Descriptive Analytics

insight into the past

Most raw data, and particularly Big Data, are not fit for human interpretation and do not provide significant value at their unprocessed form. Descriptive Analytics aim to produce understandable chunks from the initial data, allowing people to better interpret them and deduce useful information for their uses and purposes. They are used by 90% of organisations and business today and are the foundation of Business Intelligence processes.

Descriptive analytics are applied on historical data, of variable timeframes, and essentially summarise what has occurred in that timespan. The perspective that are to be evaluated are determined by the decision maker and are usually translated as calculations over different data dimensions.

Types of Descriptive Analytics

Common actions like classification and clustering, filtering, aggregation and statistical analysis fall under this category of analytics. Descriptive Analytics are therefore used when there is a need to understand data at a coarse grain and when there is a need to summarise and describe them from different aspects.

Of course, such analysis of the past, alongside its inherent value for assessing a current state, is the driver for understanding how past activities and results might influence future outcomes.

The automation of the latter is achieved via a different class of analytical process, Predictive Analytics