Data freshness describes whether data is recent enough for its intended use, based on its age and the delay between a real-world event, data processing, and availability. Teams usually evaluate it against a defined threshold, such as whether order records become available within 15 minutes of creation. The relevant threshold depends on the task: hourly updates may suit one report, while a real-time operation may require data that is only seconds old.
For example, a recommendation workflow may compare the timestamp of the latest processed customer event with the current time. A growing difference can reveal delayed ingestion or a processing backlog. Freshness does not show whether values are accurate, complete, or appropriate for a particular AI task. It measures one time-related condition. For AI work, teams should record the observation time, source timestamp, processing time, and accepted threshold so that freshness results can be compared and traced to a defined use.