Prescriptive analytics

Prescriptive analytics complements the previous levels of analysis - descriptive, diagnostic and predictive analytics - and goes one step further. Instead of just describing or predicting, it helps to derive clear recommendations for action based on the analysis results. It is ideal for solving complex, dynamic business problems where many variables and uncertainties play a role, such as in supply chain planning, finance or marketing.

A simple example is weather forecasting. Let's say our algorithms predict that it will rain tomorrow. This is interesting information, but not very valuable without interpretation. Do I work from home? Do I usually drive to work? By bike? Was I planning to go to the outdoor pool? Only with this context, which is incorporated into the so-called fitness function, can I evaluate the forecast and derive appropriate actions. So if I'm going to work in the office that day and then ride my bike to the outdoor pool, a predictive analytics system would recommend that I take the car after all.

To summarize, prescriptive analytics combines a statistical model with descriptive factors to recommend or directly carry out the best possible action.

So what exactly is the difference between prescriptive analytics and predictive analytics? Prediction describes the forecasting of events. The probability of good weather, the share price at the end of the week or whether a customer will buy an item or not are all examples of predictive analytics.

Prescriptive analytics, on the other hand, does not stop at the prediction and its probability. Prescriptive analytics takes the next step and attempts to convert the information based on the prediction(s) into an action. The recommendation for the car in the rain, the purchase of shares or a lower price for the customer would all be extensions of the prediction into a prescription.

Data usage finally becomes "actionable"

The biggest advantage of predictive analyses is that the data science process does not stop at evaluation or prediction, but also includes action. This step - away from the processing of numbers towards a clear application - is fundamental to the success of data-based work.

Prescriptive analytics goes hand in hand with the possibility of complete automation. If the best possible scenario is selected and played out based on the snapshot of the data, the entire process from data evaluation and modeling to deployment is an integrated service. As a result, only predictive analyses allow decisions to be made directly, even in highly variable environments, and corresponding actions to be played out or implemented directly.

The fitness function plays a central role in the evaluation and optimization of possible courses of action in prescriptive analytics. It serves as a benchmark for how "good" or "suitable" a decision is with regard to the desired goals. The fitness function takes into account a number of parameters that need to be carefully defined and weighted in order to realistically map relevant business rules and objectives. These parameters often include key KPIs, constraints and requirements that can have a direct impact on the action in question. One of the key challenges in developing a robust fitness function is to ensure that all relevant data is available, of high quality and that the complexity of the underlying problem is sufficiently understood.

Even with a well-designed fitness function, undesirable results can occur. Examples of this are unprofitable price recommendations, impractical suggestions (such as negative stock levels) or other results that do more harm than good to the company. Such cases must be carefully considered and either filtered or, even better, adjusted. Otherwise, such unwanted outputs could lead to poor performance of the algorithm and, in the worst case, cause significant financial damage to the company.

Prescriptive analytics shifts decision-making from relying on experience-based assumptions to using transparent, reproducible decision-making logic. In doing so, companies not only optimize their processes but also create a solid foundation for scalable growth and sustainable competitive advantages.