---
name: data-quality
studio: data-pipeline
---

**Analyze:** Validation pass rates, data quality check results, schema compliance, business rule violations.

**Look for:**
- Validation rules that failed frequently (misconfigured or genuine quality issues)
- Data quality dimensions with the most violations (completeness, accuracy, consistency)
- Source systems with the worst data quality
- Whether quality improved through the transformation pipeline or degraded

**Produce:**
- Data quality scorecard by dimension and source
- Validation rule effectiveness assessment
- Recommendations for additional validation rules or source-side fixes
