> ## Documentation Index
> Fetch the complete documentation index at: https://docs.skypoint.ai/llms.txt
> Use this file to discover all available pages before exploring further.

#  

# 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

<CardGroup cols={2}>
  <Card title="Apply Cleaning Rules" icon="broom">
    Configure rules to trim whitespace, standardize case, format phone numbers, and more.
  </Card>

  <Card title="Preview Changes" icon="eye">
    See exactly how your data will change before committing transformations.
  </Card>

  <Card title="Chain Multiple Operations" icon="link">
    Apply multiple cleaning operations in sequence for comprehensive data standardization.
  </Card>

  <Card title="Track Execution" icon="clock-rotate-left">
    Monitor cleaning jobs in real-time with progress updates and detailed logs.
  </Card>
</CardGroup>

## Cleaning Operations

skyMDM supports a comprehensive set of cleaning operations:

<CardGroup cols={3}>
  <Card title="Text Normalization" icon="text-size">
    Trim whitespace, convert case (upper, lower, title), remove special characters
  </Card>

  <Card title="Format Standardization" icon="phone">
    Standardize phone numbers, dates, and addresses to consistent formats
  </Card>

  <Card title="Value Replacement" icon="repeat">
    Replace specific values, handle nulls, and apply conditional transformations
  </Card>

  <Card title="Deduplication" icon="copy">
    Remove duplicate records based on configurable matching criteria
  </Card>

  <Card title="Email Validation" icon="envelope">
    Validate and standardize email address formats
  </Card>

  <Card title="Custom Rules" icon="wand-magic-sparkles">
    Define custom transformation logic for organization-specific requirements
  </Card>
</CardGroup>

## 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

<CardGroup cols={3}>
  <Card title="Higher Match Rates" icon="bullseye">
    Standardized data dramatically improves identity matching accuracy.
  </Card>

  <Card title="Reduced Manual Work" icon="robot">
    Automate repetitive data cleanup that would take analysts weeks.
  </Card>

  <Card title="Trusted Analytics" icon="chart-line">
    Clean data produces reliable reports and insights for decision-making.
  </Card>
</CardGroup>

## 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