Descriptive analytics is often considered the first stage of data processing where a summary is made of historical data to surface useful information and prepare for further data analysis, such as diagnostic analytics.

Descriptive analytics could be boiled down to the act of providing information about what has already happened. It is a form of retrospective analytics. Data mining and data aggregation can be deployed with descriptive data to look for signals and patterns that can surface otherwise hard to find insights. Data visualization can also be layered on to provide even more insights.

Diagnostic analytics then can be used in these retrospective data sets to try to determine what happened. What was the cause of certain behaviors and events in the retrospective dataset.

Predictive analytics is the next step. It looks at what might happen in the future based on trends and probabilities. The final stage is prescriptive analytics, which attempt to provide actionable insights that can identify the best course of action to generate the best outcome, whether that is taking advantage of opportunities or reducing risk.

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