Glossary

What is Data Quality Monitoring?

Data quality monitoring is the ongoing measurement of defined data quality conditions so teams can identify changes, exceptions, and recurring issues over time. It applies rules or metrics to characteristics such as completeness, consistency, validity, freshness, and accuracy, then records results for review. Unlike data observability, which covers broader signals about data systems and pipelines, data quality monitoring focuses on whether data meets specified quality expectations.

For example, a team may check each daily customer-data load for missing identifiers, invalid dates, and unexpected shifts in record counts. A failed check signals that investigation is needed; it does not by itself explain the cause or prove that the dataset is suitable for a particular AI task. For AI work, monitoring provides time-based evidence that can support comparison and traceability. Readiness still requires evaluation against the intended use, required context, and acceptance criteria.

Frequently asked questions

What does data quality monitoring measure?

It measures defined quality conditions such as completeness, consistency, validity, freshness, and accuracy using rules or metrics selected for a dataset and its intended use.

How is data quality monitoring different from data observability?

Data quality monitoring checks whether data meets specified quality expectations, while data observability covers broader signals about the health and behavior of data systems and pipelines.

Does passing data quality checks mean data is ready for AI?

No. Passing checks provides useful evidence, but AI readiness still depends on the intended task, required context, acceptance criteria, and other relevant conditions.