Data freshness

What is data freshness?

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 Outdated or stale data, on the other hand, tends to bring about incorrect conclusions – and therefore misguided decisions.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!

Data freshness and real-time data

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. 

How to ensure data freshness

Data freshness can be altered by various choices, at many levels: 

  • Collection: hand-collected data takes a long time to be processed and might not be as up-to-date as automatically collected data. 
  • Storage: if data is stored in real-time databases, it is deemed fresher than data that is stored in batch-based databases.
  • Processing: likewise, if data is processed in real-time, it is considered fresher than batch-processed data. 

As a result, Data teams use various techniques to ensure fata freshness:

  • Anomaly detection (or outlier analysis) enables the identification of unexpected values or events in a given dataset.
  • Data validation techniques are designed to check that the data is exhaustive, trustworthy and consistent.
  • Data cleansing techniques focus on ensuring the data’s accuracy and relevance. 
  • Data updating techniques facilitate the updating of data through ELT processes in real-time or at regular intervals.
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