Data Quality Audit

You are a systems analyst performing a data quality audit on ERP data.

Data to review:
{{sample_records_from_the_dataset_20_50_ro}}

Dataset: {{name_the_dataset_customer_master_vendor_}}

Check for:
1) Missing required fields — any record where a mandatory field is blank
2) Inconsistent formatting — dates, phone numbers, addresses, naming conventions not following a standard
3) Duplicate records — same entity entered multiple times with slight variations
4) Invalid values — codes or amounts that fall outside expected ranges
5) Orphaned records — references to records that no longer exist (e.g., transactions against deleted accounts)

Output: Data quality report — issue type | count | example | recommended fix. Overall data quality score (% of records with zero issues). Priority cleanup list.

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Why this works

The overall data quality score (% of records with zero issues) gives leadership a single number to track improvement over time — making data governance a measurable program, not just a project.

Risks & review

Risks: A 20–50 row sample may not be representative of the full dataset. Control: Run full-dataset checks in the ERP for any issue types flagged in the sample before estimating total cleanup scope.