The Data Quality Definition Consensus Gap

Despite decades of research on data quality, there is no consensus on a single, generally accepted definition. A systematic literature review of 17,000+ publications found only 35 with original dimension-based definitions, and these vary widely in scope, terminology, and focus.

The pattern: researchers frequently reference existing definitions rather than explicitly defining quality. When they do define it, they rarely use consistent terminology or structure.

The most frequently cited dimensions (accuracy, completeness, consistency, timeliness, accessibility) appear across definitions, but even these core dimensions are defined differently by different authors. Completeness, for example, is classified as “intrinsic” by some and “contextual” by others.

This suggests the problem isn’t absence of definitions, it’s absence of a shared vocabulary for comparing them.

Related: 04-atom—fitness-for-use-definition