Key Differences Between Descriptive, Predictive and Prescriptive data analytics model

While descriptive, predictive, and prescriptive analytics are interconnected, they serve different purposes and provide different insights:

Descriptive Analytics: Focuses on understanding past events and trends. It provides a summary of historical data and helps identify patterns and relationships.
Predictive Analytics: Uses historical data to forecast future events. It helps anticipate potential outcomes and trends, enabling proactive decision-making.
Prescriptive Analytics: Recommends actions to achieve desired outcomes. It combines data, algorithms, and business rules to suggest the best course of action.

The key differences can be summarized as follows:

Descriptive	Predictive	Prescriptive
Descriptive data analytical model use data aggregation and data mining to provide insight into the past and answer.	Predictive data analytical model use statistical models and forecast techniques to understand the future and answer.	Prescriptive data analytical model use optimization and simulation algorithms to advice on possible outcomes and answer.
It focuses on – What has happened in the past?	It focuses on – What could happen in the future?	It focuses on – What should we do?
This is the analysis of the past or historical data used to understand the trends and estimate metrics over time.	This analysis is used to determine the future trends.	This analysis showcases feasible solutions to a problem and the impact of considering a solution on the future trend.
It is used when the user want to summarize results for all part or a part of business.	It is used when the user want to make an educated guess at the likely outcomes.	It is used when the user have to make complex or time sensitive decisions.
Tools Used – data mining, data aggregation	Tools Used – machine learning, statistical model	Tools Used – heuristics, optimization
Use reactive approach.	Use proactive approach.	Use proactive approach.
Example : Annual Revenue Report	Example: Ecommerce businesses which uses the customer’s browsing history to recommend products	Example: Identifying techniques to optimize the patient care in the healthcare

Comparing Descriptive, Predictive, and Prescriptive Analytics Models

Data analytics is the process of examining datasets to draw conclusions about the information they contain. It involves various techniques and tools to analyze raw data and extract meaningful insights. The primary goal of data analytics is to support decision-making by providing actionable insights. The three main types of data analytics models are descriptive, predictive, and prescriptive analytics each serving a unique purpose and providing different insights.

This article delves into the differences between these models, their applications, and how they can be used to enhance decision-making processes.

Table of Content

What is Descriptive Analytics?

Techniques and Tools for Descriptive Analytics
Applications of Descriptive Analytics

What is Predictive Analytics?

Techniques and Tools for Predictive Analytics
Applications of Predictive Analytics

What is Prescriptive Analytics?

Techniques and Tools for Prescriptive Analytics
Applications of Prescriptive Analytics