ISBN 978-111-9736-080 (Hardback)
ISBN 978-111-9736-103 (ePDF)
ISBN 978-111-9736-097 (epub)
Cover Design: Wiley
Cover Image: © DNY59/Getty Images
I would like to dedicate this book to my wife Claudia, whose endless patience, bright smile, and intelligence have always been a source of inspiration. I also want to acknowledge my parents and brother, who provided gentle guidance and love. I especially want to thank my children, Nicole, Dana, and Jonathan, who inspire and always bring out the best in me.
To Dana, forever in my heart.
Lawrence S. Maisel
This book is dedicated in loving memory of my mother, Joy, the merriment of my grandmother, Tess, and the wisdom and discipline of my grandfather, Ruby. The best of life and the greatest of gifts I have are from my sister, Alice, wife, Val, and daughter, Megan.
Robert J. Zwerling
This book was written in memories of my parents, who patiently helped me learn. I also want to acknowledge my wife, Anne. Without you this would not be possible.
Jesper H. Sorensen
Acknowledgments
We thank Kent Bearden, Jonathan Morgan, and Lisa Tapp for sharing their experiences and helping us learn the ways AI and analytics contribute to improving their operations. With gratitude, we also acknowledge the support and editorial assistance of Sheck Cho and Susan Cerra of Wiley, which enabled us to complete this book.
Introduction
Everywhere you turn, you hear or read about artificial intelligence (AI) and the emerging importance of digital transformation. To be competitive in modern business, decision-making needs to evolve into a more objective, insightful, and unbiased process that is powered by the application of AI-enabled analytics.
We have written AI-Enabled Analytics for Business: A Roadmap for Becoming an Analytics Powerhouse for executives to gain a solid understanding of AI and analytics that will give clarity, vision, and voice to integrating them in business processes that will be impactful and increase business performance.
Today, there is more promise than practice in implementing AI and analytics for data-driven decisions. As you will learn, there are twice as many analytics failures than successes, and there are twice as many successes that are abandoned rather than sustained. The good news is that almost all failure can be traced back to executive decisions that are entirely avoidable and easily identified.
Further, AI is not the sole purview of big companies, big data, and big data projects that seek to boil the ocean. The butcher, baker, and candlestick maker can all incorporate AI to increase productivity, reduce workforce, retain higher-skilled talent, and enhance the customer's experience. In fact, AI and analytics are better done incrementally, building on each success to scale the business to become an analytics powerhouse.
Our research, training, consulting, and on-the-ground experiences with AI-enabled analytics have shaped our perspectives, refined our practices, and tested our tactics. We have worked side by side with executives like you, and our empirical results demonstrate the critical factor to success is the executive's mindset to the value of analytics and commitment to allocate the resources to building the Analytics Culture. This book gives you the Roadmap to implement AI and analytics, which, as you will learn, the executive will make or break. As we will show, failure is a choice; the good news is that it is eminently avoidable, and we have specified the steps for success.
In Part I, we cover the fundamentals of AI and analytics, beginning in Chapter 1 to untangle the many seemingly synonymous terms, partitioning tools that do and do not do analytics, and the ROI of AI. It is essential to know the difference between analysis, which is the application of arithmetic on data to yield information, and analytics, which is the application of mathematics on data to yield insights. In Chapter 2, we illuminate why analytics is essential in business and share Noble Prize-winning research that recognizes the limitations of human decision-making based on biased intuition and gut feel, and why analytics must be included as the essential unbiased component. Chapter 3 discusses myths and misconceptions regarding the approach to analytics, and Chapter 4 takes you through several applications of AI and analytics across different business functions.
In Part II, we define the Roadmap for how to implement AI-enabled analytics for data-driven decisions and the contributions of executives for becoming an analytics powerhouse. Chapter 5 is the fulcrum of this book and delivers a detailed discussion of analytics as more than a tool—it is a culture with four components: Mindset, People, Processes, and Systems. When these components are aligned, immense value to optimize performance is created, and we delineate in depth how this is accomplished. In Chapter 6, you will learn that executive action determines the successful implementation of the Analytics Culture, and you will see what executive actions are needed. Further, we introduce the Analytics Champion, who supports the executive and delivers the tactical implementation of the Analytics Culture. In Chapter 7, we specify with clarity and simplicity how to implement analytics and show that achieving it is not time-consuming, hard, or expensive—it is a discipline. Chapter 8 links analytics to strategic decisions and debuts the new and innovative Analytics Scorecard, which elevates the traditional and subjective Business Scorecard into a quantitative cause-and-effect delineation of strategies that can drive increased business performance.
In Part III, we present specific use cases that illustrate key themes and confirm our approach and insights conveyed in earlier chapters. As there is more to learn from failure than success, Chapter 9 discusses instances across several industries where analytics successes became failures. Chapter 10 tells the story of a hospitality company's analytics proof of concept that yielded optimized staffing while maintaining excellent customer service, significant cost savings, and opportunities to boost revenue and profit—yet failed because the senior executive did not believe in investing in analytics. Chapter 11 is the story of achieving insights that incrementally progress toward a data-driven culture from analytics in demand planning and supply chain. Finally, Chapter 12 puts an exclamation point on the notion that AI and analytics are for everyone, not just big companies, through the story of a medium-size art museum and its CFO's curiosity, which led to learning about analytics and discovering how it provides insights.
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