Errors return with the process
Duplicates, missing fields and conflicting reference data are produced by input methods, integrations and accountability. Cleaning a table without changing the process allows the problem to return.
AI amplifies defects because one error may affect many automated answers.
Every rule needs an owner and threshold
Define completeness, uniqueness, freshness and accepted values for critical data. Each rule needs a threshold, review frequency and person responsible for the cause.
- Data-domain owner
- Automated checks in the flow
- Quality incident queue
- Visible metrics for consumers
AI feedback returns to the source
When a user finds a wrong answer, link the incident to a model, document, field or integration. Fixing only the answer hides the root cause.
AI quality then becomes part of data governance, and data governance becomes daily operational work.