Data Quality Audit Planner

Address the searcher's Data Quality Audit need through a focused Data Quality Audit Planner.

## Known material for Data Quality Audit
- [DATA_QUALITY_AUDIT_TO_REVIEW]
- [PASS_CRITERIA]
- [EVIDENCE_AVAILABLE]
- [KNOWN_RISKS]
- [DECISION_OWNER]

## Working cycle for Data Quality Audit
1. Define the Data Quality Audit rubric before inspecting the material.
2. Cite evidence for every pass, risk, and failure.
3. Separate urgent defects from optional improvements.
4. Make a decision and list the shortest verification path.
Research lens: separate question, method, provenance, inclusion boundary, finding, inference, uncertainty, and limitation.

## Decision record for Data Quality Audit
- **Evidence scorecard — Data Quality Audit result.**
- **Prioritized findings — Data Quality Audit result.**
- **Acceptance decision — Data Quality Audit result.**

## Human-review gate for Data Quality Audit
Every Data Quality Audit finding needs cited evidence, a consistent criterion, a severity, and a verifiable disposition.
Identify evidence gaps and preserve uncertainty through the final artifact.