Data freshness — also known as data up-to-dateness – is one of the 10 dimensions of data quality. It refers to the data’s timeliness and accuracy: in other words, how up-to-date this data is and how relevant it is to the current situation.
What we call “fresh data” is therefore an accurate representation of a given phenomenon or system in its most recent state. Outdated or stale data, on the other hand, tends to bring about incorrect conclusions – and therefore misguided decisions.
Needless to say, Data Freshness is critical when it comes to identifying and understanding trends and patterns in datasets, as well as making informed decisions!
The term real-time Data (RTD) refers to data that is delivered immediately after collection. It is captured, processed and made available as soon as it has been generated – which undoubtedly makes it “fresh data”.
Since Real-time Data systems process and deliver data almost instantaneously, users can leverage the latest data available to make decisions accordingly. This is why Real-time Data is essential in situations where data freshness is of utmost importance, such as financial trading or traffic management systems.
Data freshness can be altered by various choices, at many levels:
As a result, Data teams use various techniques to ensure fata freshness: