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.

Real-World Applications

Here are a few real-world use cases to illustrate how prescriptive analytics are used to drive better decisions every day:

• Finance: Financial firms use prescriptive analytics to allocate assets, balancing risk and return based on changing market conditions. Banks can also use it to detect fraud, identify suspicious transactions, and recommend preventive measures.
• Healthcare: Hospitals and healthcare providers also use prescriptive analytics to allocate resources, ensuring the optimal use of medical equipment, staff, and facilities.
• Supply Chain & Logistics: Companies can use prescriptive analytics to optimize supply chains, inventory management, warehouse operations, and transportation routes—helping to minimize costs while meeting customer demand.

The Benefits of Prescriptive Analytics for Business Decision-Making

Many businesses start with predictive analytics, but struggle to turn insights into action. Prescriptive analytics bridges this gap, helping companies move from forecasts to actionable strategies.

Here’s why today’s leading businesses are increasingly adopting prescriptive analytics to drive better decision-making:

1. Better Resource Allocation

Prescriptive analytics helps organizations optimize resource allocation—whether it’s personnel, inventory, or equipment—for the most efficient and cost-effective approach. For example, airlines use prescriptive analytics to optimize crew scheduling, reducing delays and improving operational efficiency.

2. Minimized Costs, Maximized Profits

By analyzing different scenarios and constraints, prescriptive analytics can identify strategies to reduce costs and maximize revenue. For example, retailers can leverage prescriptive analytics to optimize pricing strategies, balancing demand, competition, and profitability.

3. More Agile Decision-Making

Prescriptive analytics enables dynamic, real-time decision-making, allowing businesses to adjust pricing, staffing, or production plans as needed based on the latest data.