Data Quality Audit Framework

Dataset: Customer CRM database. Issues suspected: Duplicates, incomplete records, outdated information. Size: 500K records.

## Data Quality Audit Report\n\n**Audit Dimensions**\n\n| Dimension | Check | Priority |\n|-----------|-------|----------|\n| Completeness | Required fields populated | High |\n| Uniqueness | Duplicate detection | High |\n| Accuracy | Valid formats/ranges | Medium |\n| Timeliness | Record freshness | Medium |\n| Consistency | Cross-field validation | Low |\n\n**Completeness Checks**\n```sql\n-- Find records missing critical fields\nSELECT \n COUNT(*) as total,\n SUM(CASE WHEN email IS NULL THEN 1 ELSE 0 END) as missing_email,\n SUM(CASE WHEN phone IS NULL THEN 1 ELSE 0 END) as missing_phone\nFROM customers;\n```\n\n**Duplicate Detection**\n```sql\n-- Find potential duplicates\nSELECT email, COUNT(*) as count\nFROM customers\nGROUP BY email\nHAVING COUNT(*) > 1;\n```\n\n**Remediation Plan**\n\n1. **Week 1**: Run all quality checks, quantify issues\n2. **Week 2**: Deduplicate (merge strategy)\n3. **Week 3**: Enrich incomplete records\n4. **Week 4**: Implement validation rules\n\n**Ongoing Governance**\n- Weekly quality score dashboard\n- Input validation at source\n- Quarterly full audits