A customer table contains 50,000 records.

The defect is measured using Completeness. Completeness evaluates whether all required records or values that should be present are actually present. In this scenario, the mandatory Tax Identification Number is absent from 2,500 of 50,000 customer records.

A straightforward attribute-level completeness metric would be calculated as the number of populated required values divided by the number expected. Therefore, 47,500 of 50,000 records contain the required value, producing a completeness result of 95%.

Completeness does not establish that populated values are accurate. A record may contain a Tax Identification Number and therefore pass the completeness rule while containing the wrong number. This separation between completeness and accuracy is essential when designing Data Quality scorecards. DAMA-aligned guidance defines completeness as the presence of required values and explicitly distinguishes it from factual correctness.

Governance should also determine whether the attribute is genuinely mandatory for every customer type. Quality rules must reflect business applicability rather than blindly requiring every field for every record.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Completeness; Data Quality Metrics; Business Rules; Critical Data Elements; Scorecards.

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