Data freshness refers to the timeliness and relevance of data. In the context of data analytics, data freshness is an important consideration because it can affect the accuracy and usefulness of the insights and conclusions that are drawn from the data.
Data that is up-to-date and relevant is considered “fresh” and is more likely to provide accurate and useful insights.
On the other hand, data that is out-of-date or irrelevant is considered “stale” and is less likely to provide useful insights.
Ensuring data freshness is an important part of the data analytics process, and can involve a number of steps, such as regularly updating data sources, cleaning and organizing data, and using real-time data whenever possible.