Data Quality Audit

Conduct a full data quality audit on a dataset or pipeline, identifying integrity issues, schema inconsistencies, and upstream causes with a prioritised remediation plan.

Data AnalystClaudeCo-PilotChatGPTGeminiHighUpdated Mar-26

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Prompt

I need to conduct a data quality audit on the following dataset or pipeline: Dataset / pipeline description: Data source(s): Known issues: Downstream consumers: Data volume: Please produce a structured data quality audit covering: 1. Completeness — identify null rates, missing fields, and whether gaps are systematic or random 2. Accuracy — flag values that appear incorrect, out of range, or inconsistent with business logic 3. Consistency — identify conflicting values across fields, tables, or time periods 4. Timeliness — assess whether data is arriving and updating as expected 5. Uniqueness — detect duplicate records and assess their cause and impact 6. Validity — check that values conform to expected formats, types, and reference data 7. Root cause hypotheses — for each issue, suggest the most likely upstream cause 8. Prioritised remediation plan — ordered by downstream impact, with a suggested owner and fix approach for each item If clarifying questions would improve the depth or accuracy of your audit, ask them now.