descriptive analytics

Descriptive analytics is a preliminary stage of data processing that creates a summary of historical data to yield useful information and possibly prepare the data for further analysis.

Data aggregation and data mining methods organize the data and make it possible to identify patterns and relationships in it that would not otherwise be visible. Querying, reporting and data visualization may be applied to yield more insight.

Descriptive analytics is sometimes said to provide information about happened. You might see, for example, an increase in Twitter followers after a particular tweet. Diagnostic analytics is a deeper look at data to attempt to understand the causes of events and behaviors.  Predictive analytics, which is used to identify future probabilities and trends, is said provide information about what might happen in the future. Prescriptive analytics is applied to try to identify the best outcome to events, given the parameters, and suggest decision options to best take advantage of a future opportunity or mitigate a future risk. 

Here’s a summary of the stages of data analysis:

Descriptive analytics: What happened?

Diagnostic analytics: Why did it happen?

Predictive analytics: What could happen in the future?

Prescriptive analytics: How should we respond to those potential future events?