Author & Founder of New Vantage Partners, Randy Bean: Fail Fast, Learn Faster

Anthony: Welcome to another episode of DataMasters. Today's guest is Randy Bean. Randy Bean is an industry thought leader, author, and speaker in the field of data-driven business leadership. He's the founder and CEO of New Vantage Partners, a strategic advisory firm to Fortune 1000 clients, which he founded in 2001. He's known for his frequent columns in Forbes, Harvard Business Review, and Mighty Sloan Management Review, and the Wall Street Journal, his new book Fail Fast Learn Faster, Lessons in Data-driven Leadership in the Age of Disruption, Big Data and AI, which we'll discuss today, was published in August. As we'll learn, Randy is a lifelong lover of literature. He serves as the co-chair of the James Merrill House an internationally acclaimed writer's residence program based in Stonington Borough, Connecticut, which hosts award-winning writers including recipients of the Pulitzer Prize Book Award, National Book Award, and the 2020 recipient of the Nobel Prize for Literature. And maybe most importantly, Randy has a unique distinction as one of the first repeat guests on the DataMasters Podcast. Welcome back, Randy. I look forward to discussing your new book.

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Randy Bean: Anthony. Thank you. And thank you to Tamr for hosting me again. It's always a pleasure.

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Anthony: I love the title of the new book, Fail Fast Learn Faster. Titles in books are always a difficult process. Why did you choose to name your book this way?

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Randy Bean: Yeah, thank you. Fail Fast Learn Faster is really intended as a metaphor for a number of things in the data space. It's meant for taking action as opposed to analysis paralysis. In the case of analytics, it's a metaphor for test and learn. But above all, it's a metaphor for changing and evolution. I borrowed the title from the Irish playwright Samuel Beckett that most known for writing the play Waiting for Godot and the quote was, "Ever tried, ever failed. No matter, try again. Fail again, fail better." And just as an example of other uses of this, Paul Saffo of the Prognostic Data has said, "Failure is the foundation of innovation," and Facebook is well known for their mantra, "Move fast and break things." So it was really an idea of bringing together all of these ideas in the context of using data as a means to change and evolution and helping organizations use data to gain insight and leadership.

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Anthony: Yeah, that's great. And I mean, you can see these themes about the link between moving forward and learning and failure. As you point out, these are literary themes as much as themes in business. So maybe share with that as a backdrop and this idea of learning. Maybe give listeners a brief synopsis about what the book's about. Entice them to head over to amazon.com and place a pre-order.

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Randy Bean: Yeah, absolutely. Well in a way it's all about storytelling and what metaphors do is provide an example of that. How people... Give them a concrete sense rather than writing something in too technical language. So I tried to write the book for three different audiences. First of all, senior business executives, these may be C-suite executives or board members. Secondly, I tried to write it for practitioners and I did that by providing numerous case studies, as roughly 25 case studies drawn from articles and conversations that I've had with Fortune 1000 organizations over the years. And then the third constituency is general readers, who particularly more recently in the period of COVID started hearing about data dashboards and the rush to accelerate development of vaccines, and this notion of bringing together data from all these different sources. We're only two decades removed from Moneyball, which was really the concept of bringing data to professional sports.

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Randy Bean: So really trying to do is apply some metaphors that made this concept of becoming data-driven relevant to any audience. And really the theme of the book, it's based upon my generation of experience in the data industry during the information revolution and looking back from what I've learned from that experience and looking to the future going forward. So I talk about how information has evolved, how it's proliferated at ever increasing rates over the past several decades. I talked about the change in mindset, why organizations need to think different in terms of becoming data-driven. I talked about the cultural challenges that organizations need to undertake. It's really the most organizations. It's not so much about acknowledging barriers, but human barriers and business processes and I can talk a little bit more about that. I discussed the evolving role of the chief data officer of something that didn't really exist a decade ago and how it symbolizes the commitment of major organizations to bring data to the C-suite.

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Randy Bean: And then I also talk about data ethics. Some people have asked me what's my favorite chapter in the book. And I say, it's the one about data ethics, because they say some of the use cases about successes are inspiring, but reading above the potential misuses of data is terrifying. And then I try to wrap up the book talking about data-driven artificial intelligence as what's coming in the years and decades ahead. And I conclude with a single long case study, which happens to be the story of American Express, an organization that I've worked with extensively over the past 20 years and written about on multiple occasions and really tell their stories, which goes through their attempts, their failures, how they learn from failures, how they undertook new initiatives. So they really embodied the Fail Fast Learn Faster metaphor.

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Anthony: And as you point out, you've had a long career in the industry and you've seen many things. Is there a particular reason that you wrote the book now? I mean, how much of this was being hold up in your house stuck because of COVID and how much of that is a function of where you sit in the long arc of history?

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Randy Bean: Yes, that's a great question. Well, first of all, looking last November, when the publisher reached out a long, cold winter with no place to go, it seemed like if ever there was the time to write a book, that was the moment. And plus having experienced a generation, I certainly have a perspective and a point of view you can call it a witness to history that may not be the French Revolution or the decline and fall of the Roman Empire, but for our time it's pretty relevant. And maybe I could start by reading a few opening lines from the book, which speak directly to your question about now. So the book begins, "The world is in a race to become data-driven now more than ever, but 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."

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Anthony: Absolutely. And I think that way of launching a book is exactly the point and just sort of tying two ideas together. You'd said you have this front seat to the information revolution. You've had a long career of looking at how data and information have transformed businesses. And in that way, I think the book is a really fun read because you, as you point out, you have a lot of these sort of personal stories embedded in the book, which capture these sort of big moments of change. And I'm a big believer that change isn't linear. It happens with these big disruptive moments that force change onto people, and they're very easy to see in retrospect and hard to see in the moment. And again, your book does a wonderful job of capturing those moments and sharing them in a really lovely way. But maybe you could share with the audience one or two of your favorite personal anecdotes that you shared in the book.

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Randy Bean: Yeah. Thank you for asking Anthony. Yeah. For me, it's always about stories and I gave a copy of the book to my son to read and he said, "Oh, well how come you didn't have those stories about the time you went to lunch and you spilt ketchup on your shirt before you met with the CEO of Bank of America, those types of stories. And I said, "Well, some stories are more apt than others." But there is a one story that I alluded to in the beginning of the book. And another story that I go into more extensively at the end of the book and the first story is, it was about 1996 and I was running the North American database marketing practice for our company in the database marketing space, which would now be known as CRM.

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Randy Bean: And I got a call from Microsoft in Redmond, Washington. And they said, "We think it's really important to have an understanding of our data so we can understand our customers and the totality of our customer relationships and identify ways to better retain and serve those customers. And we'd like it, if you could come out and meet with a couple of our executives here who have an interest in that." And I said, "Sure, I would be happy to. I have some time next week or the following week, if that works for you." And they said, "No, we were thinking this afternoon. And I had two young kids at the time, so it would be kind of disruptive, but I talked to my wife and said, "I have a hunch that this might be worthwhile, so I'm going to do it."

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Randy Bean: So I purchased the ticket, this was back when things were easier and prior to 2001, and flew to Redmond, Washington, flew to Seattle and traveled up to Redmond, Washington. I walked into the room and who was sitting there, but Steve Ballmer, and for those who know of Steve Ballmer it's... Well, what he was trying to do was stress the importance of data to the organization and how critical it was and something that he wanted to pay first-hand attention to. But for those who know Steve Ballmer, it was more like, "This is too important to delegate to anyone." And so it was a great experience and he was a gentleman all the time, but he was energetic and passionate and this was a priority for Microsoft. Another story is, it was about eight or nine years ago when I picked up the college alumni magazine and happened to see that an old college housemate of mine had been appointed Assistant Secretary of Defense for Research and Development, with something like the trillion dollar budget of some extraordinary sum.

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Randy Bean: And among the mandates that were listed for him was that he had oversight for looking at ways that the Pentagon could leverage big data and there were initiatives. So I reached out to him, it took me a while to figure out what the email address was for the Pentagon. But when I finally got it right, I was surprised to hear back from him within a few hours. And he said, "Well, it's really interesting the work that you're doing in big data. Can you come to the Pentagon next week to meet with a group of individuals here? Maybe share with us what organizations are doing in private industry. So the following week, I traveled down to the Pentagon. I've been to the Pentagon on the outside, never been on the inside, went through layers of security. They confiscated everything that I possibly had on me.

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Randy Bean: I think even my wedding ring, I believe, and walked into this room and there was about 18 people. And six of them had on full stars and bars, I guess you'd call it, more medals than I've ever seen. Another half dozen had on camouflage fatigues, and the other third were in a full suit and ties and I had no idea who the decision makers were, but they started talking about capturing data and analyzing data and using it for campaigns. I was used to dealing with American Express, so I was thinking marketing campaigns, increase the customer retention customer lift.

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Randy Bean: I soon learned they were talking about military campaigns and specifically they were talking about planning for drone strikes and so the quality of the data mattered significantly. And what they shared was, they were spending 80% of their time preparing the data and 20% of their time analyzing the data. And they want to know how in private industry, private industry had figured it out. So there was a much higher proportion of time spent on the analysis as opposed to the preparation. And I had to disabuse them of the notion that a private industry had any magic bullets or a better approach. And it was really pretty analogous in terms of the amount of time spent on data preparation, and that ties in nicely with the work that Tamr does.

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Anthony: Of course. So campaigns of a different type, maybe a little higher stakes. So my understanding is that one of the motivations for writing Fail Fast Learn Faster was to kind of reflect on and in a sense document the many decades of experience and work that you've had. And a lot of people listening to the podcast are just starting their careers or earlier in their career. And with the benefit of some experience, the ability to reflect, what advice would you give somebody starting their career in data today?

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Randy Bean: Well, I think data is the career of the future. So that's the good news. We conduct an annual survey of C-suite executives, CIOs, chief data officers, chief analytics officers, chief digital officers. And I'm going to share some of the findings because this may sound like a glass that's half empty, but it actually represents an opportunity. So we asked how many organizations are driving innovation with data. And the answer was 48.5%, so less than half. How many are managing data as a business asset today? The answer was 39.3%, so barely over a third. How many had fortunate data culture? 24.4%, so not even a quarter. And how many had created the data-driven organization? Only 24%. So what does that tell you? Well, it tells you that there's a lot of work to be done. And one of the things that I'm asked most frequently is how do we know when we've become data-driven and created the data culture?

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Randy Bean: And my answer is that it's not a destination, it's a journey, and it's a perpetual journey. And from my experience, the organizations that are most successful and a closest to being data-driven are those that have a relentless drive to improve. They're never satisfied. They're always looking over their shoulders. They're always worried about the competition. When I go into an organization and they say we've got things under control. We've got it figured out. Those are the organizations that I sincerely worry about. So for young people entering the field, but I think this is the field of the next decades, the next generation.

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Randy Bean: The volumes of data are only proliferating. Organizations are struggling to understand and manage these different types of data. So there's going to be a lot of work than I think data is going to permeate all aspects of society in terms of making more informed decision-making. It doesn't mean that you have to be a robot and just blindly follow what the data says, but it does mean that the more you consult the data that you have and combine it with human judgment and experience, most likely the better off you're going to be.

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Anthony: So one thing I didn't know about you prior to reading the book and spending time with you, was your intense love of literature. Obviously something you do in some of your work outside of work. When we talked before the episode, you told me about a lot of the great authors and their works that are central to the book. Maybe you could talk a little bit more about the connection between literature and the book.

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Randy Bean: Yeah. I believe that telling a story is important because you're trying to reach an audience and you're trying to really break through to that audience to develop an understanding. And from my experience, one of the ways that you can achieve with aside from storytelling is employing literary devices. So that's why, for example, the title is the Fail Fast Learn Faster metaphor borrowed from Samuel Beckett, at first I was debating what the title should be because I was focusing on this notion of a different mindset to become data-driven. And so for each of the chapters, I borrowed from a different quote to start off the chapters. And so, for example, chapter one, which tells the history of big data, it starts with the quote from the philosopher, George Santayana that says, "Those who don't learn from the past are condemned to repeat it," which seems to strike home every day in one form or another.

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Randy Bean: But chapter two is the chapter about thinking a different and it starts off, "Here's to the crazy ones, the misfits, the rebels, the troublemakers, the round pegs in the square holes, the ones who see things differently, they change things. They push the human race forward and while some may see them as the crazy ones, we see genius because the people who were crazy enough to think they can change the world are the ones who do." And in other cases, I have the chapter on data science and facts. I use this little quote from Mark Twain. He says, "Lies, damn lies and statistics," which is pretty funny because data can be used to support just about any argument.

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Randy Bean: And then I have a chapter on innovation and disruption, and I borrowed this quote from Ernest Hemingway, from his classic book, The Sun Also Rises. One of the characters asks, "How did you go bankrupt?" Then the other character responds, "In two ways, gradually and then suddenly," which is really a perfect metaphor for disruptive change because often you can't time it. So people will say, "Oh, we're fine. We have this great customer base. We'll get to this and we'll deal with this over time." And sometimes things change on a dime and that's how things are. And then lastly, I'll mention the chapter on data-driven AI. I found this nice quote from F Scott Fitzgerald. And he says, "I hope you see things that startle you. I hope you see things you've never felt before." So all of this is meant to give kind of an alore to maybe the little bit drier content that comprises each chapter.

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Anthony: Sure. And I have to admit Waiting for Godot is certainly one of my favorite plays and it's a strange play because really the story has very little plot, very little actually happens. But in a way it's a commentary on this idea that as humans we are what we do, that our choices and actions are to find our place in the world. And one of the things I really love about the book is this very action-oriented nature of it. This idea that the data sitting at rest is not that useful or interesting, it's what you do with it and the hypotheses you run and test and the actions that you take as a result. So I would highly encourage the book to all of our listeners. And you should decidedly not be like the characters in Waiting for Godot and do nothing. You should run out and pick it up and give it a read. So, Randy, thanks for joining us on DataMasters.

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Randy Bean: Anthony, it's always a pleasure. I always enjoy working with yourself and the entire team, Mandy Palmer on down, at Tamr. You're a great organization.