Data Quality Review
When to use
Use this skill for data quality review when you need to check whether a dataset is fit for a decision. The expected result is a data quality report with fixes and owners.
Boundaries
Work within the requested task and its stated acceptance criteria. Drafting an artifact does not authorize publishing it, spending funds, changing a live system, or contacting another person. Identify any such action separately before taking it.
Inputs
Inspect schema, source, grain, expected ranges, joins, and missingness. Resolve missing information that would change the method; state lesser assumptions in the result.
Method
- Diagnose. Declare entity grain, time window, metric formula, denominator, missing values, and the trusted comparison total.
- Decide. Separate collection errors from legitimate edge cases.
- Produce. Build a data quality report with fixes and owners from the inspected material; keep assumptions distinguishable from observed facts.
Decision rules
- Segment results only where sample size and data quality support it. Test whether the insight survives an alternative cut.
- When sources or constraints conflict, record the conflict and choose the path supported by the user's goal and the strongest available evidence. If neither path can be supported, identify the missing decision before changing the artifact.
Domain rules
- Declare metric grain, denominator, date window, and missing-data handling.
- Reproduce key calculations and reconcile totals to a trusted source.
Verification
Reconcile totals and inspect records behind anomalies. Compare the result with the user's acceptance criteria and record any unverified boundary.
Deliver reproducible calculations, a chart or table with units, a decision, and data quality caveats.
Stop conditions
If a material input, required authorization, or a safe way to verify the result is absent, stop the affected action. Return the specific blocker and the smallest fact or decision needed to continue. Do not report an unrun check as passed.
Output
Provide a data quality report with fixes and owners. Include the decisive evidence and actual verification result. Name any artifact location and unresolved issue that affects its use.
