Definition: Data quality is the degree to which data is fit for its intended use, usually judged on accuracy, completeness, consistency, timeliness, validity and uniqueness.
Accuracy: values match reality. Completeness: required fields are present. Consistency: the same fact agrees across systems. Timeliness: data is fresh enough for the decision. Validity: values follow formats and rules. Uniqueness: no unintended duplicates.
Automated checks run inside data pipelines: schema tests, null and range checks, referential integrity, row-count and freshness monitors, and anomaly detection on key metrics. Failing checks should block bad data or alert an owner before it reaches dashboards or models. Data contracts between producers and consumers make expectations explicit.
Training on mislabeled or skewed data produces unreliable models, and stale or duplicate documents degrade RAG answers. Shifts in incoming data are also a leading cause of model drift, so quality monitoring continues after launch.
Ryz data engineers add tests, contracts and monitoring to your pipelines, and Ryz data analysts help define the rules that matter to the business.
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