Data Quality at Scale: Duplicates, Validation, and Bulk Loads
Summary
Managing data quality at scale requires a combination of deduplication match and duplicate rules to control record duplication, using external IDs for efficient upsert operations, and applying validation rules to enforce data integrity. For bulk data loads, it's best practice to test imports in a staging sandbox before proceeding, perform Data Loader upserts using external IDs, and conduct post-load reporting and spot checks. These strategies help ensure clean, validated data while handling large volumes, which is crucial for maintaining reliable Salesforce environments.
Takeaways
- Configure dedupe match and duplicate rules to manage record duplicates effectively.
- Use external IDs for upsert operations to improve bulk data load accuracy.
- Apply validation rules to enforce data quality, such as mandatory fields on stage changes.
- Perform bulk imports first in a staging sandbox to prevent data corruption.
- Run post-load reports and spot checks to verify data integrity after bulk loads.
Dedupe Match rules + Duplicate rules (block vs allow/report) Use external IDs for upserts Validation rules AND( ISPICKVAL(StageName, 220;Closed Won221;), ISBLANK( CloseDate ) ) Bulk imports Staging sandbox first Data Loader upsert with external IDs Post load reports + spot checks The post Data Quality at Scale: Duplicates, Validation, and Bulk Loads first appeared on Salesforce Buddy .