Fail Fast, Learn Faster E-Book

Randy Bean

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 371

Veröffentlichungsjahr: 2021

Beschreibung

Explore why -- now more than ever -- the world is in a race to become data-driven, and how you can learn from examples of data-driven leadership in an Age of Disruption, Big Data, and AI In Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI, Fortune 1000 strategic advisor, noted author, and distinguished thought leader Randy Bean tells the story of the rise of Big Data and its business impact - its disruptive power, the cultural challenges to becoming data-driven, the importance of data ethics, and the future of data-driven AI. The book looks at the impact of Big Data during a period of explosive information growth, technology advancement, emergence of the Internet and social media, and challenges to accepted notions of data, science, and facts, and asks what it means to become "data-driven." Fail Fast, Learn Faster includes discussions of: * The emergence of Big Data and why organizations must become data-driven to survive * Why becoming data-driven forces companies to "think different" about their business * The state of data in the corporate world today, and the principal challenges * Why companies must develop a true "data culture" if they expect to change * Examples of companies that are demonstrating data-driven leadership and what we can learn from them * Why companies must learn to "fail fast and learn faster" to compete in the years ahead * How the Chief Data Officer has been established as a new corporate profession Written for CEOs and Corporate Board Directors, data professional and practitioners at all organizational levels, university executive programs and students entering the data profession, and general readers seeking to understand the Information Age and why data, science, and facts matter in the world in which we live, Fail Fast, Learn Faster p;is essential reading that delivers an urgent message for the business leaders of today and of the future.

Leseprobe

Table of Contents

COVER

TITLE PAGE

COPYRIGHT

DEDICATION

FOREWORD

PREFACE

INTRODUCTION: FAIL FAST, LEARN FASTER

Notes

1 A Little History of Big Data

The Big Data Value Proposition

The Implications of Big Data for Large Companies

The Transformational Impact of Big Data

The Business Value of Big Data

Notes

2 Think Different: Becoming Data-Driven

The Disruptive Power of Data

The Challenge for Legacy Companies

The Distinctive Characteristics of Data

Building a Modern Data Environment at Fannie Mae

Thinking Different in the Life Sciences: Creating a Platform for Real-World Evidence

Building a Unified Data Platform at Anheuser-Busch (AB Inbev)

Using Data to Address Natural Calamities at Munich Re

Notes

3 Insight and Knowledge: Data, Science, and Facts

Data Initiatives Successes and Failures

Organizing for Data-Driven Analysis at Cigna

Using Data for Clinical Trails: Driving a COVID-19 Cure at Parexel

Building a World-Class Genetics Center at Regeneron

Using Data to Reduce Healthcare Costs at the Health Transformation Alliance

Notes

4 The State of Data in the Corporate World Today

The 2021 Big Data and AI Executive Survey Findings

A Summary of the Key Themes

Data Investment Is Strong and Growing

Companies Struggle to Become Data-Driven

Cultural Obstacles Continue to Be the Greatest Impediment to Success

The Chief Data Officer Role Continues to Evolve

Companies Are Optimistic About the Future

For Mainstream Companies, the Journey Continues

Note

5 The Great Challenge: Establishing a Data Culture

Fighting an Uphill Battle

Understanding the Cultural Impediments

How Companies Can Overcome Cultural Obstacles

From Data-Rich to Data-Driven: Building Data Literacy at AmFam

Cultural Transformation: Building a Data Culture Program at Travelers Insurance

Creating an Enterprise Data Office at Nationwide Insurance

Integrating the Human and Technology Dimensions of Data at Northern Trust

Notes

6 The Rise of the Chief Data Officer

Sexiest Job of the Twenty-First Century

Emergence of the Chief Data Officer

Rethinking the Role of Chief Data Officer

Chief Data Officers Struggle to Make an Impact

The Evolution of the Chief Data Officer Role

The Future of the Chief Data Officer Role

Shaping the Role of the CDO: MIT's Chief Data Officer Symposium

Organizing the Chief Data Officer Function at Citizens Financial Group

Chief Data Officer 4.0: Evolution of the Function at Scotia Bank

Emphasizing Data Privacy and Data Ethics at Mastercard

The U.S. Federal Government CDO Initiative

Notes

7 Data Responsibility: A Word on Data Ethics

Weapons of Math Destruction?

Big Data for Social Good Initiatives

The Emergence of Data Ethics

Data for Social Good: Bloomberg's Data for Good Exchange

Doing Good and Doing Well: Mastercard's Center for Inclusive Growth

Data-Driven Responsible Investing at TIAA/Nuveen

Notes

8 Data, Innovation, and Disruption

The Ways Big Data Drives Disruption

Alternative Visions of Data-Driven Disruption

Data Disruption Through FinTech

Allstate's Data-Driven Innovation Initiatives

Another Perspective on Disruption and Change

The Limitations of Data-Driven Disruption

Notes

9 A Glimpse of the Future: Data-Driven AI

Understanding Machine Learning Versus AI

Delivering Business Value from AI

The Capital One Story: A Pioneer in Data-Driven Management Invests in AI

JP Morgan Stakes a Commitment on AI

The AI Transformation Initiatives of TD Bank

Charles Schwab's AI Transformation Commitment

Notes

10 BecomingData-Driven: One Company's Odyssey

The Foundation of Data-Driven Transformation

The Ten Commandments of Data-Driven Business Transformation

One Company's Odyssey: The Data-Driven Journey of American Express

Notes

CONCLUSION: A DATA-DRIVEN JOURNEY

Notes

ACKNOWLEDGMENTS

ABOUT THE AUTHOR

INDEX

END USER LICENSE AGREEMENT

List of Tables

Chapter 4

Table 4.1 NewVantage Partners' 2021 Executive Survey Participating Companies...

Guide

Cover Page

Title Page

Copyright

Fail Fast, Learn Faster

Foreword

Preface

Introduction: Fail Fast, Learn Faster

Table of Contents

Begin Reading

Conclusion: A Data-Driven Journey

Acknowledgments

About The Author

Index

Wiley End User License Agreement

Pages

i

ii

iii

iv

vii

viii

ix

xiii

xiv

xv

xvi

xvii

xix

xx

xxi

xxiii

xxiv

xxv

xxvi

xxvii

xxviii

xxix

xxx

xxxi

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

229

230

231

232

233

234

235

236

237

238

239

240

Praise for Fail Fast, Learn Faster

Big Data is indeed crossing the chasm that separates early adopters from the mainstream of enterprise customers. As Randy Bean makes abundantly clear in Fail Fast, Learn Faster, this crossing is by no means smooth. Through a wonderful hoard of business anecdotes, he shows over and over again that the challenge of becoming a data-driven business has little to do with the Big Data itself, and is only marginally about mastering the technology needed to harness it. Rather, it is primarily about leadership teams finding forcing functions that can drive massive change in the roles, processes, and systems that make up their enterprise. For some the forcing function will be regulatory demands, for others a global market demanding draconian cost reductions, for others an emerging competitive threat from digital disrupters, and for still others, a mission to solve customer problems that cannot be addressed by conventional means. As a reader of Randy's book, it is your job to determine the forcing function that will drive change through your organization and to draw upon his wealth of examples to navigate your way forward.

—Geoffrey Moore, author of Crossing the Chasm and Zone to Win

In a whirlwind tour of real-world business cases, Randy Bean gives us an ironclad set of lessons: to benefit from the increasing deluge of data now available, businesses must first have a rational strategy for what they are trying to do along with a practical plan for architecting the data management function, as well as a set of standards and practices for how they deal with the data. But most important, they must also be prepared to steer an often-resistant enterprise in what can be an entirely new direction, and this is where the learn fast/fail faster discipline is vital. If you want to learn fast yourself, then start with this book.

—Don Peppers and Martha Rogers, PhD, authors of The One to One Future: Building Relationships One Customer at a Time

Inspiring, terrifying, but always usefully instructive, Randy Bean even-handedly lays out the indisputable case for why every organization needs a Big Data strategy and what it takes to build and act on it. Filled with great first-hand examples, “profiles in courage,” and learning about what has succeeded and failed, the book is true to its title as it takes you through the tough lessons, worthy risks, and core imperatives of life competing in the Big Data future. This is a book that should be required reading across C-suites and boards, because if they don't read it, they will be overrun by those who did.

—Dave Edelman, former chief marketing officer, Aetna; former lead, McKinsey's Digital Marketing Practice

Randy Bean has written the quintessential book for organizations striving to become data-driven. While many books often cover the technology aspects of Big Data and AI, Bean uniquely strikes at the heart of why so few have been able to capitalize on their investments in data: culture and people. Bean covers the complex but critical topic of data and AI ethics. Fail Fast, Learn Faster is both inspiring and cautionary, weaving case studies, data, and best practices, making it a must-read for CEOs, CDOs, and all data and analytics leaders.

—Cindi Howson, chief data strategy officer, ThoughtSpot; host of The Data Chief podcast

Randy Bean is the preeminent writer on the modern data revolution. In this book, he explains in straight-talking business terms why data is foundational to success and how to develop a strategy to seize value from data. The data journey is continuous, full of twists and turns, as he outlines in his case studies. Ultimately companies that understand the value of data and work purposefully, collaboratively, and iteratively to sharpen their capabilities will reap outsized rewards. As Randy shows, you have to take the steps to make the leap, and he gives actionable examples to accelerate your journey.

—Allison Sagraves, one of Corinium Intelligence's 2020 Global Top 100 Innovators in Data & Analytics; named to CDO Magazine's 2020 Global Data Power Women

In his new book Fail Fast, Learn Faster Randy Bean has delivered a compelling summary of the why, what, and how companies must become data-driven to compete in the coming decades. Randy's mix of strategic perspective and pragmatic know-how is truly unique and clearly articulated in Fail Fast. I consider Randy's new book to be required reading for all of my employees, customers, and partners who are working hard to become data-driven.

—Andy Palmer, co-founder and CEO, TAMR; investor and founding board member, Vertica, VoltDB, CloudSwitch

Randy Bean's book Fail Fast, Learn Faster takes the reader past the hype of Big Data and AI to the core business lesson: being data-driven is at its heart the art of basing business decisions on a continual cycle of experiment and assessment. Randy's excellent book will help readers understand this simple-sounding idea and helps them take the steps required to create a data-driven organization.

—Alex Pentland, author of Social Physics; Toshiba Professor at MIT; director at MIT Media Lab

Among the data and analytics strategists who can capture and articulate the essence and lessons of enterprise initiatives, Randy Bean stands alone. His new book, Fail Fast, Learn Faster, gives us not only sage guidance but also hope for the future of data's preeminence as a corporate asset.

—Douglas Laney, author of Infonomics; Innovation Fellow, West Monroe

Randy Bean has been an intimate witness to the explosion of data use in business—first as a data geek, and then as a chronicler for the Wall Street Journal and other business publications. In this lively book, he explores how data has become an essential element of business strategy in the twenty-first century. He teaches how to make effective use of the modern role of chief data officer and to think about data not as a source of revenue but as a driver of successful business outcomes.

—Cameron F. Kerry, Ann R. and Andrew H. Tisch Distinguished Visiting Fellow, The Brookings Institution

A terrific guide that tells the compelling story of the first generation of data management practices and how a range of skills are needed to realize data's powerful future.

—JoAnn C. Stonier,CDO Magazine 2020 Global Data Power Woman; co-chair, World Economic Forum Data Policy Council of the Future

In Fail Fast, Learn Faster, Randy Bean brings to life the rise of the chief data officer and the importance of data management in today's economy. The CDO has gone from humble beginnings to a dominant C-suite executive. Randy chronicles this journey in a way that the importance of this role is easy to understand and necessary to appreciate.

—John Bottega, president, EDM Council; former chief data officer, CitiGroup, Bank of America, Federal Reserve Bank of New York

People, machines, and pathways, from healthcare to commerce, are generating data at a rate unparalleled in history. Fail Fast, Learn Faster is the story of how organizations must adapt to this proliferation of data or face inevitable disruption. Randy Bean draws upon his experience as a participant in the rise of Big Data and his role in helping companies learn to think differently and adopt new approaches to grow and compete in a data-driven world. Fail Fast, Learn Faster is an illuminating tale about the power of data, the cultural impediments to data success, the need for data responsibility and ethics, and lessons in data-driven leadership. Highly recommended for business executives and general readers seeking to understand the data phenomenon and why we must embrace data, science, and facts, to achieve and sustain success in this new era.

—Michael Skok, founding partner, Underscore VC; Entrepreneur-in-Residence, Harvard Business School

A great read, chock-full of insights! Randy Bean transcends the bits and bytes and digs in on what it really takes to manage and lead in the age of Big Data.

—George Overholser, vice chancellor, Northeastern University; founding team member, Capital One

The Information Age is upon us and changing the ways that we live and work. Fail Fast, Learn Faster is a journey into this Big Data revolution. Randy Bean's cogent narrative and insightful storytelling deliver practical lessons in how to avoid the pitfalls that come with creating a data-driven organization. Most importantly, Fail Fast, Learn Faster shines a light on the human and cultural challenges that organizations face. Information is power, and now more than ever, those who ignore data do so at their peril.

—Richard Mucci, former president, Group Protection Business, Lincoln Financial Group; former chairman and CEO, New York Life International

Fail Fast, Learn Faster

Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI

Randy Bean

Foreword by Thomas H. Davenport

Copyright © 2021 by John Wiley & Sons, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

Published simultaneously in Canada.

No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, [phone], fax [phone], or on the Web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, [phone], fax [phone], or online at http://www.wiley.com/go/permissions.

Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at [phone], outside the United States at [phone] or fax [phone].

Wiley publishes in a variety of print and electronic formats and by print-on-demand. Some material included with standard print versions of this book may not be included in e-books or in print-on-demand. If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http://booksupport.wiley.com. For more information about Wiley products, visit www.wiley.com.

Library of Congress Cataloging-in-Publication Data

Names: Bean, Randy, author.

Title: Fail fast, learn faster : lessons in data-driven leadership in an age of disruption, big data, and AI / Randy Bean ; foreword by Thomas H. Davenport.

Description: Hoboken, New Jersey : Wiley, [2021] | Includes index.

Identifiers: LCCN 2021021562 (print) | LCCN 2021021563 (ebook) | ISBN 9781119806226 (Hardback) | ISBN 9781119806240 (ePDF) | ISBN 9781119806233 (ePub)

Subjects: LCSH: Leadership—Technological innovations. | Business—Technological innovations. | Business—Data processing. | Artificial intelligence.

Classification: LCC HD57.7 .B42794 2021 (print) | LCC HD57.7 (ebook) | DDC 658.400285—dc23

LC record available at https://lccn.loc.gov/2021021562

LC ebook record available at https://lccn.loc.gov/2021021563

Cover image: © Getty Images / Sbayram

Cover design: Paul McCarthy

To

Beth Black

Matthew Bean

Christopher Bean

May every journey be an adventure.

FOREWORD

I suspect that if you bought or picked up this book, you are already convinced of the value of data, both big and small. You already believe that it can transform companies. You already admire the “born digital” companies that have turned data into incredible value. You don't need to be convinced that every company is now a data company. You just want to find out how best to make this all happen.

But I also suspect that you, like many fans of data, believe that the most effective key to its successful use is the latest information technology. Turn some Hadoop, some Python, some Tensorflow, some Pytorch loose on your data, and it will begin to sing. Throw in some Internet of Things sensors, some edge computing, a pinch of deep learning, and perhaps a little augmented/virtual reality, and it will all start to make sense. Maybe you think that you will read about those cool technologies in this book.

If so, I believe you are wrong, but you should not be the least bit disappointed in the book you have before you. It is perhaps even more important for “full tech stack” devotees to read this book than for the fiercest Luddite to do so. Many people believe that technology is the key to organizations becoming data-driven. But let me give you one statistic from Randy Bean's own annual survey that may disabuse you of that notion.

As Bean explains further in the book, every year he conducts a survey on big data, analytics, and AI issues among large companies, most of them in the financial services or healthcare/life sciences industry. These are admittedly mostly “legacy” companies, but they spend huge amounts of money on technology and data, and hire some very smart people to manage them. When Bean surveys these individuals, he asks them an interesting question (among many others):

“What is the principal challenge to your organization becoming data-driven?”

There are only two possible responses: (1) people/business process/culture and (2) technology. In the most recent 2021 survey, 92% of these well-paid and smart people in big companies pointed the finger at people/process/culture, and only 8% believed the problem was technology. The numbers in the previous four years in which he asked the question on the survey were approximately the same.

This failure to make progress may seem discouraging, but you have picked the right book to cheer yourself up. The focus of Fail Fast, Learn Faster is primarily how ordinary human beings can use data and technology to improve their businesses. Randy Bean has worked with many of the largest companies on Earth, and he often tells me when we attend Boston Red Sox games together that the problems he encounters are almost exclusively human, cultural, organizational, and political. And that has been my experience as well in a long career of working with the same types of people and organizations.

The lessons of this book are delivered in clear language and without technical jargon. I've worked with Bean for almost 20 years, and I've read a lot of his writing. He prides himself on his ability to communicate about technical subjects to people with no technical backgrounds. If you are someone in a business role who has heard about such topics as big data, artificial intelligence, and digitization, and you want to know what all the fuss is about without getting lost in technical detail, you have come to the right place. Bean has made a successful career out of telling senior business executives what technology and data mean to them in clear terms. Many of them have turned to him in part because they can't understand what their own technology people are telling them.

Part of that communications ability is based on effective storytelling, and Bean has included many story-based examples in the book. His consulting firm works with many of the executives and companies profiled in these pages, so he has the ability to provide context and broad perspective on the issues addressed in each situation. They are the classic recurring themes in technology management in business: how to align technology efforts with business strategy, how business leaders and technology managers can collaborate effectively, how large, established firms can compete with disruptive startups, and so forth.

That last question about disruptive startups is, I believe, at the heart of the book. You will find occasional mentions of Facebook, Amazon, and Google in this volume, but the bulk of the examples are about large, well-established businesses that are trying to transform themselves. They know that a really successful startup could eat their lunch if they don't protect it. Jamie Dimon, CEO of JPMorgan Chase, was asked whether a hypothetical “Bank of Amazon” or “Bank of Google” would seriously threaten his bank's success in the industry. Dimon said, “Of course …We have very aggressive players trying to compete in our business. And we'll always compete very aggressively.” That is the challenge of the age for many of the firms described in this book. They need to compete effectively with digitally native firms if they are going to survive over the long run. They – and you – will find plenty of examples of firms that have thus far used modern competitive weapons to keep the wolf away from the door.

One of my favorite stories in the book features a veteran data and analytics leader, Ash Gupta, at a veteran firm, American Express. Gupta rose to the position of president, Global Credit Risk and Information Management, at American Express over the course of a 41-year tenure. To me his story illustrates several key points about the introduction and management of information technology in large organizations, and how much one individual can do to improve a big company over time. Gupta's long career at American Express reminds us that the game of building technology, data, and analytics capabilities has a long season that is heavily shaped by individual leaders. For four decades Gupta continually innovated at the company, eventually embedding data and analytics at the core of the enterprise. Randy Bean's writings about Gupta embody many lessons that are present throughout the book, but I was particularly reminded of two.

One is to take a long-term perspective. We often get caught up in breathless news about the latest technology and the fastest-growing vendors. But the creation of a data-driven company like American Express happens over decades. Bean takes this long-term perspective throughout the book, which makes it unusual among books about information technology.

The other key lesson is the importance of talent acquisition in creating this type of continuous renewal. Gupta brought in many of the best and brightest data and analytical minds available throughout the world. I regularly meet very smart people throughout the financial services industry who tell me with pride, “Ash Gupta recruited me to Amex.” That background tells me that they will be not only smart and well-educated, but oriented to the business and able to fit into a collaborative culture.

Gupta was present at a meeting that Bean and his company NewVantage Partners convened in New York in February 2020, which was my last trip before the COVID-19 pandemic curtailed my travel for more than a year. One topic we discussed was educating managers about the importance and value of data. Gupta's comments were a reminder of the very human approach needed to succeed with this topic. He said that his approach to educating leaders was to embark on a set of one-on-one learning sessions with the company's most senior executives. He knew that was the best way to make the lessons personal, and he could draw upon a deep well of trust he had built with these leaders over the decades.

Don't get me wrong; this book isn't just a collection of heartwarming stories about wise and capable people like Ash Gupta. There is plenty of solid and easily understood advice about data and technology management, including topics like data lakes, DataOps, data lineage, real world evidence, machine learning–based image recognition, and many other topics. All are made both more interesting and more relevant with up-to-the-minute examples from leading companies. But the book is a reminder that the world of data and technology management in companies is just as much about relationships among people as it is relationships among data. Data requires accuracy and integrity, but so do the people who manage it. We need to trust our data, and we need to trust the human beings who help to create, capture, store, and analyze it. Data informs humans, and humans inform data. Read this book to find many examples and lessons about humans finding ways to make data support better ways of doing business.

Thomas H. Davenport

Distinguished Professor, Babson College

Visiting Professor, Oxford University

Fellow, MIT Initiative on the Digital Economy

PREFACE

“Perfect is the enemy of good.”

—Voltaire

I wrote this book during the second winter of COVID-19, 2020–2021. Travel was not an option. I had the time.

I had been writing articles and columns for many years, published in the Wall Street Journal, Forbes, MIT Sloan Management Review, and Harvard Business Review. People asked if I was ever going to write a book. I told them only if it was the next Moby-Dick, or a social comedy or observation of life in the seaside village that I moved to part-time a dozen years ago. Perhaps an updated Peyton Place. My neighbors can breathe a sigh of relief. No kiss and tell this time around.

They say write about what you know. I had never been a Pacific whaler, so I could not rewrite Moby-Dick. However, I had lived and worked for four decades during one of the periods of greatest technological transformation in modern times – the Information Age.

I began my career working in a big bank in Boston (an old bank, too – the motto was “Founded in 1784”). I had no technical skills or business background but was trained by the bank at their expense to be a computer programmer (in Cobol). To my surprise, and maybe the bank's as well, they thought I was good at it and so asked me to keep it up. I grew restless though and moved to the business side of the bank (strategic planning). I was curious about how businesses operate and how decisions were made. Working as a computer programmer, I wrote computer programs that moved data around. I asked what the organization did with all this data, and whether it could be analyzed to arrive at better decisions. I was met with blank stares.

After a decade I joined a company that specialized in data (database marketing) and helping very big companies use that data to better understand their customers – get, keep, grow. One of my customers was Steve Ballmer at Microsoft – even the tech gods believe that to stay on top you need to relentlessly probe the data. It was the Internet era. I went on to become an executive with two Internet startups, and a founding executive (prefunding) of the second one – our lead investors included Kleiner Perkins. It was a whirlwind.

After that failed, and in the wake of 9/11, I needed a change. I launched a management consulting business with a colleague (an MIT PhD computer scientist). Our focus was on data and how big companies could use data to be smarter and better at what they did. I have been doing that ever since.

I wrote this book to tell a story, based on my four decades of business experience in and around data and business transformation. My aim is simple – to educate, provoke, delight, and tease.

When I am not doing business, I happen to sit on the board of a nonprofit where I serve as a chair for what is an internationally distinguished writer's program. These writers have won many book awards, including Pulitzer and Nobel Literature prizes. In the business world, I am called the guy who writes. In the literary world, they call me the guy who is in business. I once shared a collection of my Wall Street Journal columns with one of our board advisors, a head of the English department at a prestigious university – he called them “light and lively.” I am not sure whether this was intended as a compliment or not, but I'll take it.

This book then is intended to be light and lively and engaging, yet also a provocative and in some ways cautionary look at the past two decades in particular – the Big Data era, and how big companies are undertaking data-driven business transformation. I wouldn't ever pretend to be expert in all aspects of Big Data – my business colleagues are more current and have greater expertise in the analytical and technical areas than I do. I have, however, been a highly engaged witness and observer, someone who has operated for decades in and around the center of the Big Data revolution. I understand how organizations care about delivering results and measurable business benefits. When the day is done, nobody cares whether they are using the most elegant algorithm or coolest technology. Simply put, “That stuff don't matter.”

I hope you find this story compelling and informative. Pardon any repetitions. My experience is that you often need to say the same thing, many times, in many ways, for the point to sink in. Nothing is perfect. Perfect is the enemy of good. If you find this book to be thought-provoking and instructive, that would be good enough for me.

Randy Bean

Stonington Borough, CT | Boston, MA

November 2020–January 2021

INTRODUCTION: FAIL FAST, LEARN FASTER

“Ever tried. Ever failed. No matter. Try again. Fail again. Fail better.”

—Samuel Beckett

The world is in a race to become data-driven – now more than ever. The warp-speed effort to organize scientific and epidemiological data from across the globe in a heroic effort to find a COVID-19 vaccine has illustrated the urgency and existential nature of this quest. We need data, science, facts, knowledge, and insight to make informed, wise, and critical decisions. Now more than ever, data matters, and having good data matters tremendously.

Becoming data-driven doesn't just happen. It requires leadership, and vision. Be it in the business world, government, scientific communities, universities, professional sports, or other facets of society, data-driven leadership can be what distinguishes organizations that succeed, that learn and prosper, and grow and reinvent themselves, from those that fail in their efforts to do so.

Today, we live and operate in a world that is increasingly impacted by the existence of Big Data. Big Data refers to the existence of extensive sources and repositories of data of many different forms and varieties, which have become available in increasingly vast quantities in recent decades. To enable insight and knowledge, these sources of data must be identified, captured, and analyzed. In business, data is the lifeblood that drives competition, innovation, and disruption.

Since its emergence, a decade ago, Big Data has proven itself to be a transformational force that is having a profound and revolutionary impact in many ways on the global economy. It has become a driver of economic and business disruption. The emergence of data-driven artificial intelligence (AI) adds a further dimension, which holds the potential to accelerate the breadth and speed of innovation. Big Data has become pervasive in existence and in its use.

To claim revolutionary significance for Big Data is not to engage in hyperbole. In October 2012, Erik Brynjolfson and Andrew McAfee published a landmark article in the Harvard Business Review proclaiming “Big Data: The Management Revolution.”1 Two years later, Viktor Mayer-Schönberger of Oxford and Kenn Cukier of The Economist published their work, Big Data: A Revolution That Will Transform How We Live, Work, and Think.2

Extolling the “revolutionary” potential of Big Data soon became commonplace. Thomas Harrer, chief technology officer at IBM and IBM Distinguished Engineer, observes, “If you cast your mind back to a decade ago, the 10 highest valued companies were quite diverse but with a dominance of oil and gas. Now seven out of the 10 highest valued global brands are data companies. Data as the new oil? Clearly.”3 Revolutions imply disruption and a break from the past, from which point things are never the same and a new order or way of operating prevails. By any standard, Big Data is revolutionary.

Harkening back to another technology revolution, the distinguished British historian Ian Kershaw remarks in his work The Global Age: Europe 1950–2017, “The spread of the Internet in the 1990s had made the world smaller.”4 The same can be said of Big Data. The Internet transformed how we communicated with one another, made purchases, planned vacations, conducted business. It resulted in a beneficial transformation, delivering convenience, speed, and efficiency.

Big Data is having a similarly consequential impact. It represents a continuation of developments that emerged with the advent of the Internet and extends the ability to access information quickly through digital technology that increases speed, efficiency, and engagement.

As with any revolution, not all the consequences are positive. The Internet and its byproduct, social media, pose threats to individual privacy and risks to cybersecurity. The result can be the dissemination of disinformation and outright lies. In recent years, we have been operating in a dark and uncertain time when data, science, and facts have been repeatedly challenged.

* * * *

Analyzing data to make better decisions is not new. Data has long existed, and organizations and individuals have long sought to identify, aggregate, and analyze data – like reading tea leaves – to discern insights and make more informed decisions. In the beginning, data was a field inhabited primarily by specialists, who worked to organize relatively small amounts of data to develop insights. This changed suddenly and dramatically with the arrival of Big Data.

Big Data implies a new way of doing things, which results from a new set of approaches, technologies, and techniques that enable the accessing, managing, and analyzing of data. In a world that is highly dynamic and characterized by ever-faster rates of change, these new techniques and approaches enable executives and data analysts to see, use, and think differently about data and the questions they are seeking to answer. Big Data permits users of data to experiment and fail, to learn quickly from their mistakes, and to move forward with speed, agility, and confidence.

As data volumes and sources of data proliferate at ever-increasing rates, leading companies will be forced to plan for a data-driven future. What has fundamentally changed with the advent of Big Data is the scale at which data is being generated, and the speed and ease with which data can be organized and analyzed. Organizations are undertaking massive efforts and making extraordinary investments to prepare data for analysis so that insights can be gleaned.

Now more than ever, businesses and governments must rely on good data and analytics. Data is being used to make important business, scientific, medical, public health, and policy decisions that impact broad swaths of society. These decisions depend upon access to the very best data available.

Consider the global response to COVID-19 and how scientists, epidemiologists, pharmaceutical companies, hospitals, and communities and governments at city, state, and country levels across the world sought to gather data about the outbreak and its spread at a scale perhaps unprecedented in human history.

Today, we live and work in a world in which data sources and volumes are steadily proliferating. Data is being captured and analyzed for decision-making at a pace that has not existed in human history. It now exists in a variety of forms, including financial data, customer transaction data, scientific and medical data, marketing data, sensor data, and on and on. Data can represent numbers, words, documents, locations, pictures, and signals, among other indicators.

Developments in Big Data and AI are having an impact that is reshaping how we think about and engage data and is reaching into all corners of business and society. Companies like Amazon, Google, eBay, Facebook, Uber, and Airbnb are rooted in data and analytics and have leveraged new data-driven business models to disrupt and transform traditional industries such as retail, media, and travel. For innovative firms such as these, data brings speed, agility, and the ability to fail fast, learn from experience, and execute smarter.

Data is also transforming traditional businesses across many industries. From industrial systems to financial services, from media to healthcare delivery, from drug discovery to government services, from national security to professional sports, data is driving critical decision-making. The opportunity to deploy data and analytics has accelerated the speed at which companies can enter new markets, with new solutions, and quickly challenge or displace traditional competitors and market leaders.

Nearly all leading companies now state somewhere in their annual reports and business mission statements that data is a critical business asset, that they are striving to become data-driven in their analysis and thinking, that they are deeply engaged in forging a data culture at all levels of their organization, and that they view data as a basis for innovation and competition in the global marketplace. The Big Data revolution is here to stay.

Fail Fast, Learn Faster is a history and a chronicle of this Big Data revolution and its impact, as organizations strive to become data-driven. It represents a synthesis of developments and themes that have arisen with the ascendance of data over the course of the past two decades. In addressing these themes and questions, this book seeks to tackle one of the most disruptive dynamics facing leading corporations, government agencies, and social institutions today.

Progress does not come easily. This book describes how firms are using data to establish themselves as leaders as they innovate in their businesses and disrupt traditional markets, and how working with and using data becomes part of an organization's fundamental DNA. One aim of this book is to provide a window into the challenges that organizations face when they attempt to develop a data culture.

Executives and business leaders must ask themselves critical questions. Why should this matter to you? What can you learn from the experiences of others? How can you be successful in leading the data-driven charge? How do you avoid the pitfalls? How do you overcome the challenges? What does data-driven leadership mean? How do you reach your destination?

This book serves as a guide to understanding the evolution of data in the context of a changing world, where technology breakthroughs, the rise of consumer-driven services and self-service, and changing customer demographics are driving broader social and cultural implications. This is a story of how businesses have struggled to undertake corporate data transformation initiatives, and how they have sought to become data-driven.

Big Data is characterized by change and new approaches. Organizations are seeking to understand and appreciate how they can begin to derive value from the advanced application of data and analytics. There are many benefits to data-driven decision-making, including greater accuracy, precision, efficiency, and responsibility in the use of data.

* * * *

A central premise of Fail Fast, Learn Faster is that individuals and organizations learn through experience, and experience entails trial and error. “Ever tried. Ever failed. No matter. Try again. Fail again. Fail better.” This quote, from the twentieth-century Irish novelist, poet, and avant-garde playwright Samuel Beckett, offers a metaphor for data-driven change and the resulting disruption and innovation it is unleashing in an age of Big Data and AI.

One of the ways in which Big Data has helped fuel rapid innovation is through faster iterative learning – fail fast, learn faster, execute smarter. This book aims to educate organizations by providing a glimpse into paths taken, lessons learned, pitfalls to avoid, and realistic guidance on the steps, as well as the time horizon that it takes to develop a data-driven culture.

Paul Saffo, technology forecaster and managing director of San Francisco-based Discern Analytics, has observed, “Failure is the foundation of innovation.”5 In the world of data and analytics, corporations have long been bound by approaches that are costly and time-consuming, and that have hamstrung some of their more innovative ambitions.

John Bottega, one of the first executives to assume the role of chief data officer, holding this position at CitiGroup, the Federal Reserve Bank of New York, and Bank of America, comments, “Failure is informative. Even with imperfect data, business analysts can gain insight and knowledge with respect to the viability of an approach or hypothesis.”6

* * * *

Fail Fast, Learn Faster is the story of how data is impacting businesses and enabling companies that use it well to improve performance, drive efficiency, gain competitive advantage, and disrupt traditional ways of doing business. The great leaders and innovators in using data have transformed entire industries and now stand among the most highly valued and capitalized businesses in the world today, and in world history.

The book represents a summation of this period of change and the resulting transformation and leadership required to achieve success. The narrative is presented as a broad, historical, and cultural perspective on the rise of data-driven decision-making over three decades, and its impact across businesses and industries stretching into all corners of society.