What Is Prescriptive Analytics?

Understanding the Three Pillars of Data Analytics

Generally speaking, there are three main pillars of data analytics. Each one plays an important and unique role:

1. Descriptive Analytics: Understanding what happened and why, using data aggregation and data mining.
2. Predictive Analytics: Forecasting what might happen, employing machine learning, statistical models, and simulation
3. Prescriptive Analytics: Deciding what should be done, utilizing optimization and heuristics.

Why Prescriptive Analytics Matters

Prescriptive analytics goes beyond merely predicting outcomes. It applies computational sciences and mathematical models to optimize decisions for a given business situation. By exploring an astronomical number of possible combinations and options, it finds the proven best option, maximizing or minimizing objectives such as total product costs.

Here’s why data scientists should consider embracing prescriptive analytics:

• Complex Problem Solving: It handles the world’s most complex business problems, generating optimal solutions.
• Integration with Machine Learning: Works hand-in-hand with machine learning to deliver significant business benefits across various industries.
• Increased Profitability: Improves decisions, leading to increased profitability and efficiencies for businesses.