecting Snowflake to the skyData. ## Prerequisite You will need the following details to configure and import data using the Snowflake⇗ connector: * Account Name * Username * Password * Database Name * Schema Name * Host * Warehouse Name ## To import data using the Snowflake connector Follow the below steps to create and configure a new dataflow for the Snowflake import connector: 1. Go to **Dataflow > Imports**. 2. Click **New dataflow**. The **Set dataflow name** page appears. Alt image 3. Enter the dataflow name in the **Name** text area. 4. Click **Next**. The **Choose connector** page appears. Alt image ## Add Snowflake connector 1. In the **Choose connector** page, select **Snowflake** connector. You can use the **Search** feature to find the connector. Also, the **Snowflake** connector can be found under **Cloud** and **Data Warehousing** category. Alt image 2. Enter the **Display Name** for your dataflow in the text area. 3. You can add in the **Description** text area. 4. Click **Next**. The **Configuration** page appears. Alt image ## Connect to the Snowflake account Follow the below steps to configure the connection to Snowflake: 1. Enter the **Account Name**, which is provided by Snowflake when you sign up for an account. 2. Enter your **Username** and **Password**. 3. Enter the **Database Name**. 4. Enter the **Schema Name**. 5. Enter **Host**, which is the hostname or IP address of the Snowflake service. 6. Enter the **Warehouse Name** of your Snowflake account that you want to use for executing queries. 7. Click **Connect**. Once the connection is established, you can use the connector to import data from a table in Snowflake. Alt image 8. In the **Table details** section, each table is identified by a row. Click the checkbox to select the table for import and use the dropdown to mark it as Data or Metadata.
In the **Table Details** section, all tables are selected by default. You can choose only the tables you want to import and process the data. For example, to import customer data, select the tables containing customer information such as name, email, address, and contact details. | Item | Description | | :------------------------ | :------------------------------------------------------------------------------ | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | |
Data
| Loads customer data | |
Metadata
| Loads Metadata | | File name | Displays the name of the file that you imported. | | Entity name | Displays the imported table name by default. You can rename it later if needed. | 9. Click **Save** to apply the changes. After saving the connection, the Snowflake connector appears on the **Dataflow** > **Imports page**. ## Run, edit, and delete the imported data Once the table is imported, you can execute, modify, and remove the imported table from the Dataflow. Follow the below steps: 1. Go to the **Dataflow** > **Imports page**. Alt image | Item | Description | | :---------------- | :------------------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables count | Displays the number of tables imported. | | Created Date | Displays date of creation. | | Last refresh type | Displays the refresh value: Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Group by | Option to view the items in a specific group. For example, type, status, tables count, etc. | 2. Select the horizontal ellipsis under the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run history**. |
In the Dataflow, you can view the error message for data import failures from a data source under **Run History** > **Description**. Also, you can see the status, start time, end time for the execution of data pipeline. ## Next step After completing the data import, start the [Master Data Management (MDM) - Resolve](stitch.md) process to create a single, unified view of the data. With skyData's MDM, you can ensure that your data is accurate, consistent, and reliable, making it easier to use for business processes, analytics, and reporting. # Source: https://docs.skypoint.ai/docs/sogolytics # Sogolytics ## Overview The Sogolytics connector integrates the Sogolytics survey and experience management platform with skyData, bringing survey data and participant response information into your unified data environment. Sogolytics is a survey and feedback platform used by organizations to capture structured feedback from patients, residents, members, employees, and students. By connecting Sogolytics to skyData, organizations can combine survey and experience data with clinical, operational, and workforce data to build a complete picture of satisfaction and engagement across their communities. > **Enhanced connector:** Sogolytics has been upgraded to an Enhanced connector, replacing the previous Classic version. The new setup experience uses a guided, step-by-step wizard with built-in connection validation. ## What Data We Extract The Sogolytics connector brings in the following survey data: * **Surveys** — The full catalog of surveys created in your Sogolytics account, including survey names, types, and configuration * **Survey Responses** — Individual response records submitted by participants, including answers to each question. Optionally includes partial and incomplete responses * **Participation Status** — Records of who has been invited to take each survey and their current completion status (invited, started, completed). Optionally includes direct participation links * **Survey URLs** — The published URLs for each active survey ## Why It Matters Patient, resident, and member satisfaction data is critical for understanding care quality, service performance, and engagement. Connecting Sogolytics to skyData enables organizations to: * Combine satisfaction survey results with clinical outcomes, readmission rates, and care metrics * Analyze survey participation rates across communities, facilities, or departments * Identify correlations between resident or patient experience scores and operational data * Track satisfaction trends over time alongside workforce changes or care interventions * Build unified dashboards that bring together experience data and operational performance ## Who Benefits | Role | Value | | ------------------------------------ | --------------------------------------------------------------------------------------- | | Quality and Patient Experience Teams | Survey response data combined with clinical metrics to measure and improve care quality | | Operations Leaders | Participation rates and satisfaction trends across communities or departments | | HR and People Teams | Employee engagement survey data alongside workforce and retention analytics | | Executive Leadership | Organization-wide experience scores tied to operational and financial outcomes | ## Get Started Contact your Skypoint representative to configure the Sogolytics connector for your organization. > **Note:** If you previously used the Classic Sogolytics connector, the Enhanced version provides the same data with an improved guided setup experience. Existing dataflows using the Classic connector continue to work without any changes required. # Source: https://docs.skypoint.ai/docs/sqlserver # Connecting to SQL Server Microsoft SQL Server is a proprietary relational database management system (RDBMS) by Microsoft, built to efficiently store and retrieve data for diverse applications. It supports transaction processing, BI, and analytics, scaling from single machines to large systems. SQL Server 2022 introduces features like Azure-based disaster recovery, Synapse Link, and enhanced scalability, ensuring performance, data integrity, and seamless business operations. SkyPoint AI Platform (AIP) integrates with SQL Server via an inbuilt connector, enabling centralized data management, streamlined operations, and advanced analytics. This integration breaks data silos, supports direct data operations, and enhances governance for improved compliance and insights, providing a secure and efficient data management solution. This document will guide you through the process of connecting SQL Server to the skyData. ## Prerequisite You need the following details to configure and import data using the SQLServer⇗ connector: * Server name * Database name * Port number * Username * Password ## To import data using the SQL Server connector Follow the below steps to create and configure a new dataflow for the SQL Server import connector: 1. Navigate to **Dataflow > Imports**. Alt image 2. Click **New dataflow** as indicated by an arrow. The **Set dataflow name** page appears. Alt image 3. Enter the desired name for the dataflow in the **Name** text field. 4. Click **Next** to proceed. The **Choose connector** page appears. Alt image ## Add SQL Server connector 1. On the **Choose Connector** page, use the **Search** feature to locate and select the **SQL Server** Connector. 2. Enter the **Display Name** for your dataflow in the provided text field. 3. Optionally, add a **Description** in the designated text area. Alt image 4. Click **Next** to proceed. The **Configuration** page appears. Alt image ## Connect to the SQL Server account 1. Fill in the required details on the Configuration page. 2. Click **Connect**. Alt image Once the connection is established, the connector can be used to import data from SQL Server tables. 3. Scroll down to the **Table Details** section, select the checkboxes for the tables you wish to import, and use the dropdown menu to label them as either **Data** or **Metadata.** Alt image By default, all tables in the **Table Details** section are selected. You can choose to import only specific tables that are relevant to your data processing needs. For example, to import customer data, select tables containing details like name, email, address, and contact information. | Item | Description | | :---------- | :-------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data | | Metadata | Loads Metadata | | File name | Displays the name of the file that you imported. | | Entity name | Displays the imported table name by default. You can rename it if required. | 4. Please scroll down to the bottom of the page to ensure all required tables are selected. Alt image 5. Click **Save** to apply the changes. Alt image Congratulations ! for saving the SQL Server connector dataflow, which appears on the **Dataflow > Imports** page. ## Run, Edit, and Delete the imported data Once the table is imported, you can execute, modify, and remove the imported table from the Dataflow. Follow the below steps: 1. Go to the **Dataflow** > **Imports** page. Alt image | Item | Description | | :---------------- | :------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Connector Name | Displays connector name. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables imported. | | Created Date | Displays date of creation. | | Last Refresh Type | Displays the refresh value: Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Actions | Provides multiple options for managing dataflows. | 2. Select the horizontal ellipsis under the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the dataflow | Select **Run history**. | 3. Click **Run** to execute the dataflow. Once the execution is successful, the data pipeline status will update to **Completed**, as illustrated in the figure below. Alt image In the Dataflow's **Run History Description,** you can view error messages related to data import failures from a data source. Additionally, you can check the status, start time, and end time of the data pipeline execution. ## Next step After completing the data import, start the [Master Data Management (MDM) - Resolve](https://docs.skypointcloud.com/docs/stitch.html) process to create a single, unified view of the data. With Skypoint MDM, you can ensure that your data is accurate, consistent, and reliable, making it easier to use for business processes, analytics, and reporting. # Source: https://docs.skypoint.ai/docs/square # Connecting to Square # Import data ## Overview  Square is a financial service, merchant services aggregator, and mobile payments company. You can use skyData's built-in connector for importing data from Square. This document will guide you through the process of connecting Square to skyData. ## Prerequisite  You must have following details to configure and import data using Square: * [Square Account](https://squareup.com/login) * URL * API key. You can refer to the Square Setup Guide document to learn more about the Prerequisites. ## Import data using Square connector  Follow below steps to create a new dataflow for Square import connector: 1. Go to **Dataflow** > **Imports**. 2. Click **New Dataflow**. The **Set dataflow name** page appears.  Alt text  3. Enter a **Name** to identify your Dataflow. The name must start with a letter and can only include letters and numbers, with no spaces between them.  4. Click **Next**.  Alt text ## To add Square connector  1. In the **Choose connector** page, select **Square** connector. You can use the Search feature to locate the connector. The **Square** connector is also available under the **Ecommerce** category. Alt text 2. Enter the **Display Name** to identify your Dataflow.  3. In the **Description** box, you can enter an explanation of the connector.   4. Click **Next**.  Alt text ## To configure Square Follow the below steps to configure the connection to Square: 1. Enter your credentials such as **API Key** and **URL** to configure with the Square connector. 2. Click **Connect**.  Alt text 3. After configuration, you can enter the **Table Details** to process the data. In the **Table Details** column, select only the data fields you want to import and process. | Item  | Description  | | :------------------------ | :----------------------------------------------------------- | | Purpose  | Option to assign a purpose (Data, Metadata) for each table.  | |
Data
| Loads customer data.  | |
Metadata
| Loads Metadata.  | | Name | Displays the file name that you have imported.  | | Table Name  | Displays the imported table name.  | 4. Click **Save** to apply the changes.  ## Run, edit, and delete the imported data  Once you save the connector, the **Square** connector gets displayed in the list of tables created in the Dataflow page.  Alt text  | Item | Description | | :------------ | :------------------------------------------------------------------------------- | | Name  | Displays the name of the Dataflow.  | | Type  | Displays connector type symbol.  | | Status  | Indicates whether the data is imported successfully.  | | Tables Count  | Displays the number of tables.  | | Created Date  | Displays date of creation.  | | Updated Date  | Displays last modified date.  | | Last Refresh  | Displays the latest refresh date, which updates each time you refresh the data.  | | Group by  | Option to view the items in a specific Group (For example, name, type, status).  | * Select the horizontal ellipsis in the **Actions** column and do the following: | If you want to | Then | | :------------------------------------ | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow  | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow  | Select **Run**.  | | Bring the data to its previous state  | Select **Rollback**.  | | Delete the Dataflow  | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow  | Select **Run History**.  | In the Dataflow, you can view the error message for data import failures from a data source under **Run History** > **Description**. # Setup guide ## Overview This document will help you gather credentials for connecting Square with skyData. ## Prerequisite You must have an active Square account. ## To find your App Below steps will guide you navigate on Square Developer Dashboard: 1. Log in to your [Square](https://squareup.com/login) account. 2. Scroll down the **All products** list. 3. Go to **Settings > App integrations**. Alt text 4. Click **Manage** in **My Applications** page. Alt text The **Application page** appears. Alt text ## Finding the API key 1. Click **Credentials** in the application list. 2. Click **Show** on **Access token** text area to find the API key. Alt text A Personal Access Token grants access to all your Square information and should be kept confidential. ## Locating the URL 1. Go to **Application page > OAuth**. 2. Enter [https://connect.squareup.com](https://connect.squareup.com) in the **Redirect URL** text area. Alt text 3. Click **Save**. ## Next step  After completing the data import, start the [Master Data Management (MDM) - Stitch process](stitch.md) to develop a unified view of your customers. # ERD SQUARE ERD # Source: https://docs.skypoint.ai/docs/stripe # Connecting to the Stripe connector ## Overview Stripe is an online payment processing platform that offers payment processing software and API to manage flow of funds for e-commerce websites and mobile applications. You can use skyData's built-in connector for importing data from Stripe. Our platform collects, analyzes, and provides a solution to transform data into meaningful information for generating valuable insights. ## Prerequisite * You have a [stripe account](https://dashboard.stripe.com/login) and credentials such as API Key. Each API user has credentials that must be used to authenticate requests. ## To import Data using the Stripe connector 1. In the left pane, go to **Dataflow** > **Imports**. The Dataflow window appears. Alt text 2. Click **New Dataflow** to create dataflow. Alt text 3. Enter a **Name** to identify your Dataflow. The name must start with a letter and can only include letters and numbers, with no spaces between them. 4. Click **Next**. ## Add Stripe Connector 1. In the **All categories** or **Services and apps** tab, select the **stripe** connector to start the configuration. Alt text 2. Also, you can use the **Search** function to find the **stripe** connector. Alt text 3. In the **Set dataflow name** page, enter the **Display Name** to identify your dataflow. 4. In the **Description** box, you can enter an explanation of the connector. 5. Click **Next**. Alt text ## To configure Stripe Follow the below steps to configure the connection to Stripe: 1. Enter the **Api Key** to configure with the Stripe connector. For details about the API key, click [API reference](https://stripe.com/docs/api). 2. Click **Connect**. Alt text 3. You can enter the **Table Details** to process the data. After configuration, all tables that are part of the connector are listed in the **Table Details** section. By default, it displays a single table upon successful connection, showing information specific to the client connector and the tables it supports. For multiple tables, you can select only those tables you want to import and process. For example, to import customer data, choose the tables that contain customer information such as name, email, address, and contact details.
| Item | Description | | :------------------------ | :---------------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (All, Customer 360, or Privacy-Metadata) for each table. | |
Data
| Loads customer data | |
Metadata
| Loads Metadata | | File Name | Displays the name of the file that you imported. | | Table Name | Displays the imported table name. | 4. Click **Save** to apply the changes. ## Run, edit, and delete the imported data 1. After saving the connection, the Stripe connector appears on the Dataflow page. Also, you can see the list of created tables in the [Databases](https://skypointcdpdocs.z22.web.core.windows.net/docs/entities.html) section. Alt text | Item | Description | | :----------- | :------------------------------------------------------------------------------- | | Name | Displays the name of the Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables. | | Created Date | Displays date of creation. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data.. | | Group by | Option to view the items in a specific Group (For example, name, type, status). | 2. Select the horizontal ellipsis in the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run History**. | You can view the error message for data import failures from a data source in the Dataflow under **Run History** > **Description**. ## Next step After completing the data import, start the [Master Data Management (MDM) - Stitch process](stitch.md) to develop a unified view of your customers. # Source: https://docs.skypoint.ai/docs/tels # TELS ## Overview The TELS connector integrates the TELS senior living management platform with skyData, bringing facilities and organizational data into your unified data environment. TELS is a management platform designed specifically for senior living communities, providing tools for facilities management, work order tracking, and organizational operations. By connecting TELS to skyData, organizations gain a consolidated view of their property operations alongside clinical, workforce, and financial data. ## What Data We Extract The TELS connector brings in key operational data including: * **Facilities** — Property records, building details, and community information * **Work Orders** — Maintenance requests, task tracking, and completion status * **Organizational Structure** — Departments, reporting hierarchies, and community relationships ## Why It Matters Senior living communities rely on well-maintained facilities to deliver quality care and resident satisfaction. Connecting TELS to skyData enables organizations to: * Combine facilities management data with clinical and resident satisfaction metrics * Track work order trends across communities to identify systemic maintenance issues * Align organizational structure data with workforce and operational analytics * Build comprehensive community performance dashboards that include facilities health ## Who Benefits | Role | Value | | :------------------- | :--------------------------------------------------------------------------- | | Facilities Directors | Cross-community visibility into work orders and maintenance operations | | Regional Directors | Organizational performance analytics combining facilities with clinical data | | Operations Leaders | Unified view of property operations across the portfolio | ## Get Started Contact your Skypoint representative to configure the TELS connector for your organization. # Source: https://docs.skypoint.ai/docs/tenant # Tenants ## Overview The Tenants module provides administration capabilities for managing tenant-level configurations in skyData. A tenant represents your organization's dedicated environment with its own storage, search services, and database configurations. ## Key Capabilities ### Tenant Creation Set up new tenants with customized configurations for data storage and platform services. Each tenant receives a unique identifier for mapping and reference purposes. ### Storage Configuration Configure Azure Data Lake Gen2 settings for data storage: * **Default Settings** - Use skyData-managed storage with default configurations * **Custom Configuration** - Connect your own Azure Data Lake Gen2 account with custom credentials ### Azure AI Search Integration Configure search capabilities for your tenant: * **Default Settings** - Use skyData-managed AI Search services * **Custom Configuration** - Connect your own Azure AI Search service with custom credentials ### Database Settings Configure Cosmos DB settings for tenant operations: * **Default Settings** - Use skyData-managed database services * **Custom Configuration** - Connect your own Cosmos DB instance with custom endpoints and keys ### Vector Database Configuration Set up Astra DB for vector storage and retrieval: * **Default Settings** - Use skyData-managed vector database * **Custom Configuration** - Connect your private Astra DB with custom credentials ## Configuration Options Each service can be configured with default skyData-managed settings or custom configurations: | Service | Default Option | Custom Configuration | | -------------------- | ------------------ | ---------------------------- | | Azure Data Lake Gen2 | Managed by skyData | Your own storage account | | Azure AI Search | Managed by skyData | Your own search service | | Cosmos DB | Managed by skyData | Your own database instance | | Astra DB | Managed by skyData | Your private vector database | ## Business Benefits * **Flexibility** - Choose between managed services or bring your own infrastructure * **Data Sovereignty** - Maintain control over data storage locations * **Integration** - Connect with existing Azure and database investments * **Scalability** - Scale storage and services based on your requirements ## Who Benefits * **IT Administrators** - Configure and manage infrastructure settings * **Security Teams** - Ensure data storage meets compliance requirements * **Cloud Architects** - Integrate skyData with existing cloud infrastructure * **Operations Teams** - Manage tenant-level configurations and services # Source: https://docs.skypoint.ai/docs/throttling # Throttling & Rate Limits Throttling is the process responsible for regulating the rate at which the application processing is conducted. Throttling is a way to practically implement rate-limiting. It lets API developers control how their API is used by setting up a temporary state, allowing the API to assess each request. Rate Limits refers to controlling the amount of incoming and outgoing traffic to or from a network. In this case, the rate limit for our API is set to be ***2000 requests/sec***. If the number of requests get above the threshold, then the requests after the threshold will get an error message responding that the request limit has reached. # Source: https://docs.skypoint.ai/docs/transformations # Transformations ## Overview skyData platform integrates with [dbt Core](https://docs.getdbt.com/docs/introduction) for data transformation. dbt (data build tool) is an open-source command-line tool that helps build, test, and maintain data infrastructure. dbt allows users to define their data models using SQL that can be run against a data warehouse or other data storage system and build a maintainable and scalable data infrastructure that can be easily updated and extended over time. The transformations are defined using SQL code stored in `.sql` files and organized into a directory structure within a dbt project. You can transform your data by writing SQL `SELECT` statements (also known as "dbt models") in a Git repository. You can create relationships and dependencies between models, and then materialize those models as tables. ## Data transformation in Lakehouse SQL skyData allows tenants to create transformed tables based on the table imported or generated by the platform and use them for analysis or other downstream uses. It follows multi-tenant architecture with each tenant consisting of one or more instances, allowing one dbt git repository per tenant and one branch of the repository per instance. This allows users to add/modify models in a pre-configured branch that isolates each instance's models. skyData platform is integrated with dbt Core and built on top of the Lakehouse SQL warehouse to provide a centralized environment for collaborative data transformation. skyData-powered transformations allow data teams to organize and analyze data without sacrificing data quality or analytical flexibility. This can involve a wide range of activities, including data cleaning, data standardization, data aggregation, data enrichment, and more. Features of skyData integration with dbt Core are as follows: * Offer a managed and cloud-hosted service to run the transformation queries and create tables in your Lakehouse. * Integrate with any git repository that hosts a valid dbt project. * Run models when data changes or on a periodic schedule. * Manage schedules within skyData Cloud. * View the current status and historical logs of each dbt transformation within skyData. # Source: https://docs.skypoint.ai/docs/transformations-dbtcore # Transforming data with dbt Core skyData connects to your Git repository and runs your dbt models in your destination. Integrated with dbt Core, skyData allows you to use SQL queries to perform a wide range of data transformations, including aggregation, filtering, and data cleaning. ## Prerequisite You must fulfil the following prerequisites to proceed: * An existing dbt repository. * dbt models for data transformation. Once you have defined your models, you can use the transformations in skyData Studio to run the SQL queries and create tables in your data warehouse. If you don’t have an existing repository, skyData will create a repository for you in Azure DevOps. ## To set up transformations Follow the below steps to begin transforming your data via SQL: 1. Go to **Dataflow > Transformations**. Alt image 2. Click **Setup**. Alt image 3. On the **Configuration** page, find the public key and click the clipboard icon to copy it. You will need it to grant Skypoint SSH access to your Git repository. ## Connect to the Git repository You must have a dbt project in a Git repository. Follow the instructions for Git provider to grant Skypoint SSH access: * For GitHub: See and follow the instructions for [Managing deploy keys](https://docs.github.com/en/developers/overview/managing-deploy-keys#deploy-keys). * For BitBucket: See and follow the instructions to [Configure SSH and two-step verification](https://support.atlassian.com/bitbucket-cloud/docs/configure-ssh-and-two-step-verification/). * For GitLab: See and follow the instructions to [Deploy keys](https://docs.gitlab.com/ee/user/project/deploy_keys/index.html#project-deploy-keys). Follow the below steps to validate credentials and connect with your Azure Git repository: 1. Open your Git repository. 2. In the **User settings** section, go to **SSH public keys** > **New Key** and paste the skyData public key in the **Public Key Data** field to synchronize your dbt project in the Git repository with skyData. 3. On the Git repository main page, click **Clone**. 4. Select the **SSH** command line and copy the repository **URL**. Follow the below steps to verify the directory structure and define transformations in your dbt project: 1. Navigate to your dbt project directory and open the `dbt\_project.yml` file. You can define the directories of the dbt project and project configurations such as name, version, profile, etc. For example, the `model-paths` setting specifies the directories where dbt should look for model files. ``` model-paths: ["models"] analysis-paths: ["analyses"] test-paths: ["tests"] seed-paths: ["seeds"] macro-paths: ["macros"] snapshot-paths: ["snapshots"] ``` 2. Open a `model` directory in your dbt project and define your data transformation models using SQL. Models are primarily written as a `SELECT` statement and saved as a `.sql` file. For example, the `{{config(materialized='table')}}` directive in dbt is used to specify the materialization type table for a model. ``` {{ config(materialized='table') }} with source_data as ( select 1 as id union all select null as id ) select * from source_data ``` ## Finish configuration 1. Enter your Git **Repository URL**. The repository location you specify should contain a model's directory from where dbt will look for your models by default. Once you have specified the location of your repository, you can create and maintain your dbt models within it. When you run transformations from skyData Studio, it will use the models in your repository to build your data model. 2. Enter your **Git branch**. The **Connection tests** pop-up appears. Alt image 3. Click **Done**. 4. Once the sync with Git is completed, models from Git repo will get listed on the **Transformations** page. ## Run transformation models 1. Go to **Dataflow > Transformations**. Alt image 2. Click **Run** to execute the desired **Output model name**. 3. If you want to disable the dbt model, select your model and click **Disable** under the horizontal ellipsis in the **Actions** column. Alt image 4. Click on the **DBT Tests** tab under the Transformations section to view all DBT test results, including totals, pass, fail status and detailed logs. 5. Click on the **Run all tests** button to execute all DBT tests. Alt image Disabling a dbt model does not delete it from the system. It controls whether the record is available for use in transformations. You can always re-enable a disabled dbt model if needed. 4. Select **Run history** to view all the states for transformation and the operation duration for the pipeline completion. 5. Once the transformation is executed successfully, you can view the transformation tables in the **Bronze** tab under **Lakehouse > Explorer**. # Source: https://docs.skypoint.ai/docs/triggers # Triggers Triggers are actually what causes a function/flow to run. A trigger defines how a function/flow is invoked taking into consideration the conditions behind it. For skyData connector, we have 2 different types of triggers available for the flow. They are: 1. **Endpoint for notifications when a dataflow refresh is completed** : This is a trigger that notifies the user when a dataflow refresh is completed. This trigger helps a lot in automatic updation processes where there is a need for daily updates for dataflow refresh and this feature can automate the process of notifying the user when its done. 2. **Endpoint for notifications when a dataflow refresh is failed** : This is a trigger that notifies the user when a dataflow refresh has failed. This trigger helps a lot in automatic updation processes where there is a need for daily updates for dataflow refresh whether it has worked or not, and this feature can automate the process of notifying the user when its failed. # Source: https://docs.skypoint.ai/docs/ukgpro # Connecting to UKG PRO UKG Pro is an all-in-one Human Capital Management (HCM) platform that enables businesses to streamline HR, payroll, talent acquisition, and workforce management within a single, unified solution. The API-based UKG Pro Connector integrates workforce and HR data into the skyData Platform, enabling secure data transfer, customized reporting, and advanced data interaction through chat tools. This document will guide you through the process of connecting UKG PRO to the skyData. ## Prerequisite You need the following details to configure and import data using the UKGPRO⇗ connector: * Username * Password * UKG Host Name * US Customer Api Key * Default Activity Start Date ## To import data using the UKG PRO connector Follow the below steps to create and configure a new dataflow for the UKG PRO import connector: 1. Navigate to **Dataflow > Imports**. Alt image 2. Click **New dataflow** as indicated by an arrow. The **Set dataflow name** page appears. Alt image 3. Enter the desired name for the dataflow in the **Name** text field. 4. Click **Next** to proceed. The **Choose connector** page appears. Alt image ## Add UKG PRO connector 1. On the **Choose Connector** page, use the **Search** feature to locate and select the **UKG PRO** Connector. 2. Enter the **Display Name** for your dataflow in the provided text field. 3. Optionally, add a **Description** in the designated text area. Alt image 4. Click **Next** to proceed. The **Configuration** page appears. Alt image ## Connect to the UKG PRO account 1. Fill in the required details on the Configuration page. 2. Click **Connect**. Alt image Once the connection is established, the connector can be used to import data from UKG PRO tables. 3. Scroll down to the **Table Details** section, select the checkboxes for the tables you wish to import, and use the dropdown menu to label them as either **Data** or **Metadata.** Alt image By default, all tables in the **Table Details** section are selected. You can choose to import only specific tables that are relevant to your data processing needs. For example, to import customer data, select tables containing details like name, email, address, and contact information. | Item | Description | | :---------- | :-------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data | | Metadata | Loads Metadata | | File name | Displays the name of the file that you imported. | | Entity name | Displays the imported table name by default. You can rename it if required. | 4. Click **Save** to apply the changes. Alt image Congratulations ! for saving the UKG PRO connector dataflow, which appears on the **Dataflow > Imports** page. ## Run, Edit, and Delete the imported data Once the table is imported, you can execute, modify, and remove the imported table from the Dataflow. Follow the below steps: 1. Go to the **Dataflow** > **Imports** page. Alt image | Item | Description | | :---------------- | :------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Connector Name | Displays connector name. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables imported. | | Created Date | Displays date of creation. | | Last Refresh Type | Displays the refresh value: Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Actions | Provides multiple options for managing dataflows. | 2. Select the horizontal ellipsis under the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the dataflow | Select **Run history**. | 3. Click **Run** to execute the dataflow. Once the execution is successful, the data pipeline status will update to **Completed**, as illustrated in the figure below. Alt image In the Dataflow's **Run History Description,** you can view error messages related to data import failures from a data source. Additionally, you can check the status, start time, and end time of the data pipeline execution. ## Next step After completing the data import, start the [Master Data Management (MDM) - Resolve](https://docs.skypointcloud.com/docs/stitch.html) process to create a single, unified view of the data. With Skypoint MDM, you can ensure that your data is accurate, consistent, and reliable, making it easier to use for business processes, analytics, and reporting. # Source: https://docs.skypoint.ai/docs/ukgwfm # Connecting to UKG WFM UKG WFM offers a workforce optimization platform. This platform helps teams improve compliance, scheduling, HR service delivery, reporting and analytics, time and attendance. You can connect UKG WFM to skyData Studio to perform tasks like data ingestion, unification, transformation, and enrichment to gain insights from the data. Leverage skyData's built-in connector for importing data from UKG WFM, which supports incremental loads and multiple parallel calls to enable faster data updates, significantly reduce load times, and provide users with quicker access to up-to-date information for a more efficient workflow. This document will guide you through the process of connecting UKG WFM to the skyData. ## Prerequisite You need the following details to configure and import data using the UKG⇗ connector: * Username * Password * Client Id * Client Secret * UKG url * Default activity start date * Reload records from (days) ## To import data using the UKG WFM connector Follow the below steps to create and configure a new dataflow for the UKG WFM import connector: 1. Navigate to **Dataflow > Imports**. Alt image 2. Click **New dataflow** as indicated by an arrow. The **Set dataflow name** page appears. Alt image 3. Enter the desired name for the dataflow in the **Name** text field. 4. Click **Next** to proceed. The **Choose connector** page appears. Alt image ## Add UKG WFM connector 1. On the **Choose Connector** page, use the **Search** feature to locate and select the **UKG WFM** Connector. 2. Enter the **Display Name** for your dataflow in the provided text field. 3. Optionally, add a **Description** in the designated text area. Alt image 4. Click **Next** to proceed. The **Configuration** page appears. Alt image ## Connect to the UKG account 1. Fill in the required details on the Configuration page. 2. Click **Connect**. Alt image Once the connection is established, the connector can be used to import data from UKG WFM tables. 3. Scroll down to the **Table Details** section, select the checkboxes for the tables you wish to import, and use the dropdown menu to label them as either **Data** or **Metadata.** Alt image By default, all tables in the **Table Details** section are selected. You can choose to import only specific tables that are relevant to your data processing needs. For example, to import customer data, select tables containing details like name, email, address, and contact information. | Item | Description | | :---------- | :-------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data | | Metadata | Loads Metadata | | File name | Displays the name of the file that you imported. | | Entity name | Displays the imported table name by default. You can rename it if required. | 4. Click **Save** to apply the changes. Alt image Congratulations ! for saving the UKG WFM connector dataflow, which appears on the **Dataflow > Imports** page. ## Run, Edit, and Delete the imported data Once the table is imported, you can execute, modify, and remove the imported table from the Dataflow. Follow the below steps: 1. Go to the **Dataflow** > **Imports** page. Alt image | Item | Description | | :---------------- | :------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Connector Name | Displays connector name. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables imported. | | Created Date | Displays date of creation. | | Last Refresh Type | Displays the refresh value: Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Actions | Provides multiple options for managing dataflows. | 2. Select the horizontal ellipsis under the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the dataflow | Select **Run history**. | 3. Click **Run** to execute the dataflow. Once the execution is successful, the data pipeline status will update to **Completed**, as illustrated in the figure below. Alt image In the Dataflow's **Run History Description,** you can view error messages related to data import failures from a data source. Additionally, you can check the status, start time, and end time of the data pipeline execution. ## Next step After completing the data import, start the [Master Data Management (MDM) - Resolve](https://docs.skypointcloud.com/docs/stitch.html) process to create a single, unified view of the data. With Skypoint MDM, you can ensure that your data is accurate, consistent, and reliable, making it easier to use for business processes, analytics, and reporting. # Source: https://docs.skypoint.ai/docs/unitycatalog # Unity Catalog ## Overview skyData leverages Unity Catalog for centralized data governance, ensuring secure management of data and AI assets across Lakehouse SQL and multiple workspaces. It provides a structured framework for organizing, controlling, and monitoring data access, enhancing consistency, security, transparency, operational efficiency, and compliance across the organization. ## Key features of Unity Catalog: * **Centralized Access Control:** Administrators Can define data access policies in one place, ensuring uniform enforcement across all workspaces, simplifying permission management, and enhancing security. * **Standards-Compliant Security Model:** Utilizing standard ANSI SQL, Unity Catalog allows for familiar and straightforward permission management at various levels, including catalogs, schemas, tables, and views. * **Built-in Auditing and Data Lineage:** The system automatically captures detailed audit logs and tracks data asset creation and usage across languages and platforms, enhancing compliance and troubleshooting. * **Data Discovery:** Users can tag, document, and search for data assets easily, facilitating efficient data discovery and collaboration among teams. ## skyData - Unity Catalog model In skyData Lakehouse, the Unity Catalog metastore follows a three-tier structure, where catalogs contain schemas that organize data and AI assets such as tables and models. The metastore, catalogs, and schemas help structure and manage stored data while enabling seamless access and manipulation. The object model flows from the metastore to the table as follows: Alt image * **Metastore:** Metastore (`skypoint_metastore`) is a central repository that stores metadata about the data stored in the Lakehouse SQL. Each metastore exposes a three-level namespace (`catalog.schema.table-volumes-views-functions-aimodels`) that organizes your data. #### Level One: * **Catalogs:** The first layer of Unity Catalog’s three-level namespace, which is used to organize your data assets. Each catalog can have any number of schemas, for example, `tenant_instance_main`. #### Level Two: * **Schemas:** Schemas are the second layer of the object hierarchy that contain a set of related objects, such as tables, volumes, functions, AI Models and views. Each schema can have any number of tables. For example, bronze, silver, and gold. #### Level Three: * **Table:** The lowest level in the object hierarchy, a table is a collection of data that is organized into rows and columns. For example, profiles, audience, metrics, predictions, etc. * **Volumes:** Volumes store unstructured, non-tabular data in cloud storage. They can be managed, where Unity Catalog controls data lifecycle, or external, where it manages access within Azure Databricks but not external clients. * **Views:** Views are stored queries that retrieve data from one or more tables. * **Functions:** These are predefined logic units that return either a single scalar value or a set of rows. * **AI Models:** AI models, integrated with MLflow, are registered in Unity Catalog as callable functions. ### **Workspace-Catalog Binding & Privileges in SkyPoint AI** Unity Catalog in skyData enables workspace-catalog binding, allowing controlled data access across workspaces. By default, catalogs are shared, but admins can **restrict access** for **data isolation, security, and compliance**, ensuring data is processed only in designated environments. When a workspace is enabled for Unity Catalog, it is automatically attached to a metastore, and a workspace catalog is created. **Workspace admins** are the default owners, managing privileges for the catalog and its child objects. All **workspace users** receive the **USE CATALOG** privilege, along with **USE SCHEMA, CREATE TABLE, CREATE VOLUME, CREATE MODEL, CREATE FUNCTION,** and **CREATE MATERIALIZED VIEW** privileges on the **default schema** within the catalog. This setup ensures structured governance, controlled access, and seamless data management within skyData. # Source: https://docs.skypoint.ai/docs/welcomehome # Connecting to WelcomeHome WelcomeHome offers a customized CRM platform designed specifically for senior living operators. This platform helps sales and marketing teams improve conversion rates and community economics. You can connect the WelcomeHome to skyData Studio and perform various tasks such as data ingestion, unification, transformation, and enrichment to gain insights from the data. You can use SkyPoint AI's built-in connector for importing data from WelcomeHome. This document will guide you through the process of connecting WelcomeHome to the skyData. ## Prerequisite You need the following details to configure and import data using the WelcomeHome⇗ connector: * API Token * Activity Start Date ## To import data using the WelcomeHome connector Follow the below steps to create and configure a new dataflow for the WelcomeHome import connector: 1. Go to **Dataflow > Imports**. 2. Click **New dataflow**. The **Set dataflow name** page appears. Alt image 3. Enter the dataflow name in the **Name** text area. 4. Click **Next**. The **Choose connector** page appears. Alt image ## Add WelcomeHome connector 1. In the **Choose connector** page, select **WelcomeHome** connector. You can use the **Search** feature to find the connector. Also, the WelcomeHome connector can be found under the **Cloud** category. Alt image 2. Enter the **Display Name** for your dataflow in the text area. 3. You can add a **Description** in the text area. 4. Click **Next**. The **Configuration** page appears. Alt image ## Connect to the WelcomeHome account Log in to your WelcomeHome account using your username and password. If you do not have an account, you can create one by visiting the WelcomeHome #x21D7; website. 1. Enter API **Token** the unique identifier assigned to your application. 2. Click **Connect**. Once the connection is established, you can use the connector to import data from a table in WelcomeHome. Alt image 3. In the **Table details** section, click the checkbox for the tables you wish to import, then use the dropdown to mark them as either **Data** or **Metadata**. In the **Table Details** section, by default, all tables are selected. You can mark only those tables that you want to import and process the data. For example, to import customer data, you can check those tables which contain customer information, such as name, email, address, and contact details. | Item | Description | | :---------- | :-------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data | | Metadata | Loads Metadata | | File name | Displays the name of the file that you imported. | | Entity name | Displays the imported table name by default. You can rename it if required. | 5. Click **Save** to apply the changes. After saving the connection, the WelcomeHome connector appears on the **Dataflow** > **Imports** page. ## Run, edit, and delete the imported data Once the table is imported, you can execute, modify, and remove the imported table from the Dataflow. Follow the below steps: 1. Go to the **Dataflow** > **Imports** page. Alt image | Item | Description | | :---------------- | :------------------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables count | Displays the number of tables imported. | | Created Date | Displays date of creation. | | Last refresh type | Displays the refresh value: Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Group by | Option to view the items in a specific group. For example, type, status, tables count, etc. | 2. Select the horizontal ellipsis under the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run history**. | You can see the error message corresponding to failure while importing data from a data source in the Dataflow under **Run History** > **Description**. Also, you can see the status, start time, and end time for the execution of the data pipeline. ## Next step After completing the data import, start the [Master Data Management (MDM) - Resolve](https://docs.skypointcloud.com/docs/stitch.html) process to create a single, unified view of the data. With SkyPoint MDM, you can ensure that your data is accurate, consistent, and reliable, making it easier to use for business processes, analytics, and reporting. # Source: https://docs.skypoint.ai/docs/workday # Connecting to Workday Workday is a cloud-based enterprise platform offering integrated solutions for HCM, finance, payroll, and planning, helping mid-to-large enterprises streamline operations across departments. Workday integrates seamlessly with skyData Platform (AIP) through its built-in connector, enabling intelligent automation, predictive analytics, and real-time insights. This streamlines workflows, ensures compliance, and empowers organizations to boost efficiency and make smarter decisions. This document will guide you through the process of connecting Workday to the skyData. ## Prerequisite You need the following details to configure and import data using the Workday⇗ connector: * Username * Password * Workday URL * Company Reference Id * Default Activity Start Date ## To import data using the Workday connector Follow the below steps to create and configure a new dataflow for the Workday import connector: 1. Go to **Dataflow > Imports**. Alt image 2. Click **New dataflow** (indicated by an arrow). The **Set dataflow name** page appears. Alt image 3. Enter the dataflow name in the **Name** text area. 4. Click **Next** to proceed. The **Choose connector** page appears. Alt image ## Add Workday connector 1. On the **Choose Connector page**, use the Search feature to locate and select the **Workday** Connector. Alt image 2. Enter the **Display Name** for your dataflow in the text area. 3. You can add a **Description** in the text area. 4. Click **Next** to proceed. The **Configuration** page appears. Alt image ## Connect to the Workday account 1. Fill in the required details on the **Configuration page**. 2. Click **Connect**. Once the connection is established, you can use the connector to import data from a table in Workday. Alt image 3. Scroll down to the **Table Details** section, select the checkboxes for the tables you wish to import, and use the dropdown menu to label them as either **Data** or **Metadata**. Alt image In the **Table Details** section, by default, all tables are selected. You can mark only those tables that you want to import and process the data. For example, to import customer data, you can check those tables which contain customer information, such as name, email, address, and contact details. | Item | Description | | :---------- | :-------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data | | Metadata | Loads Metadata | | File name | Displays the name of the file that you imported. | | Entity name | Displays the imported table name by default. You can rename it if required. | 5. Click **Save** to apply the changes. Alt image After saving the connection, the Workday connector appears on the **Dataflow** > **Imports** page. ## Run, edit, and delete the imported data Once the table is imported, you can execute, modify, and remove the imported table from the Dataflow. Follow the below steps: 1. Go to the **Dataflow** > **Imports** page. Alt image | Item | Description | | :---------------- | :------------------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables count | Displays the number of tables imported. | | Created Date | Displays date of creation. | | Last refresh type | Displays the refresh value: Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Group by | Option to view the items in a specific group. For example, type, status, tables count, etc. | 2. Select the horizontal ellipsis under the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run history**. | Alt image You can see the error message corresponding to failure while importing data from a data source in the Dataflow under **Run History** > **Description**. Also, you can see the status, start time, and end time for the execution of the data pipeline. ## Next step After completing the data import, start the [Master Data Management (MDM) - Resolve](https://docs.skypointcloud.com/docs/stitch.html) process to create a single, unified view of the data. With SkyPoint MDM, you can ensure that your data is accurate, consistent, and reliable, making it easier to use for business processes, analytics, and reporting. # Source: https://docs.skypoint.ai/docs/workdayRaaS # Connecting to Workday RaaS Workday RaaS is a cloud-based ERP system for finance, HR, and planning. Its Report-as-a-Service (RaaS) feature enables automated, scheduled report delivery and unlimited custom report creation for seamless data integration. Workday RaaS integrates seamlessly with skyData Platform (AIP) through its built-in connector, enabling intelligent automation, predictive analytics, and real-time insights. This streamlines workflows, ensures compliance, and empowers organizations to boost efficiency and make smarter decisions. This document will guide you through the process of connecting Workday RaaS to the skyData. ## Prerequisite You need the following details to configure and import data using the Workday RaaS⇗ connector: * Username * Password * Workday Base URL * Workday Report Name * Query parameters ## To import data using the Workday RaaS connector Follow the below steps to create and configure a new dataflow for the Workday RaaS import connector: 1. Go to **Dataflow > Imports**. Alt image 2. Click **New dataflow** (indicated by an arrow). The **Set dataflow name** page appears. Alt image 3. Enter the dataflow name in the **Name** text area. 4. Click **Next** to proceed. The **Choose connector** page appears. Alt image ## Add Workday RaaS connector 1. On the **Choose Connector page**, use the Search feature to locate and select the **Workday RaaS** Connector. Alt image 2. Enter the **Display Name** for your dataflow in the text area. 3. You can add a **Description** in the text area. 4. Click **Next** to proceed. The **Configuration** page appears. Alt image ## Connect to the Workday RaaS account 1. Fill in the required details on the **Configuration page**. 2. Click **Connect**. Once the connection is established, you can use the connector to import data from a table in Workday RaaS. Alt image 3. Scroll down to the **Table Details** section, select the checkboxes for the tables you wish to import, and use the dropdown menu to label them as either **Data** or **Metadata**. Alt image In the **Table Details** section, by default, all tables are selected. You can mark only those tables that you want to import and process the data. For example, to import customer data, you can check those tables which contain customer information, such as name, email, address, and contact details. | Item | Description | | :---------- | :-------------------------------------------------------------------------- | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data | | Metadata | Loads Metadata | | File name | Displays the name of the file that you imported. | | Entity name | Displays the imported table name by default. You can rename it if required. | 5. Click **Save** to apply the changes. Alt image After saving the connection, the Workday RaaS connector appears on the **Dataflow** > **Imports** page. ## Run, edit, and delete the imported data Once the table is imported, you can execute, modify, and remove the imported table from the Dataflow. Follow the below steps: 1. Go to the **Dataflow** > **Imports** page. Alt image | Item | Description | | :---------------- | :------------------------------------------------------------------------------------------ | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables count | Displays the number of tables imported. | | Created Date | Displays date of creation. | | Last refresh type | Displays the refresh value: Full or Incremental. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Group by | Option to view the items in a specific group. For example, type, status, tables count, etc. | 2. Select the horizontal ellipsis under the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run history**. | Alt image You can see the error message corresponding to failure while importing data from a data source in the Dataflow under **Run History** > **Description**. Also, you can see the status, start time, and end time for the execution of the data pipeline. ## Next step After completing the data import, start the [Master Data Management (MDM) - Resolve](https://docs.skypointcloud.com/docs/stitch.html) process to create a single, unified view of the data. With SkyPoint MDM, you can ensure that your data is accurate, consistent, and reliable, making it easier to use for business processes, analytics, and reporting. # Source: https://docs.skypoint.ai/docs/workspaces # Using skyData Workspaces ## Overview The Workspace module in skyData Studio allows users to view, configure, and manage workspaces linked to their instances. It provides secure access, promotes efficient resource utilization, and supports centralized workspace management across environments. With this module, users can: * Map a workspace to an instance. * Open the connected Databricks environment to manage workspaces. * Set up and access SQL credentials for their workspaces. ## To map new Workspaces Users can map a workspace to an instance that is already connected to an another instance within a tenant. 1. In the left pane, go to **Lakehouse** > **Workspaces**. Alt text The Workspaces window appears. 2. Click **Setup New Workspaces** to map workspaces. Alt text The setup workspace pop-up window appears. Alt text 3. Click on **form existing workspace**. Alt text 4. Select the workspace you want to map from the dropdown menu. Alt text 5. Click on **Map** to map the custom workspace to an instance. Alt text Only custom workspaces can be configured by users. Default workspaces are view-only and cannot be modified. ## To Access Databricks workspace from skyData studio To open the workspace in Databricks, directly from the skyData studio 1. Click on **arrow** icon present on workspace you want to access. This will re-direct you to the corresponding databricks workspace Alt text Default workspaces cannot be redirected to a Databricks workspace. Only custom workspaces support redirection to Databricks workspace. 2. Click on the workspace you want to access. 3. Workspace details page appears. 4. Click the **Open in Databricks** button located at the bottom-left corner. Alt text You will be redirected to the corresponding Databricks workspace Alt text ## To enable SQL Access 1. Access a **workspace** by clicking on its name. The **Workspace Details** window appears Alt text 2. Click **SQL Access** to enable the SQL connection. Alt text skyData supports **Refresh Hive Metastore** to reflect database schema changes made directly to the Lakehouse Delta Lake. With the click of the **Refresh Hive Metastore** button, the metadata gets refreshed to reflect the changes in Lakehouse Databases like listing new tables, new attributes, or removal of tables. 5. If necessary, do the following: | To | Do | | :------------------------------------------------------------------- | :---------------------------------------------------------------- | | View and copy the credentials for SQL Access | Click **Show fields** and copy the credentials to your clipboard. | | Initialize a cluster to quickly connect to Lakehouse and run queries | Click **Initialize Warehouse**. | 6. You need to obtain the following Lakehouse workspace SQL credentials: * Server hostname * Port * HTTP path * JDBC URL * Personal access token | Item | Description | | :-------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------- | | Server hostname | Indicates the root URL for Lakehouse workspace SQL endpoint. | | Port | Refers to the port in your cluster credentials. | | HTTP path | Refers to the Lakehouse SQL endpoint provisioned for the instance. | | JDBC URL | Indicates the driver that is used to connect to a database. It is a standard protocol for each Instance, which is used to connect with data sources. | | Personal access token | Contains your security credentials used to authenticate and connect to SQL endpoints. | Refresh personal access token generates a personal access token for accessing the lakehouse tables. Clicking the **Refresh** icon, you can set the default expiry time to 90 days. An alert message starts displaying 7 days prior to access token expiration. skyData is a multi-tenant Software as a Service (SaaS) platform where each customer is a tenant, and each tenant has one or more instances. You can create databases and tables in your Instance. Lakehouse SQL supports data integration at instance level. When you click the **Enable** button, by default, the system loads data into Databricks from **Lakehouse** > **Explorer** and creates credentials to authenticate and explore your data through Power BI, Tableau, etc. Lakehouse workspace SQL integrates with all JDBC compliant tools for BI and ELT/ETL use cases. Once you’ve enabled Lakehouse workspaces, you can begin visualizing and working with tools like Power BI or Tableau. Also, you can integrate your Lakehouse workspace endpoints and Databricks with more MDS tools such as Fivetran, Airbyte, Dbt, etc. The third-party data integration with Databricks helps you to centralize data from disparate data sources into Lakehouse. * To get started with third-party tools for data integration, click the **More MDS tools** tile. Lakehouse workspace SQL capability provides direct full SQL access to your instance-level Delta Lakehouse accessible to Instance Administrator and above roles. You can use any SQL ingestion, transformation, or visualization tools to interact bi-directionally with Lakehouse including Fivetran, Dbt, Power BI, Tableau, and Looker. Any external changes to the Lakehouse will be automatically synchronized using Delta Lake **Change Data Feed (CDF)** to update the skyData metadata store. ## See also [Tenants](tenant.md) [Instances](instance.md) [Explorer](entities.md) # Source: https://docs.skypoint.ai/docs/zenoti # Connecting to Zenoti ## Overview Zenoti provides an all-in-one, cloud-based software solution for the spa, salon and med spa industry. You can use skyData's built-in connector for importing data from Zenoti. skyData collects, analyzes, and provides a solution to transform data into meaningful information for generating valuable insights. ## Prerequisite You will need the following details to allow skyData access to your Zenoti account: * API Key * URL ## To add Zenoti connector Follow the below steps to create a new dataflow for the Zenoti import connector: 1. Go to **Dataflow > Imports**. 2. Click **New dataflow**. The **Set dataflow name** page appears. Alt text 3. In the **Set dataflow name** page, type **Dataflow name** in the **Name** text area. 4. Click **Next**. The **Choose connector** page appears. Alt text ## To choose Zenoti connector 1. In the **Choose connector** page, select **Zenoti** connector. Use the Search feature to find the connector. The **Zenoti** connector is also available in the **Marketing** category. The **Set dataflow name** page for **Zenoti** appears. Alt text 2. Enter **Display Name** for your dataflow in the text area. 3. Enter **Description** for your dataflow in the text area. 4. Click **Next**. Alt text ## To configure Zenoti Follow the below steps to configure the connection to Zenoti: 1. Select **FirstDate** to specify the date after which the data for appointments, collections, and sales will be retrieved. 2. Enter your credentials such as **API Key**, and **URL** to configure with Zenoti. 3. Click **Connect**. The **Table Details** columns appear. Alt text 4. Enter the **Table Details** to process the data. | Item | Description | | :--------- | :------------------------------------------------------------ | | Purpose | Option to assign a purpose (Data or Metadata) for each table. | | Data | Loads customer data. | | Metadata | Loads Metadata. | | Name | Displays the name of the file that you imported. | | Table Name | Displays the imported table name. | 5. Click **Save**. ## Run, edit, and delete the imported data Once you save the connector, the Zenoti connector gets displayed in the list of tables created in the Dataflow page. Alt text | Item | Description | | :---------------- | :------------------------------------------------------------------------------------------------------- | | Name | Displays the name of the imported Dataflow. | | Type | Displays connector type symbol. | | Status | Indicates whether the data is imported successfully. | | Tables Count | Displays the number of tables. | | Created Date | Displays date of creation. | | Last refresh type | Displays the refresh value. You can see if the value is Full or Incremental after the last data refresh. | | Updated Date | Displays last modified date. | | Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. | | Group by | Option to view the items in a specific Group (For example, name, type, status). | * Select the horizontal ellipsis in the **Actions** column and do the following: | If you want to | Then | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | Modify the Dataflow | Select **Edit** and modify the **Dataflow**. Click **Save** to apply your changes. | | Execute the Dataflow | Select **Run**. | | Bring the data to its previous state | Select **Rollback**. | | Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. | | See the run history of the Dataflow | Select **Run** history. | ## Next step After completing the data import, start the [Master Data Management (MDM) - Stitch](stitch.md) process to develop a unified view of your customers.