Difference Between Descriptive and Predictive Data Mining

The descriptive and predictive data mining techniques have huge applications in data mining; they are used to mine the types of patterns. The descriptive analysis is used to mine data and specify the current data on past events. In contrast, the predictive analysis gives the answers to all queries related to recent or previous data that move across using historical data as the main principle for decision.

The task of data mining can be predictive, descriptive and prescriptive. In this article, we will discuss the two terms, predictive data mining and descriptive data mining, separately. In laymen language, you can say that descriptive mining involves finding interesting patterns or associations relating to data. In contrast, predictive mining involves the prediction and classification of the data gathered in past or current. Read the article to learn the difference between descriptive and predictive data mining.

What is descriptive data mining?

Descriptive mining is usually used to provide correlation, cross-tabulation, frequency, etc. These techniques are used to determine the data regularities and to reveal patterns. It targets the summarization and conversion of data into meaningful data for reporting and monitoring.

As the name suggests, descriptive mining "describe" the data. Once the data is captured, we convert it into human interpretable form. Descriptive analytics focus on answering "What has happened in the past?" Descriptive analytics is useful because it enables us to learn from the past.

How descriptive analytics is used in learning analytics

• Comparing pre-test and post-test assessments.
• Tracking course enrollments.
• Collating course survey results.
• Recording which learning resources and accessed and how often.
• Summarizing the number of times, a learner posts on a discussion board.

What is Predictive data Mining?

The term 'Predictive' means to predict something, so predictive data mining is the analysis done to predict the future event or other data or trends. Predictive data mining can enable business analysts to make decisions and add value to the analytics team efforts. Predictive data mining supports predictive analytics. As we know, predictive analytics is the use of information to predict outcomes.

Let's understand this concept with the help of an example;

Any retail shop may use algorithm-based tools to go through a customer database to look at the previous transactions to predict future transactions. In other words, the previous data may enable the shopkeeper to project what will happen in future in the business, enabling business people to plan accordingly.

Advantages of Predictive mining in business

Following are the given most important business benefits of Predictive mining.

1. It increases the company production.
2. It reduces risks in business.
3. It helps business analysts to make better decisions in a business organization.
4. It helps to maintain a competitive environment.

Difference between predictive and descriptive data mining