What is Data Quality?

Data quality measures the degree to which data is accurate (reflects reality), complete (no missing values), consistent (same data in different systems agrees), timely (available when needed), valid (conforms to defined formats and rules), and unique (no unnecessary duplicates). Poor data quality is the single most common barrier to analytics and AI success — models trained on bad data produce bad predictions, dashboards built on inconsistent data produce misleading insights. Data quality management includes profiling (understanding current quality levels), cleansing (fixing issues), monitoring (detecting quality degradation), and governance (preventing issues at the source). It is a foundational dimension in any data maturity assessment.