Business analytics is all about decision-making—not the way it was done in the past, which relied heavily on experience, gut feeling, and intuition, but the one that relies on data/evidence and computational/mathematical/statistical sciences. Because of the need to make faster and better decisions in today’s highly competitive business world and the availability of large and feature-rich data sources along with the advanced computing resources (both on hardware and software side), managerial decision-making is experiencing a paradigm shift. This chapter provided an overview of the human decision-making process and how business analytics can enhance this process toward more accurate and actionable outcomes.
As discussed in this chapter and illustrated in Figures 1.4 and 1.5, the simple taxonomy of business analytics is composed of three echelons: descriptive/diagnostic, predictive, and prescriptive. Prescriptive analytics is the highest echelon in this hierarchical relationship; it is closest to the decision-making. That is, whereas descriptive, diagnostic, and predictive analytics aim to create information to explain what happened, why it happened, and what will happen, prescriptive analytics focuses on the use of the information generated by these earlier echelons to identify the best course of action—to answer the question of what to do or what the optimal solution is to the problem. The following five chapters cover some of the most popular family of techniques that are collectively called prescriptive analytics with plenty of exemplary case studies and simple hands-on exercises.