Prescriptive analytics is a form of advanced analytics that enables you to do your job better. It focuses on what should be done or what we can do to make a better decision.

As this simple definition explains, prescriptive analytics can involve many different techniques such as rules-based decisions, math, data operation, machine learning, optimization to determine whether or not to act, and if so, what actions should be taken.

One of the many useful use cases and applications of prescriptive analytics is called the “next best action”. In the next best action scenario a prescriptive analytics powered solution will determine what is the best actions to be taken in a specific situation.

These situation-aware decisions are applicable to many industries. For an instance below is a rule-driven decision model and prescribes what should be done in the case of an accident on a road?

Rule-based decision modeling in decision table

Or for example, in the health industry, the prescriptive analytics powered solution can determine based on situations of a patient, what should be done? Should an emergency be contacted? Or, should the patient practice self-isolation? For how long? A very detailed example of this scenario is explained here.

next best action in healthcare

As we can see in these examples, the goal of prescriptive analytics is to determine whether to act and what actions should be taken in a specific situation?

To put it simply, it will determine the set of relevant actions for a specific situation to reach a particular objective.

Evolving Decisions Logic for a Better Outcome

One very important element when designing such a solution is to ensure the logic is reviewed over time. The success of the decisions should be closely monitored and measured based on a set of KPIs. These solutions generally evolve over time, therefore it is very critical to enable domain experts to have constant input and optimizing the decision logic of the prescriptive analytics. As the result, prescriptive analytics powered solutions should separate the life cycle of the application from prescriptive decision logic.

This will enable ease of evolving and maturing the solution with business agility in releasing applications and changing code base.

What is NOT Prescriptive Analytics?

There are many techniques and tools under the advanced analytics umbrella such as dashboard, data aggregation, machine learning, maths, and statistical analysis, etc. neither of them is prescriptive analytics. Showing aggregated data on a dashboard, identifying a critical case based on rules, predicting required numbers of products for seasonal sales, are all part of the analytical solution. But they do not focus on actions, they are insights and information.

Prescriptive Analytics suggests what should be done in a particular situation once you have the facts and information on a case.

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Prescriptive Analytics is a powerful method using many techniques to enable organizations make better decisions by providing the relevant actions for a specific situation. By combining predictive and prescriptive analytics we can set organizations on the path making of consistently quality decisions. Organizations can deliver business value when multiple techniques such as predictive models, rules-driven decisions, optimization, etc. are orchestrated to guide them through steps of achieving goals in specific situations.


Published October 15th, 2020 at 06:55 pm