What is Prescriptive Analytics?
Introduction to Prescriptive Analytics
If Predictive Analytics tells you “what is likely to happen,” then Prescriptive Analytics goes a step further: it recommends “what you should do about it.”
Imagine an airline.
- Descriptive Analytics: “Flight 101 from NY to LA was only partly full yesterday.”
- Predictive Analytics: “Based on historical weather and booking trends, Flight 101 tomorrow will be about half full.”
- Prescriptive Analytics: “Because Flight 101 will be about half full, lower the fare on that route and swap in a smaller aircraft to cut fuel costs.”
Prescriptive analytics does not just forecast the future; it evaluates many possible decisions, estimates the outcome of each one, and recommends the option that best meets the business goal, such as maximizing revenue or minimizing risk.
How Prescriptive Analytics Works
Prescriptive analytics is among the more complex forms of data science. It combines machine learning (for the prediction) with operations research, simulation, and optimization algorithms.
1. The Optimization Engine
At the core of prescriptive systems is an optimization algorithm (often linear or integer programming, or heuristics). The business defines an objective: maximize profit. The business also defines constraints: we only have 5 delivery trucks, drivers cannot drive more than 8 hours a day, and packages must arrive before 5 PM.
2. The Simulation (Monte Carlo)
The system then runs simulations. It evaluates many possible delivery routes and feeds in the outputs of predictive analytics (e.g., the estimated chance of a traffic jam on Route A). It estimates the cost and outcome of each simulated route.
3. The Prescription
Finally, it outputs a recommended decision. It hands the dispatcher an optimized route plan that maximizes deliveries while respecting the constraint that no driver exceeds the 8-hour limit.
Real-World Implementations
Prescriptive analytics powers many automated business decisions.
- Algorithmic Trading: Trading systems do not just predict that a stock will rise. A prescriptive system decides how many shares to buy and how to time and split the orders to balance expected return against risk and market impact.
- Dynamic Pricing (Surge Pricing): Ride-hailing companies such as Uber use dynamic pricing. A predictive model forecasts that demand will spike when a concert ends. The pricing engine raises prices in that area, which aims to balance rider demand with the number of drivers willing to go there.
- Agentic AI: A growing direction for prescriptive analytics is agentic AI. Instead of only producing a dashboard recommending that a company buy more inventory, an AI agent can use tools to connect to a supplier’s API and submit the purchase order, ideally with human approval for high-impact actions.
The Barrier to Entry
Prescriptive analytics is hard to achieve because it requires a high level of trust in the data.
If a descriptive dashboard is wrong, an executive might make a poor decision. If an automated trading algorithm acts on bad data, it can lose large amounts of money in minutes.
Because of this risk, prescriptive systems need mature data engineering: strong Data Quality checks, reliable Data Observability, and a well-maintained Data Lakehouse that keeps the optimization engine working from accurate, current data.
Conclusion
Prescriptive analytics is one of the most advanced stages of the enterprise data journey. It moves part of decision-making from human intuition to mathematical optimization. While it is among the hardest capabilities to engineer, it can deliver a strong return on investment by turning data into action.
Learn More
To learn more about the Data Lakehouse, read The Lakehouse Built for Everyone by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.