Predictive & Prescriptive Analytics: The Insights You Need to Stay Relevant

Many data scientists lump all “predictive analytics” together. They assume one approach fits every decision. Even more business leaders do the same. But the reality is more nuanced. Recognizing case differences is key to building better models and their proper evaluation.

One Complex Need, Many Approaches

Business leaders often voice a single request: “Help us navigate an uncertain future.” Behind the scenes, data science tackles this in three distinct ways:

1. Predicting “Nature” (uncontrolled events)
2. Predicting things we can change by our subsequent actions
3. Prescribing the best action among many alternatives

Some professionals see “prescriptive” analytics as part of “predictive,” while others draw a sharp line. If we pigeonhole them too much, we risk incomplete solutions. Let’s examine each category.

1) Predicting “Nature”

What It Is

Forecasting events or states you can’t alter easily by business action. Examples include weather, border delays, or the impact of raw material impurities. This is the most straightforward type of prediction—much like “school homework.”

Why It Matters

These predictions help us understand the world we will live in. Often, clarity about the future is enough to make the right decisions. If appropriately collected, data will describe the dynamic we study. We can even conduct experiments to validate the model. External reality unfolds independently. So,?you can compare your forecast with actual events in real life.?

Example: Forecasting an upcoming storm doesn’t make the storm more or less likely. If your weather model indicates there’s an 80% chance of rain, you simply check if it rained and count your hits and misses.

2) Predicting Outcomes Impacted by Our Future Action

What It Is

In this situation, you predict an outcome the decision-makers can?influence after seeing your forecast.?A typical example is revenue. Sales managers will act if the forecast shows that the sales might not reach the target. They might hustle, offer discounts, or increase promotions. Because of this, the final revenue could be higher than the forecast. But that is the goal of the forecast: to see where the org ends up keeping things intact and act if it is not good. This idea also applies to operations. For instance, if you anticipate equipment failures, you can increase maintenance. This helps to prevent or delay the failure altogether.

Another aspect of this is predicting how your actions alter the future. For example, do people switch to a competitor if you raise prices?

Why It’s Tricky

• Confounding Factors of Reactions: When people encounter a troubling forecast, they prevent it. As a result, the “bad outcome” might never occur. Was the prediction wrong?
• Training Data: Separating the impact of actions in historical data is often impossible. Even if we have data on past actions, it is usually observational data. Actions are individual cases, which makes it hard to understand the mechanics involved. Moreover, the ability to do controlled experiments is generally minimal. For example, we can't test what would have been the "uninfluenced" result in the past.
• Different Validation Methods: Comparing the actual outcome with the initial prediction is pointless. Standard “predicted vs. actual” accuracy checks may lead you astray. This is like a time travel paradox. If the model considers the action that will follow the alarm, then there is no alarm and no action. So the forecast is wrong, assuming action that was never triggered. It is easy to make the wrong conclusion that the forecasts are bad because they do not match reality. We need to take a more complex validation approach.

Example: If a model foresees that a sales team will miss targets by 20%, the team will likely scramble to boost performance. The actual number may be beating the forecast by a large margin.

3) Prescribing the Best Action

What It Is

Selecting the best action among many alternatives. Sometimes, the best choice is straightforward. Then, the only challenge is to bring all relevant data to the decision-makers. They can pick the optimal response. But the human mind is very limited. The task soon exceeds the capacity of the human mind. Look for the article by Sheena Iyengar and Mark Lepper describing the "jam experiment." Buy one jam from three. Easy and attractive to fine-tune the flavor of breakfasts. From twenty? Most people were not able to buy anything. Other examples:

• Scheduling 500 machines across 300 production lines
• Monty Hall problem
• Penney's game

Why It’s Complex

Optimization describes the entire decision-making problem through a set of constraints. It is a very different language than “For X, the outcome is Y". Sound optimization requires a thorough understanding of the domain and advanced mathematics. A naive formulation can lead to non-linearities and long computational times. Solution computations need knowledge of multidimensional minimization and/or graph algorithms. Additionally, incorporating uncertainty might increase the size by order of magnitudes.

Example: Imagine the city that needs to collect the waste from the containers around the city. Planning the routes for municipal waste collection vehicles is a complex logistical challenge. How many cars the city needs? How often will they visit each container?

Pulling It All Together

In practice, a single decision might need all three perspectives:

1. Insight on future environment state (e.g., macroeconomic trends, energy prices, weather).
2. Insight on outcomes impacted by our future action (e.g., if you raise prices, do people switch to a competitor?).
3. Insight on the ideal action (e.g., best product portfolio or marketing mix).

Many professionals specialize narrowly. But from a business standpoint, that can lead to incomplete solutions. Forecasts alone aren’t enough if you apply simplistic heuristics to how to act. Pure optimization exploits the full potential through all-in strategies if the future is certain.

Example: Consider allocating campaigns to the customers. A propensity model discloses the probability of accepting the offer. If you rely solely on a propensity, you need to leverage simple heuristics for assignment. For example, assign the campaign with the highest likelihood of acceptance to the customer. But in reality, you’re limited by budget, capacity, or risk diversification. A robust prescriptive approach addresses these constraints and finds the best overall strategy.

Why This Matters for Junior Data Scientists and Business Leaders

Early on, you might lean on a single technique, easiest or most discussed by influencers. The?real impact?arises when we address the complexity of business in full, not leaving the simplicity backdoor to bite us. This means that the bottleneck is often not the precision of the prediction but the way we select the action.

Building a broader skill set keeps you from the “man with a golden hammer” pitfall and makes you a more valuable asset to any organization.

Business leaders seldom concern themselves with which label (“predictive,” “prescriptive,” “operations research,” or “machine learning”) is used. They inhabit a straightforward world of the latest buzzwords. However, they still seek a?comprehensive?answer on how to navigate their business situations.?

What’s your take? Have you seen confusion between forecasting and prescribing lead teams astray? Let’s talk below!

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