Data Quality Assurance
Quality checks are used to reduce obvious data defects before publication. Checks do not guarantee correctness in all contexts; they are practical controls with known limits.
Quality dimensions
- Completeness: required fields present.
- Consistency: values align across related records.
- Validity: values match expected formats and ranges.
- Timeliness: update cadence matches stated policy.
Validation workflow
- Run schema-level checks.
- Run rule-based cross-field checks.
- Run spot reviews on sampled records.
- Record exceptions and remediation path.
Exception handling
Known issues are tracked in System Status Notes and resolved entries are listed in Change Log. Material corrections are announced under Correction Policy.