Читать книгу Fail Fast, Learn Faster - Randy Bean - Страница 7

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