In today’s data-driven world, the ability to make informed, strategic decisions is paramount. While descriptive and predictive analytics provide insights into what has happened and what might happen, prescriptive analytics—also known as mathematical optimization, mixed-integer programming (MIP), and decision intelligence—offers a pathway to determine what should happen. It’s a powerful tool that data scientists are increasingly adding to their analytics toolbox.

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.

Learn More About Prescriptive Analytics

Resource > Whitepaper
Solve a Different Class of Problems

You probably have a very firm grasp of descriptive, diagnostic, and predictive analytics tools, but perhaps are not that familiar with prescriptive analytics in general and mathematical optimization (the primary prescriptive analytics tool) in particular. In this white paper, you’ll hear about organizations—across a wide range of industries—that are using mathematical optimization to conduct prescriptive analysis at massive scale and complexity.

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Prescriptive Analytics: The Data Science Master Key to a Turbulent Future

Analytics––descriptive, predictive and prescriptive is a rapidly evolving field that gives companies the knowledge to make smarter business decisions. It provides valuable insight into past performance and future prediction and decision guidance.

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Adding Optimization to Your Data Science Analytics Toolbox

This video presents real-world examples of Machine Learning and optimization in action, illustrating the value it can bring to your organization. It also provides you with next steps on how to get started with optimization as well as available resources.

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Resource > Training
Level 1 (Part 1) – Introduction for Data Scientists

In this video, Gurobi CEO and Co-founder Dr. Ed Rothberg explains how mixed-integer programming (MIP) combines expressiveness and robustness to produce high-quality, reliable solutions.

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Resource > Blog
Mathematical Optimization: A Powerful Prescriptive Analytics Technology

Unlock the full value of data and drive optimal decision-making processes.

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