Data quality dimensions are measurable characteristics used to define, assess, and communicate whether data meets the requirements of a particular use. Common dimensions include accuracy, completeness, consistency, timeliness, validity, and uniqueness, although frameworks may use different names or include additional characteristics. Each dimension turns a broad statement about data quality into a condition that teams can evaluate with defined rules, metrics, and thresholds.
For example, a customer dataset may be checked for missing required identifiers under completeness, conflicting values across systems under consistency, and outdated records under timeliness. A strong result in one dimension does not compensate automatically for a failure in another, and no universal set of dimensions proves fitness for every task. For AI work, teams should select dimensions according to the intended use, record how each measure was calculated, and preserve the evaluated data state. This supports comparison and traceability while keeping the final readiness decision tied to explicit requirements.