Data quality is not a cleanup project at the end. It begins with the model, entry points, duplicate strategy, ownership, and the decisions the data must support.
Prevent problems upstream
AuraForce’s popular duplicate-prevention article explored checking new and updated Account records with Flow. Today I apply the same intent with current platform capabilities: prevent bad states as close to their source as possible.
Foundation work
The work is tailored to the data that matters most.
- Object and relationship review
- Duplicate and validation strategy
- Migration mapping and reconciliation
- Reporting-ready definitions and ownership
The outcome
A smaller set of dependable fields and rules is more valuable than a larger model nobody fully trusts.