What is Prescriptive Analytics?
There are some misconceptions that prescriptive analytics, and advanced analytics in general, require a data warehouse and a data scientist (or multiple data scientists). Although both are useful assets, there are other ways to tackle prescriptive analytics and the process of applying its simulation and optimization techniques to address key business challenges. While you don’t have to be a data scientist, it is helpful to have an understanding of statistics.
The prescriptive analytics process is similar to the Cross Industry Standard Process for Data Mining (CRISP-DM). It begins with establishing a comprehensive description of the business process to be modeled, including defining the business objective and the variables, control factors and constraints to be analyzed. Often the model will evolve from an initial conceptual mental model to a visual, logical or mathematical model. In many cases, the definition phase alone introduces new questions and advantageous insights. Much like in predictive modeling, coming up with a complete and accurate definition of the business process to model and the prescriptive objective is critical for valid and actionable results.
The entire process is highly iterative in nature and requires close cooperation between analytics professionals and subject-matter experts in business units. Once a prescriptive analytics model is finalized, it can be used for manual decision making or embedded into operational systems to support automated, real-time decisions.