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Continuous Data Quality Monitoring (CDQM) is a process for ensuring ongoing data quality. It enables organizations to quickly identify and rectify issues in their data, leading to better decision-making and increased efficiency.
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Continuous data quality monitoring checks data continuously against defined quality dimensions and reports deviations promptly.
The approach grew from combining data quality management with ongoing system monitoring. As distributed pipelines spread, teams needed to observe freshness, completeness, and plausibility in the flow instead of checking quality only in downstream reports.
Imagine a network of pipes with sensors: they continuously measure pressure and purity, alert at thresholds, and indicate where a fault begins.
Rules check dimensions such as completeness, freshness, accuracy, and consistency.
Automated tests compare data with expectations or thresholds.
Deviations create visible signals and trigger responsible responses.
CDQM shortens the time from data defect to response and shows pipeline trends. Measurements do not prove business correctness by themselves; rules, thresholds, and ownership need maintenance.
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