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
What You Can Do
Apply Cleaning Rules
Configure rules to trim whitespace, standardize case, format phone numbers, and more.
Preview Changes
See exactly how your data will change before committing transformations.
Chain Multiple Operations
Apply multiple cleaning operations in sequence for comprehensive data standardization.
Track Execution
Monitor cleaning jobs in real-time with progress updates and detailed logs.
Cleaning Operations
skyMDM supports a comprehensive set of cleaning operations:Text Normalization
Trim whitespace, convert case (upper, lower, title), remove special characters
Format Standardization
Standardize phone numbers, dates, and addresses to consistent formats
Value Replacement
Replace specific values, handle nulls, and apply conditional transformations
Deduplication
Remove duplicate records based on configurable matching criteria
Email Validation
Validate and standardize email address formats
Custom Rules
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
Higher Match Rates
Standardized data dramatically improves identity matching accuracy.
Reduced Manual Work
Automate repetitive data cleanup that would take analysts weeks.
Trusted Analytics
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