Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI

By Randy Bean and Thomas H. Davenport

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.

• Language: English
• Publisher: Wiley
• Release date: Aug 25, 2021
• ISBN: 9781119806233

Book preview

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

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

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

Published simultaneously in Canada.

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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

Reviews for Fail Fast, Learn Faster

[jpsnow · Rating: 4 out of 5 stars]

The true value of modern analytics isn’t about supercomputers mining big data to give us the the precise answer. The value derives from scaling up our speed in testing and learning. This book is current and practical. As an analytics leader, I especially appreciated the guiding principles, derived from countless case studies, for maximizing data-culture and enablement within a large corporation.