Data Quality Checklist

Receipt checks

• File/table received from expected source.
• Period matches request.
• Row count matches source-owner expectation or reconciliation report.
• Field list matches request.
• File format can be opened and processed.

Completeness checks

• Required fields are present.
• Key identifiers are populated.
• Date fields cover expected period.
• No unexpected filters are visible.
• Population reconciles to source report where possible.

Validity checks

• Date formats are valid.
• Numeric fields parse correctly.
• Categorical values are understood.
• Key fields are unique where expected.
• Duplicates are identified and explained.

Reasonableness checks

• Values fall within plausible ranges.
• Outliers are flagged, not automatically removed.
• Missing values are quantified.
• Transformation row counts are tracked.
• Exceptions are reviewed with business context.

Documentation

• Validation procedures documented.
• Limitations recorded.
• Data owner questions logged.
• Final usable dataset version identified.