Data Analytics and Data Mining - Difference Explained

Data analytics can be classified into three categories:

Descriptive analytics: Describes the collected data or dataset with clear visualization and summary.

Predictive analytics: Predict the future behavior of interest. Provides scenario analysis.

Prescriptive analytics: Makes or suggests smart decisions based on the predictive results. Optimization of solution based on the results of predictive analytics.

The three steps or categories of data analytics have to be used to make a decision based on data. To make data analytics valid or effective within a company in many different decisions, the company needs to involve at least three different people with different skills:

Business experts: Some of them set the problem  objective and some provide the decision model that which is based on domain knowledge. The decision model  indicates the data to be collected, the processes from which the data will be collected and the period for which data needs to be collected.

Information technology experts: They design the database which is likely to be filled during transaction processing, and they also manage the database.

Data analysis experts: They understand data mining, statistical and OR techniques.


Data analytics as explained is objective-oriented process that aims to make smart decisions. The goal is set first and data is  analyzed to take the decision that helps in achieving the goal in efficient manner.

Data mining focuses on identifying undiscovered patterns and establishing hidden relationships embedded in the dataset.  Data mining is a part of predictive analytics method.

Ud. 16.11.2023

First published  15.5.2015