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Prescriptive Analytics


Prescriptive Analytics is a form of advanced analytics which examines data or content to answer the question “What should be done?” or “What can we do to make ___ happen?”, and is characterized by techniques such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning.(Source.:

Analytics are divided in the following three steps:

  1. Descriptive Analytics, which use data aggregation and data mining to provide insight into the past and answer: “What has happened?”
  2. Predictive Analytics, which use statistical models and forecasts techniques to understand the future and answer: “What could happen?”
  3. Prescriptive Analytics, which use optimization and simulation algorithms to advice on possible outcomes and answer: “What should we do?”(Source:

Predictive and Prescriptive Analytics Tools are on the Top 11 Business Intelligence and Analytics Trend for 2017 (comp.: In the future, the focus will be more to answer the questions „what could happen“ and „what could we do to make it happen“. To understand the benefits of Prescriptive Analytics we must understand in the first step the benefits of Predictive analytics. If you don’t know the benefits of Predictive analytics please consider the Wiki page Predictive Analytics (

Prescriptive Analytics is nearly based on Predictive analytics but provides deeper information for the future. The focus is based on which decision is he best to reach our predefined goals. It although provides the information what steps must taken to reach the goal. Prescriptive Analytics will compare the outcomes in the future with the steps and the outcomes in the future without these steps. That’s a huge benefit, the manager will be able to see the impact of a future decisions before the decision are made.

In the finance sector, especially on the stock market it is very important to make correct decisions in real-time. In the self-driving car from google prescriptive analytics are already used with success. In some other industries, today a variety of structured and unstructured datasets are used to optimize decisions. The unstructed datasets which are analyzed are for example videos, images, sounds. In many industries for example medical and healthcare prescriptive Analytics should have a bigger impact than predictive analytics.

The future of big data, however, lies with prescriptive analytics. (Source:

Some techniques which are today used to make prescriptive analytics:

  • machine learning
  • artifical intelligence
  • data mining
  • algorithmen of operations research

Key weaknesses of Predictive analytics:

The result of the decision depends totally on the quality of the data sources. Decisions that are just based on Prescriptive Analytics could be wrong interpreted. A mathematical correlation between two variables means not that both variables must be causally cohesive.


Predictive analytics has a high potential to every industry. Today in some industries it provides already a benefit. Predictive analytics compared with human decisions making could chance the future of strategical management. It can help to optimize scheduling, production, inventory, supply chain and to deliver what your costumer want in the most optimized way (Source:


Mona Lebied (2016): Top 11 Business Intelligence and Analytics Trends for 2017 (05.04.2017)

Frank Romeike & Andreas Eichler (2016): Predictive Analytics (05.04.2017)

Mark van Rijmenam (2014): The Future of Big Data: Prescriptive Analytics Changes the Game (05.04.2017)

Michael Connaughton (not known): Prescriptive Analytics: Die neue Generation von Big-Data-Analysen (05.04.2017)

David Crockett (unknown): Prescriptive Analytics Beats Simple Prediction for Improving Healthcare (05.04.2017)

concepts/prescriptive-analytics.txt · Last modified: 2020/08/20 13:27 (external edit)