Predictive Analytics for the Modern Enterprise

Chapter 9. Food for Thought

In order to predict the future, we must understand the past.

Let’s recap what we explored in this book. We started with a background of predictive analytics. Chapter 1 assumed minimal prior knowledge of the subject and painted a picture in terms of everyday life. We discussed the use of predictive analytics versus other forms of analytics, and we took a walk down memory lane to understand the origins of data analytics and how it has evolved over the past century. The chapter then introduced the tools and frameworks available for data professionals to start implementing predictive analytics in their enterprises. What I enjoyed most about writing this chapter was the conceptualization of the magic store and the data analytics timeline. Since this is a rapidly evolving industry, I see the list of tools and frameworks changing over the course of future edits to the book.

Chapter 2 built on the discussion in Chapter 1, covering how organizations have moved from data producing to data driven. We explored the significance of data within organizations and strategies for maximizing the potential of this frequently underestimated organizational resource. Then, we dove into common challenges faced by enterprises when they are trying to build a predictive analytics practice, highlighting a few successful implementations of predictive analytics across vertical industries. The part I had the most fun writing was the detailed account of the use of predictive analytics ...