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
What You Can Do
Geographic Enrichment
Expand ZIP codes into full geographic context including city, state, county, and timezone.
Lookup Table Joins
Match your data against reference tables to add standardized codes and categories.
Choose a Geocoding Provider
Use the built-in reference data that ships with skyMDM, or point a rule at Google or Nominatim instead.
Control How Values Are Written
Set each rule to fill only empty cells or to overwrite every value, and order rules by priority.
Enable Rules Selectively
Switch individual enrichment rules on or off between runs without deleting their configuration.
Watch a Run in Progress
Follow live progress in the run window, cancel a run, and review the history of previous runs.
Enrichment Sources
Built-In Geocode Reference
US geocode reference data maintained by skyMDM — coordinates, city, and state from a ZIP or address
External Geocoders
Google or Nominatim as the geocoding provider when a rule needs coverage beyond the built-in reference
Phone Attributes
Reformat to E.164, national, or international form and extract the area code
Email Attributes
Normalize the address and split out the username and domain as their own columns
Address and State
Standardize state values and normalize ZIP codes, including restoring dropped leading zeros
Name Parsing
Break a full name into its component parts and normalize the result
Key Capabilities
Automated Enrichment Rules
Configure which source columns should be enriched and what additional data to append, using a guided rule builder. Rules are grouped by category and enrichment type, carry a priority so they apply in a predictable order, and can be enabled or disabled individually. Each rule names its own target columns, so enrichment adds fields without overwriting the source.Confidence Scoring
Enrichment matches include confidence scores, so you know how reliable each enriched value is.One Run at a Time
A table can only have one enrichment run in flight. Starting a second is blocked while the first is still executing, so two jobs cannot race to write the same enriched table.Enriched Output Flows Forward
Enrichment writes its own output table, kept separate from the cleaned table. Map and the stages after it read the enriched table, while Clean and Validate continue to work from the cleaned one, so added attributes reach matching without disturbing earlier stages.Runs Where Your Data Lives
Enrichment executes natively on Databricks, Snowflake, or Microsoft Fabric, using the same rules regardless of platform.Audit Trail
Track which records were enriched, what sources were used, and when enrichment occurred for compliance and debugging.Business Impact
Better Matching
Enriched data provides more attributes for accurate identity resolution.
Richer Analytics
Geographic and demographic data enables population health insights.
Operational Efficiency
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