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.
Search your entities and columns, then pick a label from a dropdown — no hand-typed column names.
Add labels your organization needs, each with its own description and encryption setting, from the Labels page.
Search the catalog, edit a label in place, and remove several labels at once with multi-select bulk delete.
Confirm the run, follow live status in the run window, and reopen the history of previous mapping runs.
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
skyMDM ships a built-in catalog of standard labels that every tenant starts with. Labels you create sit alongside it, clearly distinguished from the defaults, so an upgrade to the standard catalog never disturbs your own definitions.
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.
Sensitivity and Encryption Classification
Each label carries an encryption setting that marks how the underlying attribute should be handled. The Labels page summarizes the result across your data — how many sensitive attributes exist, how many are mapped, and how many are encrypted — giving privacy and compliance teams a single view of where protected data sits.
Mapping Validation
A label can only be assigned once per table. If the same label is applied to two columns, skyMDM names the conflict and blocks the save rather than letting an ambiguous mapping reach Match. Primary keys are chosen from a dropdown of eligible key columns only, and a stale primary key can always be cleared.
Cross-Source Alignment
View how labels are mapped across all your source tables to ensure consistency and identify gaps in your semantic model.
Runs Where Your Data Lives
Mapping executes natively on Databricks, Snowflake, or Microsoft Fabric, running per table and writing the mapped output back into your own catalog or lakehouse.
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