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RESOURCE · CHECKLIST

Critical-data quality checklist

Reliable does not mean it looks right in a chart. It means the data passes explicit checks that someone owns. Run these against the few datasets your decisions actually depend on.

Free. No email required.

Pick the critical data first

  1. List the few datasets that directly feed decisions or external reporting. Check those, not everything.

Run six checks on each

  1. Completeness: are required fields and expected rows all present?
  2. Uniqueness: are there unintended duplicate business keys?
  3. Validity: do values conform to the expected type, range and allowed set?
  4. Referential integrity: does every child row point to a valid parent key?
  5. Freshness: is the data recent enough for the decision it supports?
  6. Reconciliation: do independent sources agree at record level, not just on totals?
  7. Record the owner for each dataset and where failures are alerted.

Why this works, in depth: How do we know whether our data is actually reliable?