Data quality is measured along defined dimensions: completeness (are values missing?), correctness (are the values accurate?), timeliness (how old is the data?), consistency (do sources contradict each other?), and uniqueness (are there duplicates?).

In practice, these dimensions are monitored through automated quality checks in the data pipelines. Every dataset undergoes validation during ingestion. The data catalog documents quality metrics for each data source. Data stewards regularly review the results and escalate deviations. This creates a continuous quality process rather than one-off cleansing campaigns.