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Foreword
Sandra Wilikens
Secretary General, responsible for CSR and member of the Executive Committee, BNP Paribas Fortis
In today's corporate world, strategic priorities tend to center on customer and shareholder value. One of the consequences is that analytics often focuses too much on complex technologies and statistics rather than long-term value creation. With their book Profit-Driven Business Analytics, Verbeke, Bravo, and Baesens pertinently bring forward a much-needed shift of focus that consists of turning analytics into a mature, value-adding technology. It further builds on the extensive research and industry experience of the author team, making it a must-read for anyone using analytics to create value and gain sustainable strategic leverage. This is even more true as we enter a new era of sustainable value creation in which the pursuit of long-term value has to be driven by sustainably strong organizations. The role of corporate employers is evolving as civic involvement and social contribution grow to be key strategic pillars.
Acknowledgments
It is a great pleasure to acknowledge the contributions and assistance of various colleagues, friends, and fellow analytics lovers to the writing of this book. This book is the result of many years of research and teaching in business analytics. We first would like to thank our publisher, Wiley, for accepting our book proposal.
We are grateful to the active and lively business analytics community for providing various user fora, blogs, online lectures, and tutorials, which proved very helpful.
We would also like to acknowledge the direct and indirect contributions of the many colleagues, fellow professors, students, researchers, and friends with whom we collaborated during the past years. Specifically, we would like to thank Floris Devriendt and George Petrides for contributing to the chapters on uplift modeling and profit-driven analytical techniques.
Last but not least, we are grateful to our partners, parents, and families for their love, support, and encouragement.
We have tried to make this book as complete, accurate, and enjoyable as possible. Of course, what really matters is what you, the reader, think of it. Please let us know your views by getting in touch. The authors welcome all feedback and comments – so do not hesitate to let us know your thoughts!
CHAPTER 1
A Value-Centric Perspective Towards Analytics
INTRODUCTION
In this first chapter, we set the scene for what is ahead by broadly introducing profit-driven business analytics. The value-centric perspective toward analytics proposed in this book will be positioned and contrasted with a traditional statistical perspective. The implications of adopting a value-centric perspective toward the use of analytics in business are significant: a mind shift is needed both from managers and data scientists in developing, implementing, and operating analytical models. This, however, calls for deep insight into the underlying principles of advanced analytical approaches. Providing such insight is our general objective in writing this book and, more specifically:
◼ We aim to provide the reader with a structured overview of state-of-the art analytics for business applications.
◼ We want to assist the reader in gaining a deeper practical understanding of the inner workings and underlying principles of these approaches from a practitioner's perspective.
◼ We wish to advance managerial thinking on the use of advanced analytics by offering insight into how these approaches may either generate significant added value or lower operational costs by increasing the efficiency of business processes.
◼ We seek to prosper and facilitate the use of analytical approaches that are customized to needs and requirements in a business context.
As such, we envision that our book will facilitate organizations stepping up to a next level in the adoption of analytics for decision making by embracing the advanced methods introduced in the subsequent chapters of this book. Doing so requires an investment in terms of acquiring and developing knowledge and skills but, as is demonstrated throughout the book, also generates increased profits. An interesting feature of the approaches discussed in this book is that they have often been developed at the intersection of academia and business, by academics and practitioners joining forces for tuning a multitude of approaches to the particular needs and problem characteristics encountered and shared across diverse business settings.
Most of these approaches emerged only after the millennium, which should not be surprising. Since the millennium, we have witnessed a continuous and pace-gaining development and an expanding adoption of information, network, and database technologies. Key technological evolutions include the massive growth and success of the World Wide Web and Internet services, the introduction of smart phones, the standardization of enterprise resource planning systems, and many other applications of information technology. This dramatic change of scene has prospered the development of analytics for business applications as a rapidly growing and thriving branch of science and industry.
To achieve the stated objectives, we have chosen to adopt a pragmatic approach in explaining techniques and concepts. We do not focus on providing extensive mathematical proof or detailed algorithms. Instead, we pinpoint the crucial insights and underlying reasoning, as well as the advantages and disadvantages, related to the practical use of the discussed approaches in a business setting. For this, we ground our discourse on solid academic research expertise as well as on many years of practical experience in elaborating industrial analytics projects in close collaboration with data science professionals. Throughout the book, a plethora of illustrative examples and case studies are discussed. Example datasets, code, and implementations are provided on the book's companion website, www.profit-analytics.com, to further support the adoption of the discussed approaches.
In this chapter, we first introduce business analytics. Next, the profit-driven perspective toward business analytics that will be elaborated in this book is presented. We then introduce the subsequent chapters of this book and how the approaches introduced in these chapters allow us to adopt a value-centric approach for maximizing profitability and, as such, to increase the return on investment of big data and analytics. Next, the analytics process model is discussed, detailing the subsequent steps in elaborating an analytics project within an organization. Finally, the chapter concludes by characterizing the ideal profile of a business data scientist.
Data is the new oil is a popular quote pinpointing the increasing