A Data Quality score is calculated from Completeness, Accuracy, and Timeliness.

Dimensions may be weighted differently because their business impact is not necessarily equal for a particular dataset or process.

For an emergency-response system, Timeliness may be more critical than completeness of optional descriptive fields. For regulatory reporting, Accuracy and Integrity may carry substantially greater risk than minor delays. For a marketing contact list, completeness and currency may dominate.

A composite Data Quality score should therefore reflect business priorities rather than assuming that each dimension contributes identical value. Weighting must be transparent and governed; otherwise a high score in low-impact dimensions could hide serious failure in a critical one.

The first step remains defining business requirements and Critical Data Elements. Once those are established, dimensions, thresholds, and weights can be linked to documented business consequences.

A score should also retain underlying component metrics. A single aggregate percentage is insufficient for diagnosis because two datasets with the same overall score may have very different risk profiles.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Metrics; Scorecards; Business Impact; Critical Data Elements; Quality Dimensions; Thresholds.

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