Predictive Analytics vs Prescriptive Analytics

Although the two sound similar, they serve different purposes within the decision making process of a company. While predictive analytics attempt to estimate the future, prescriptive analytics are utilized to identify the optimal decision to be taken, based on said estimate.

Predictive Analytics:

It utilizes historical data and algorithms to forecast future outcomes, and provide managers with new insights.

According to SAS “A 2014 TDWI report found that the top five things predictive analytics are used for is to:

· Identify trends.

· Understand customers.

· Improve business performance.

· Drive strategic decision making.

· Predict behavior.”

Predictive analytics operates with the use of modules that serve particular purposes. Models are reusable, and modeling software Solution often “export the model information into a local file in industry standard predictive modeling markup language (PMML)” (Imanuel).

Prescriptive Analytics:

After obtaining the trend or forecast information, Prescriptive analytics try to answer what action would be optimal for the company to undertake. For this, simulations are often used — particularly in order to identify potential outcome scenarios under divergent circumstances (RiverLogic).

Similar to this optimization models also attempt to foresee the consequences that certain decisions or forecast implementations may have. However, it integrates real operational constraints into the analysis (RiverLogic).