Data analytics incorporates a wide diversity of methods and processes that can render it a difficult concept to define accurately. Towards that effort, a few examples of data analysis types can assist in understanding the numerous facets of data analytics.

Predictive analysis is a method of analyzing data that seeks to offer foresight about future events or outcomes. Data previously derived from past analysis is assessed and reported to provide predictions for forthcoming options. For instance, a business attempting to predict sales for the coming year will turn to data about past sales in an effort at foresight that can derive actionable decisions. Predictive analytics can also be applied to more complex predictions relating to qualified leads, risk assessment, or customer satisfaction.

Descriptive analysis has a primary focus of providing a report on events or outcomes that have previously occurred. By analyzing past data on a specific subject, descriptive analysis can portray what the data indicates. It is common to use descriptive analysis to assess key performance indicators (KPI), revenue, sales leads, and various other vital business components.

Diagnostic analysis serves to provide an answer to an essential question about why a particular scenario occurred. In other words, once descriptive analysis has been produced, a diagnostic analysis can provide insights as to the reasons for the data’s results. For instance, a business may witness sales growth in a specific demographic. Diagnostic analysis can give more awareness of how or why this growth in sales occurred, such as potential marketing efforts that produced more effective results.

Prescriptive analysis is a data analytics field that amalgamates the data from each of the three previously mentioned analysis types. By combining the collective analysis of the other three types, prescriptive analysis can provide actionable data. This is data that can be utilized to plan or implement strategies for a business.