Data QualityBy Zapinner1 min read
CRM Data Quality Checklist
A practical checklist for auditing and maintaining CRM data quality — completeness, consistency, duplicates, validity, and the boundary controls that keep it clean.
Use this checklist to audit a CRM today and to keep it clean going forward. Each item maps to something you can measure or enforce — nothing vague.
Completeness
- Required fields (name, email, company) are populated on every record.
- Empty-string and whitespace-only values are treated as missing, not present.
- You've profiled completeness with the data-quality endpoint, not eyeballed it.
Consistency
- Company names follow one canonical form (no Acme / Acme Inc / ACME split).
- Phone numbers are stored in a single format (E.164).
- Dates use one format across all records.
- Emails are lowercased and trimmed.
Duplicates
- Near-duplicates (not just exact) are detected with fuzzy matching.
- A confidence threshold governs auto-merge vs. human review.
- Survivorship rules decide which value wins per field.
- New records are matched against existing ones at write time.
Validity
- Email and phone formats are validated before records are written.
- A validation boundary (Guard) rejects malformed inbound records with a reason.
- Failed records go somewhere reviewable, not silently dropped.
Maintenance
- Data quality is re-profiled on a schedule to catch drift.
- Cleanups are previewed with dry_run before they run.
- Every automated change is auditable after the fact.
Turn the checklist into enforcement: profile with /api/v1/data-quality, clean with /api/v1/repair, and guard the boundary with /api/v1/guard.
Fix messy data before it breaks your workflow.
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