Generate a data quality audit report from sample records
Claude reads a sample of your database records and writes an audit report: missing fields, format issues, and recommended fixes.
A one-page report with the top five data quality issues, example records, and a prioritized fix list.
- 1
Pull a representative sample
Use recent records, not just the oldest or the cleanest. A cherry-picked sample hides real problems.
- 2
Remove credentials before pasting
Strip passwords, API keys, and full payment details. Claude needs the structure, not the secrets.
- 3
Fix the top two issues
Start with the highest-impact problems. Run the report again after the fix to confirm the issue is gone.
The prompt to paste
Audit the data quality of this sample dataset and write a report I can send to the team that owns it. Dataset: [paste 20-50 sample records as JSON, CSV, or a table] Context: - What this data is used for: [e.g., customer records, product inventory, transaction logs] - Known issues, if any: [paste, or say "none known"] Return: 1. Summary: the overall quality in two sentences (is it usable, or is it broken?) 2. Top five issues: for each, name the field, the problem (missing, inconsistent format, invalid values), and how many records in the sample are affected 3. Example records: show 2-3 actual records that illustrate each issue 4. Impact: for each issue, one sentence on what breaks if we do not fix it 5. Fix priority: order the issues by impact (what to fix first) 6. Recommended fixes: for each issue, the change needed (validation rule, default value, or manual cleanup) Do not invent issues. If the sample looks clean, say so and name one risk to watch for.
A sample of 20 records can miss rare but critical issues. Run the audit on a larger sample if the stakes are high.
Sample case written by Hidden Logic Lab. Not affiliated with Claude.
More in Data
Get new cases in your inbox
Weekly notes on new use cases and agent workflows. No product pitches.