Samenvatting

In this paper, we show our vision on prescriptive analytics. Prescriptive analytics is a field of study in which the actions are determined that are required in order to achieve a particular goal. This is different from predictive analytics, where we only determine what will happen if we continue current trend. Consequently, the amount of data that needs to be taken into account is much larger, making it a relevant big data problem. We zoom in on the requirements of prescriptive analytics problems: impact, complexity, objective, constraints and data. We explain some of the challenges, such as the availability of the data, the downside of simulations, the creation of bias in the data and trust of the user. We highlight a number of application areas in which prescriptive analytics could or would not work given our requirements. Based on these application areas, we conclude that domains with a large amount of data and in which the phenomena are restricted by laws of physics or math are very applicable for prescriptive analytics. Areas in which the human or human activities play a role, future research will be required to meet the requirements and tackle the challenges. Directions of future research will be in integrating model-driven and data-driven approaches, but also privacy, ethics and legislation. Whereas predictive analytics is often already accepted in society, prescriptive analytics is still in its infancy.