The dataset can be complete while remaining inaccurate. Completeness measures whether the required records and values are present; Accuracy measures whether those values correctly represent reality.
If every customer has a telephone number, the field may achieve 100% completeness. However, if 8% of those numbers belong to other people, the values are inaccurate.
This distinction is one of the most important principles in Data Quality measurement. A populated field is not automatically trustworthy. Similarly, a syntactically valid value is not necessarily accurate.
DAMA-aligned guidance explicitly distinguishes these dimensions and notes that a complete dataset may still contain incorrect values.
The appropriate quality scorecard should therefore present completeness and accuracy separately rather than treating one as evidence of the other.
For high-risk customer contact processes, the business may define independent thresholds for both dimensions. Verification services, customer confirmation, authoritative sources, and exception handling may be needed to improve accuracy after basic completeness has already been achieved.
Reference Topics: DAMA-DMBOK2 Chapter 13 — Completeness; Accuracy; Data Quality Dimensions; Metrics; Fitness for Purpose.
===============
