Big Data, Data Mining, and Machine Learning

A book by Jared Dean, ISBN

This book project was first presented to me during my first week in my current role of managing the data mining development at SAS. Writ- ing a book has always been a bucket‐list item, and I was very excited to be involved. I’ve come to realize why so many people want to write books, but why so few get the chance to see their thoughts and ideas bound and published.

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Big Data, Data Mining, and Machine Learning by Jared Dean

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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 sum- maries, often using approaches from business intelligence that have become commonplace. But in recent years, a paradigm shift has taken place. Organiza- tions 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 are all at least 15 years old, a testimony to their effectiveness that fundamentally new algorithms are not needed. Nevertheless, predictive modeling is in fact new to many organizations as they try to improve decisions with data. These organizations need to gain an understanding not only of the science and principles of predictive modeling but how to apply the principles to problems that defy the standard approaches and answers. But there is much more to predictive modeling than just build- ing predictive models. The operational aspects of predictive modeling xiii xiv ▸ FOREWORD projects are often overlooked and are rarely covered in books and courses. First, this includes specifying hardware and software needed for a predictive modeling. As Jared describes, this depends on the orga- nization, the data, and the analysts working on the project. Without setting up analysts with the proper resources, projects flounder and often fail. I’ve personally witnessed this on projects I have worked on, where hardware was improperly specified causing me to spend a considerable amount of time working around the limitations in RAM and processing speed. Ultimately, the success of predictive modeling projects is measured by the metric that matters to the organization using it, whether it be increased efficiency, ROI, customer lifetime value, or soft metrics like company reputation. I love the case studies in this book that address these issues, and you have a half‐dozen here to whet your appetite. This is especially important for managers who are trying to understand how predictive modeling will impact their bottom line. Predictive modeling is science, but successful implementation of predictive modeling solutions requires connecting the models to the business. Experience is essential to recognize these connections, and there is a wealth of experience here to draw from to propel you in your predictive modeling journey. Dean Abbott Abbott Analytics, Inc. March 2014