Profit Driven Business Analytics

A Practitioner's Guide to Transforming Big Data into Added Value

Wouter Verbeke, Bart Baesens, Cristian Bravo

• Publisher: Wiley
• Year: 2017
• Print ISBN: 9781119286554
• eBook ISBN: 9781119286981
• Edition: 1
• Topic: Business
• Subtopic: Decision Making
• Index: Business

Maximize profit and optimize decisions with advanced business analytics

Profit-Driven Business Analytics provides actionable guidance on optimizing the use of data to add value and drive better business. Combining theoretical and technical insights into daily operations and long-term strategy, this book acts as a development manual for practitioners seeking to conceive, develop, and manage advanced analytical models. Detailed discussion delves into the wide range of analytical approaches and modeling techniques that can help maximize business payoff, and the author team draws upon their recent research to share deep insight about optimal strategy. Real-life case studies and examples illustrate these techniques at work, and provide clear guidance for implementation in your own organization. From step-by-step instruction on data handling, to analytical fine-tuning, to evaluating results, this guide provides invaluable guidance for practitioners seeking to reap the advantages of true business analytics.

Despite widespread discussion surrounding the value of data in decision making, few businesses have adopted advanced analytic techniques in any meaningful way. This book shows you how to delve deeper into the data and discover what it can do for your business.

• Reinforce basic analytics to maximize profits
• Adopt the tools and techniques of successful integration
• Implement more advanced analytics with a value-centric approach
• Fine-tune analytical information to optimize business decisions

Both data stored and streamed has been increasing at an exponential rate, and failing to use it to the fullest advantage equates to leaving money on the table. From bolstering current efforts to implementing a full-scale analytics initiative, the vast majority of businesses will see greater profit by applying advanced methods. Profit-Driven Business Analytics provides a practical guidebook and reference for adopting real business analytics techniques.

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:

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• 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.

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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.

Business Analytics

Data is the new oil is a popular quote pinpointing the increasing value of data and—to our liking—accurately characterizes data as raw material. Data are to be seen as an input or basic resource needing further processing before actually being of use. In a subsequent section in this chapter, we introduce the analytics process model that describes the iterative chain of processing steps involved in turning data into information or decisions, which is quite similar actually to an oil refinery process. Note the subtle but significant difference between the words data and i...

Table of contents

• Cover
• Wiley & SAS Business Series
• Title Page
• Table of Contents
• Foreword
• Acknowledgments
• CHAPTER 1: A Value‐Centric Perspective Towards Analytics
• CHAPTER 2: Analytical Techniques
• CHAPTER 3: Business Applications
• CHAPTER 4: Uplift Modeling
• CHAPTER 5: Profit‐Driven Analytical Techniques
• CHAPTER 6: Profit‐Driven Model Evaluation and Implementation
• CHAPTER 7: Economic Impact
• About the Authors
• Index
• End User License Agreement