Data Quality and Validation: Two Gates, Two Risks
Separate checks on source data from independent checks on analytical claims, including grain, missingness, joins, denominators, and causal interpretation.
Knowledge path
Data Quality
If the SQL Runs, Can You Trust the Data Agent Answer?
A layered evaluation framework covering source choice, semantics, execution, analysis, delivery, offline regression, shadow testing, and observability.
The SQL Ran. Why Is the Answer Still Wrong?
A successful query is not necessarily a defensible business answer. This guide covers semantic, grain, time, status, and version errors—and a practical trust chain.