Descriptive analytics answers the question of what happened. For instance, a healthcare provider will learn how many patients were hospitalized last month; a retailer – the average weekly sales volume; a manufacturer – a rate of the products returned for a past month, etc. Let us also bring an example from our practice: a manufacturer was able to decide on focus product categories based on the analysis of revenue, monthly revenue per product group, income by product group, total quality of metal parts produced per month.

At this stage, historical data can be measured against other data to answer the question of why something happened. Thanks to diagnostic analytics, there is a possibility to drill down, to find out dependencies and to identify patterns. Companies go for diagnostic analytics, as it gives in-depth insights into a particular problem. At the same time, a company should have detailed information at their disposal, otherwise data collection may turn out to be individual for every issue and time-consuming.

Prescriptive analytics is a data analysis type that uses advanced technology heavily to find the best solution based on data provided from predictive analytics. Thus, prescriptive analytics would determine what a company could do with a problem or trend foreseen by predictive analytics. Like predictive analytics, prescriptive analysis needs its own business logic and algorithms. As for prescriptive analytics techniques, machine learning is one of the most common.