Data quality measures how well a dataset serves its intended use, judged across dimensions such as accuracy, completeness, consistency, validity, uniqueness, and timeliness. Teams assess it by profiling columns, applying validation rules, and tracking error rates over time.
A bank, for example, may rate a customer table as high quality once it removes duplicate records and fills required fields. Quality rules catch malformed values before they reach a report or a model.
High data quality is necessary for analytics and AI, but it is not the same as readiness for AI execution. A dataset can pass every quality check and still break a model in production when the exact state that produced a result cannot be reproduced. AI-ready data extends quality with reproducibility and traceability of the data state, so a result can be replayed and audited later.