Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners

Foreword

I love the field of predictive analytics and have lived in this world for my entire career. The mathematics are fun (at least for me), but turning what the algorithms uncover into solutions that a company uses and generates profit from makes the mathematics worthwhile. In some ways, Jared Dean and I are unusual in this regard; we really do love seeing these solutions work for organizations we work with. What amazes us, though, is that this field that we used to do in the back office, a niche of a niche, has now become one of the sexiest jobs of the twenty-first century. How did this happen?

We live in a world where data is collected in ever-increasing amounts, summarizing more of what people and machines do, and capturing finer granularity of their behavior. These three ways to characterize data are sometimes described as volume, variety, and velocity—the definition of big data. They are collected because of the perceived value in the data even if we don’t know exactly what we will do with it. Initially, many organizations collect it and report summaries, often using approaches from business intelligence that have become commonplace.

But in recent years, a paradigm shift has taken place. Organizations have found that predictive analytics transforms the way they make decisions. The algorithms and approaches to predictive modeling described in this book are not new for the most part; Jared himself describes the big-data problem as nothing new. The algorithms he describes ...