This episode features an interview with Randy Bean, CEO of NewVantage Partners. In this episode, Randy talks about what it means to be truly data-driven, growth in the industry, and the ethics around collecting and using data.
This episode features an interview with Randy Bean, CEO of NewVantage Partners. In this episode, Randy talks about what it means to be truly data-driven, growth in the industry, and the ethics around collecting and using data.
Quotes
“Being data-driven doesn't mean being a robot where you just look at the data and thereby make decisions blindly. You have to bring to bear human judgment and experience. But you should look at the data. You owe it yourself. You owe it to your customers, you owe it to your colleagues, and you owe it to the industry, to gather the best possible data, the most accurate, the most timely, the highest quality data you have. Look at it, review it, analyze it, synthesize it, digest it, and then make some decisions.”
Time Stamps
*[3:26] Introducing Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI
*[5:59] Why is good data so important?
*[10:55] Common issues in becoming data-driven
*[12:46] How to start on the pathway to being data-driven
*[15:19] Data in layman’s terms
*[19:59] Using data to remain agile
*[21:19] What does “data-driven” mean anyway?
*[24:40] The beginning of the data revolution
*[25:27] The good, the bad and the ugly data ethics
*[35:13] Communicating the data message
Links
Check out Randy’s book, “Fail Fast, Learn Faster”
Connect with Randy on LinkedIn
Connect with Lauren on LinkedIn
Thanks to our friends
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Lauren Vaccarello: Companies today are collecting massive amounts of data. On their products, employees, customers...You may have noticed more notifications about cookies when visiting different websites, for instance. And unless you live off the grid, you’re being tracked.
You may not know what companies are doing with your information. You might assume it’s being used for targeted advertising or to develop products. But you don’t know for sure. So today we’re talking with someone who does know. In fact, he’s the one advising companies on how to use that data, and do it with integrity.
Randy Bean is CEO of NewVantage Partners, helping Fortune 1000 companies become data-driven. He’s also a prolific writer, authoring 150-plus articles for Forbes, Harvard Business Review and The Wall Street Journal. And his new book comes out at the end of this month.
It’s called Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI. In it, Randy covers case studies of companies that have found success becoming data-driven. But he also covers the darker side of data ethics and the abuse of data.
Let’s talk about what it means to be truly data-driven, growth in the industry, and the ethics around collecting and using data.
So without further ado, let’s get into it. Welcome to Truth Be Known.
Hi, everybody. Welcome to another episode of Truth Be Known. We have another exciting guest today. We have Randy Bean, CEO, and founder of NewVantage Partners. Randy, welcome to the show.
Randy Bean: Thank you, Lauren. It's a pleasure being here.
Lauren Vaccarello: Yeah, we're really excited to have you here. And then for people who don't know about NewVantage Parnters, can you tell us a little bit about you, about the company?
Randy Bean: Sure. NewVantage Parters was founded in 2001. So we've been in business for 20 years. We work with large Fortune 1000 companies as strategic advisors, focused exclusively on data and analytics. We basically help them in four ways. We help them leverage data as a business asset, we help them become data-driven organizations, we work with them to forge a data culture, and we help them innovate with data in their businesses.
Lauren Vaccarello: No, I can imagine you've only gotten busier and busier over the last number of years doing this.
Randy Bean: Yes. You know, that's, what's kept us going for 20 years. People have been at this for a long time, but really see it as the tip of the iceberg and data initiatives are in their infancy. And even within the past decade, we've seen, for example, the emergence of the Chief Data Officer role within major corporations. And that's only accelerated in recent years.
Lauren Vaccarello: And I know you just released a brand new book or are about to release a brand new book called Fail, Fast, Learn Faster: Lessons in data-driven leadership in an age of disruption, big data and AI. I really want to dig into the book. I had a chance to go through it. And you've got a ton of great content in there in case studies and all of the things that you've been working on since since you wrote the book. Can you tell us more about it?
Randy Bean: I really wrote it as a summing up of my 35 years plus, so, so a generation in the data field, a perspective on how things have evolved, what I've learned, what are really the key issues and what organizations and data leaders need to think about in terms of planning for the future. So I begin by talking about a, really a history of how data has evolved over the past 35 plus years, in terms of new sources of data, the uses of data across a business, as well as in broader society. I talk about the mindset and the change of mindset that's required to really be a data-driven organization because data's an asset that flows across businesses and you really have to look at it from a very different perspective. I talk about the cultural challenges that organizations face. You know, the biggest thing that we hear from C executives of maybe major corporations, we do a survey each year and each year points out to, you know, 90% of the challenges for organizations rarely relate to a cultural issues. Organizational alignment, changes in processes, skillsets everything revolving around change management. And then I go on to talk about the state of data within the large corporate world today, in terms of where organizations see themselves in terms of their progress in being data-driven and having developed a data culture and what they see as the challenges. I talk a little bit about data ethics. That's probably the most controversial chapter in the book because most of the book I'm saying how great it is to be in the data profession. And now everybody should be data-driven. And then I use this chapter talk to talk about the risks and the dangers of data when used improperly or used with bad intent. And then I closed the book talking about data in the context of AI. And that's a look at the future and how organizations can enable their AI capabilities by having larger and larger volumes of data. And I close with one long case study in terms of providing a perspective that becoming data-driven is really a journey and it plays out over time.
Lauren Vaccarello: Right. There's so many things I want to dig in there. And after 35 years of experience and putting together this book, and I know you've written a ton for HBR So getting started. Where do you think an organization who is on this journey to becoming more data-driven, where should they start?
Randy Bean: Yeah, let me let me actually read from the very beginning of the book, because that really sets the tone and sets the key theme. So the book starts as follows. The world isn't 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 in 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. So the point in all of that is there's a renewed urgency. There's been so much in the past year, past couple of years you know, alternative facts if you will. So data's becoming more and more important in terms of being able to get a complete picture for companies making decisions about what they're doing in terms of retaining customers or entering new markets. As well as it's come into play in terms of public health issues and understanding what is the data, what does this mean for us? What does this tell us? So, data has really gone beyond a niche area of a small group of people buried in the basement of a large corporation. There's something that is not only in the C suite, but really touches upon all aspects of society. And that's really what I've tried to do in the book. I've tried to write the book with three audiences in mind. One is C-suite executives members of the board. Who really care about the business value. They don't care so much about the, what they don't care so much about the, how relative to how things get done. They care about the, what is the business benefit to them? What is the bottom line impact? Second audience for is data practitioners. And that's why I provided roughly 25 case studies in the book. So practitioners can go deep, look at specific examples of what other organizations are doing and learn from those exams. And then lastly, I wrote for general readers who are trying to understand what is all the fuss about and why should they care about data and why data is important to all of us.
Lauren Vaccarello: Oh, that's great. And you had talked, you had mentioned using data to do things like figure out what markets to go into, product development. How do you see the role of data in decision-making and really figuring out where a business should go?
Yeah, Yeah,
Randy Bean: well let me talk a little bit, speak a little bit about my career evolution. So I started off in banking and financial services. I had studied English and history when I was an undergraduate. So hence why I've done a lot of writing on this topic. But I was trained as a COBOL and assembler programmer and what I was basically what that entailed was bringing data in. So that's inputs and then doing things with it and then sending it off to various varying locations. And I was much more interested in the data and how it was used. And I was in the programming. And at the time I said to my colleagues, I said, so what do we do with all this data that we're compiling on our customers? And they said, well, you know, the regulators make us keep it for seven years and then we're free to destroy it. And I was like, what? You know, this is a wealth of information that you can gain insight into your customers, their behaviors, and you can learn how to better serve those customers. Subsequently I went into what was the, at that time nascent field of database marketing. Now it would be known as CRM and I worked with one of the early leaders in database marketing. And within a couple years led their North American financial services database marketing practice, and then later their business to business and emerging markets database marketing practice. And what that was all about was understanding the totality of all of your interactions with your customers. So fundamentally you could get, keep and grow those customer relationships. And those interactions with the customers were really unique to your organizations. You could augment that with third party behavioral third party external data, but you, each organization has this unique repository of data relative to their interactions with their customers that is unique and very powerful. So that's the oriented orientation I came from. In other words, understanding your, the interactions with your best customers and how to leverage those to basically to retain and better serve those customers. And then to identify prospective new customers that have the same characteristics as your best customers So you've got a chapter in your book called Think Different. And it's talking about clients like CVS and Amex and Wells Fargo. Can you tell me a little bit about how you've helped clients, those types of clients think different?
Yes, absolutely. And you know, time and time again, one of the things that I hear and see is that thinking about data and organization requires a different mindset. And so the second chapter of the book is entitled, Think different: Become data, becoming data-driven. And you know, it starts with w with this quote, from the apple computer Think Different advertising campaign of 1997, which I'll read briefly because it sets the tone. 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 are crazy enough to think they can change the world are the ones who do. Well, you know, it doesn't mean that you have to be crazy or radical to become data-driven, but it means that you have to look at your current processes, your current skill sets, the people you have, how you're set up organizationally, and really take a very different perspective. As mentioned at the outset, data is an asset that flows across an organization. So that means that it's fragmented, it's siloed. There's different constituencies that own it. Some people may see a larger benefit in sharing their data. Others may have concerns about sharing their data. So there's a whole, a litany of issues that organizations have to confront in terms of evolving into an organization that leverages data as an enterprise asset
Lauren Vaccarello: Then as organizations leverage data as an enterprise asset, what are some of the things that you've seen organizations do with some of that data? And if I'm just getting started in trying to do this, where should I start looking?
Randy Bean: Yeah. You know what? We encourage organizations to do is to focus on business value and business use cases. And we say start small. Don't try to boil the ocean all at one time. So identify, you know, try to really identify what is the most important business question that you need to answer today that you can't answer and that the data can help you with. Then identify what data is needed to answer that question rather than going out and getting all of the data, just get the specific set of data that you've, that you need to answer that question. Once you've answered that question, once you've shown business value, and once you can measure that value, that establishes an initial foothold of credibility with the business sponsors or the business leaders. And so then you can move to the second question and identify what's the second most critical question? What data is needed exclusively to answer that question? And repeat the process. And that starts to increase the credibility. It starts to build momentum. Because too often, what I see is I go into organizations, meet with the data teams. They talk about all of the capabilities that they've built. They're very proud of those capabilities. I then go and meet with the technology leaders. They similarly describe the capabilities that they've created. They're very proud of those capabilities, they're very robust. But then I'll go and meet with the line of business CEO. And they'll say we don't have confidence in our data. We're not getting the data that we need to make the decisions that we needed. We're not receiving the data in a timely fashion. So obviously there's been a gap between the capabilities that have been built and the business questions that are trying to be solved. And sometimes, you know, I speak to the technologists and they'll say, well, we're building an architecture, a platform for the long term. Which is fine, but often business questions have to be answered in the day and in the moment. And results are measured and reported to, you know, the street on a quarterly basis. So to get to that long-term view it's very important to have a long-term platform and be thinking from a long-term vantage point. But at the same time, you need to demonstrate short-term results and you need to build up that immediate credibility. Otherwise, sometimes you just don't get to see the light of day for the long-term.
Lauren Vaccarello: I couldn't agree with you more on that. And how do you think both technologists and the business can get really the business can get confidence in the data that they're getting?
Randy Bean: You know, one of the things that I tried to do and everything that I write is to speak in language that everyone can understand and that business leaders can understand. Sometimes I tell folks that when I write with a CEO in mind and therefore I'm writing at a third grade level. And what I mean by that is I'm trying to make things so simple, so basic that anyone can understand them. And that's often what happens. I go into organizations and speak with senior business leaders and they say, you know, MDM, you know, data lake, data swamp, you know, I hear all of this talk and I just don't know what it means. I can't differentiate one term versus another. I need to understand what is the business impact in terms of, am I able to do something faster or am I able to do something more cost effectively? Am I gaining greater insight that I, I had in the past? So it's really boiling things down to the fundamentals and speaking business terms. And I find that when data leaders and when technology leaders are able to do that, that really breaks down the barriers. And the most effective organizations really see, you know, it's not a barrier between data leaders and technology leaders and business leaders, but they really blend into one and they're all business focused or they're bringing various expertise and skillsets to bear and they are ultimately speaking a common language.
Lauren Vaccarello: That's awesome. And it's the, I like what you had said about making data so that everyone can understand it. And it sounds in a lot of ways, you're kind of demystifying a lot of the buzzwords around becoming data-driven and how you can make data useful, which I can imagine is really helpful for people on the business side who don't want to come across like they don't know anything because, you know, everyone wants to be data-driven nowadays. But the wanting to be data-driven and knowing where and how to make it useful are very different things.
Randy Bean: Yeah. You know, I had a business partner who I co-founded the firm with and we were together for 12 and a half years before we actually did another software startup. And I remember going in and meeting with the president of a top 20 or so insurance company. The president of the consumer insurance division and my colleague, the MIT PhD explained in great brilliance, in great depth, you know, the cardinality and ordinality of data and all these very complex themes. And it was a brilliant discussion. You know, I think I followed about half of it, but I knew it was, I knew it was brilliant. And I didn't say a word the entire meeting until at the end of the president of the consumer insurance group turned to me, put his hand on my arm and said what exactly did that mean? And I said, it meant you could do it faster more cost-effectively, and as a result of that, you could better serve your customers. And he said, thank you. This was amazing. This was brilliant, but he really needed it kind of boiled down. He kinda thought he was getting the general idea and it certainly sounded amazing, but he needed to know what the bottom line impact for him was in his business.
Lauren Vaccarello: Oh, absolutely. And in some ways it's that bridging between the technologists and the business person that you had mentioned earlier of, if you truly want to make great business decisions, the technologist needs to understand really what the business user needs and the business user needs to understand and trust the technologist
Randy Bean: Yeah. You know, and having started as a programmer back in the day, so to speak. I often heard my colleagues and I often felt the same. That you know, our business users couldn't articulate what they wanted. And sometimes it was like, well, we're going to have to build it based upon certain assumptions because the they can't say what it is that they need. But at the same time, you have to bear with them, because they are the ones that are ultimately closest to the customer, need to serve the customers and drive the revenue. So it's often difficult and challenging for business leaders to articulate things. So really you just have to go the extra mile and put yourself in their shoes and see if you can see the world from their vantage point. And say, well, you know, is this what you're trying to do? And they'll say yeah that's what we're trying to do. The other thing is you need to have very agile adaptive capabilities. And that goes to the title of the book about Fail Fast, Learn Faster. You know, there used to be the old adage back in the Copa program, grant programming days that if you wanted to change, regardless of what the change was, it was going to be 15 months and, you know, $3 million. We don't have that luxury anymore. The world's too fast moving. Organizations need to be able to move on, on a dime and so need to be able to shift quickly fail fast, learn from your mistakes. You know, it goes to the old data science approach of test and learn. You know, you're not wed to the algorithm that you're using. You're constantly tinkering. And so it's shortening these cycle times. And operating in a highly agile environment that helps organizations adapt and be successful.
Lauren Vaccarello: That's great. And it's it reminds me a little bit of some of the content you've written for HBR. And you've written about how companies struggle to develop a data-driven culture. What does being data-driven mean to you? And then how do you think companies can become a data actually become and develop that data-driven culture across both technology and the business, and really drive results?
Randy Bean: Yeah, that's a great question because data-driven doesn't mean being a robot where you just look at the data and thereby make decisions blindly. You have to bring to bear, you know, human judgment and experience. But you should look at the data. You owe it yourself. You owe it to yourself, you owe it to your customers, you owe it to your colleagues, and you owe it to the industry, to gather the best possible data, the most accurate, the most timely the highest quality data you have. Look at it, review it, analyze it, synthesis, synthesize it, digest it, and then make some decisions. So for me, that's fundamentally what being data-driven is about. The other thing that's a risk with data is that You know, data can be presented to represent almost any particular point of view or any particular case to do, make this decision, or make that decision. So data in and of itself, you know, you sometimes have to look at you know, where was this data produced? You know, what assumptions lay behind this? You know, who produced this? Because data can be very selectively cherry picked to make about any argument. And that's a little bit, one of the themes that I talk about in the data ethics section of the book is that, you know, data can potentially be misused as well as it can be used effectively, but at least beginning from a starting point where you have not only as much good data as is available, but you have good people with good judgment and a variety of view points. So that you can come up with a critical perspective and not a strongly biased perspective in terms of the decisions you make from the data you have.
Lauren Vaccarello: So as you think about your role in data, what do you think the biggest changes will be over the next five or 10 years?
Randy Bean: Well, I think that we're just at the beginning of the journey. Actually, after a generation data sources only continued to proliferate. I was speaking with the 23-year-old CEO of a new AI company that I believe has something like an $8 billion valuation last week. And he was telling me 80% of data is unstructured. And that isn't something that wasn't something that I had really thought about particularly working with financial services and life sciences clients. And I played that back to a number of you know, very serious thoughtful people that I know in this space. And they say, yeah, it's interesting because it's really the tip of the iceberg in terms of organizations and the data that they're working with, and the data that they're dealing with. And there's whole new sets of signaling data and GPS and pictures and text and images that organizations haven't even begun to grapple with in a meaningful fashion. So I see the data profession only growing and growing exponentially over the next 10, 20, 30 years. Chief data officers are just in their ascendancy. They're going to become more and more critical in the coming decades. I just received notification that the university that I went to is introducing a new data science major next year. So, we're really at the beginnings in many respects of the data revolution. And we also have a responsibility to to you know, basically shepherd that in a responsible fashion and not abuse it or not become victims to miss misuses of data.
Lauren Vaccarello: I think you're completely right. And I think this is an interesting segue. You have written about data ethics. And I would love to talk a little bit about about data ethics what you see as some of the potential problems that becoming so data-driven creates and just sort of dig in there a little bit.
Randy Bean: Yeah I'm gonna read from a couple of quotes I have in the book and I call this the good, the bad and the ugly. And this is all from the chapter on data ethics. So one quote is, "We had all of the data. We just didn't connect the dots." Well, that's from the 9/11 commission. You know, that they had all this data captured from all these various sources and Intel information, but they couldn't put the pieces together in time to, to revert what happened. "We will make our decisions guided by expertise, data and science." Actually, that's a quote from now former governor Andrew Cuomo a year ago, when he was saying, you know, how they were going to use expertise, data, and science to guide the decisions they were making about treating COVID. As it turns out, even though that was a noble intention, I believe that there were a lot of data such as in the nursing homes, in the states of New York that they didn't use in that fashion. So it's one thing to say, even the right thing. It's another thing to, to fully act on it. Here's another quote, "Big data will create winners and loses losers, and it's likely to benefit the institutions who wield its tools over the individuals being mined, analyzed, and sorted." So the point there is that yeah, big data will create winners and losers and data can be abused. I love this quote. This is from an article that had appeared in the New York Times. And I quote from this is from a few years ago, it says, "Big data is the big, bad of our moment. Companies and governments amass enormous troves of information about our online and offline activities so they can understand them better than we do. Creepy firms like Cambridge Analytica make mine big data from websites, such as Facebook. Facebook itself seems increasingly creepy, grounded in lying to the public about what happens to the data it collects." So, you know, there's really a a tip of the iceberg in terms of some of the data and ethical issues. You know, a few years ago I read Weapons of Math Destruction by Cathy O'Neil. And she talks a lot about algorithmic bias and I quote from her in this book and have also done work within quoted from a camera Cam Kerry who was with the Brookings Institution. His brother, John Kerry was the Democratic party nominee for President in 2004. And he's doing a lot of work in terms of developing ethical data standards. You know, in Europe they have GDPR and in California they have the I always get this wrong, the CPC, a CPA so, selectively different geographic entities in different states and countries are establishing data standards. But for the most part it's been you know, there was no common standards.
Lauren Vaccarello: So as business leaders or as technologists, what do you think we can do as we build out our own data programs to use data ethically and try to eliminate some bias?
Randy Bean: Yeah. Well, you know, there's organizations like MasterCard that are doing a fabulous job with this. And I talk about MasterCard at length in the book with their Chief Data Officer Joanne Stonier. And they've established the MasterCard Center for Inclusive Growth. And they tried to look at opportunities to help gather and use data to solve a variety of social and governmental issues. They also have established very strict ethical standards in terms of, because they capture data across a wide array of customers and customer institutions to make sure that data is only used for purposes that basically everybody has agreed to and signed off on. So that's one organization that's paying. That's establishing very formal policies, practice practices and structures around ethical data management. But for most organizations, this is somewhat of a new area though I host quarterly, C executive round table. And a few years ago I hosted one on the topic of data ethics. And I said to my colleagues, this will probably be the least attended of the sessions and it was actually the most attended, most attended and the most passionate in terms of the participation.
Lauren Vaccarello: I can definitely see that. I we hosted an event at Talend called Talk Data with Me and we had series a bunch of different women in various roles, from an analyst, we had a NASA astronaut at NASA astronaut, come on, a woman who was a coder's another woman who taught a spy school and really talked about sort of when we had a panel on data and bias. And how do you overcome bias in data? And one of the things that I thought was fascinating during that conversation and it more went towards even some of the AI and some of the data that we use inherently, as we build it, comes with a degree of bias, eh, versus on the ethics side. And one of the things we had discussed was that I had no idea when people are going through training to be astronauts, everyone is scored and ranked and you have to be trained to be an astronaut. And here's all the programs you have to sit through. So obviously here's the tasks we all have to do. And we do these tasks. We take these physical tests and that's how you understand, are you ready to be an astronaut, are you not ready to be an astronaut? It's all very data-driven and it's all supposed to be very unbiased. And this woman who was the astronaut, who had just gotten back from the International Space Station, brought up a point. And she said, well, actually the space suits are meant, are designed for people who are six feet tall to six foot four and 180 pounds. So you're trying to get this whole, you know, generation of women training to be astronauts. And it turns out all the space suits are for someone, and I'm five feet one, that if I put on this space suit, I wouldn't be able to move in it. And it's well, ever, you know, we're going by the data to understand who the top performers are. And are you ready to do this? Without taking that step back to go well, are the conditions even set up to be equitable? And I think that's something everyone on the panel really sat and took away. And it's something that I've really taken away and taken to heart of, yes, the data may tell you this, but what is the underlying bias? What is the thing? And is the data set that we're pulling from equitable and relevant? And does it have all the context?
Randy Bean: Yes. Context is so critical and it's it's interesting to your point because there is obvious examples of algorithmic bias and not to go too far down this track, but it gets into many issues such as Yeah, you know, I guess you could even argue about questions of political correctness in terms of the biases that are brought to bear, because for example, the Cathy O'Neil book, Weapons of Math Destruction, Weapons of Math Destruction you know, I had interviewed her and I had written about her and I was discussing her book with one of colleagues who was a very senior executive in the industry and has been a leader in data for many years. And he said that he resented her book. That it went to the extreme and that it used extreme examples to suggest and illustrate that certain behaviors were the norm and and their organization, they had go so gone so far to try to be ethically compliant to protect the privacy of their customers, that they felt that the book was a distortion and a disservice. And I was surprised at the vehemence of the reaction, but basically when I came to think about it in terms of this executive and his team of hundreds of people had gone to great pains and fought the internal battles, and argued internally, these are the reasons why we need to do the protections and then to feel that they were blindsided by a book that took what he thought were easy shots. It's interesting. So, you know, it's a complex area. There's not just one easy answer. As you know, w with most data questions, there's not any easy answers, you know, it's a long-term journey. It's a process. There's multiple points of view. But that's what makes it interesting.
Lauren Vaccarello: Oh, a hundred percent. And I think that is one of the things that gives everyone in the data the data profession, a lot of job security. Is this is not easy, it's going to be ever-changing. And one of the points that has really stuck with me over the course of the interview is the people who will do really well will have a big impact, are going to be the people who yes, understand the technology. But also understand the business. What are the key problems you're trying to solve? And how do you figure out what the specific data you need is? And how do you translate that to the rest of the audience? It is great to be the smartest person in the room. But you have to be able to communicate your results to everybody else in the room to get that buy-in.
Randy Bean: I couldn't agree more. Communication is essential and it's you know, there's a lot of smartest people in the rooms, but particularly in the data and technology fields. But it's the ability to really communicate and to convey the core messages, the business value, the business benefits. Why each individual has to buy in and play their part. And, you know, to that point I hear many organizations paying lip service to change. We need to change the way we do things, but usually people are all for that when it means somebody else changing. When it means them changing the way they do things, they're less quickly to embrace that.
Lauren Vaccarello: It is a lot harder when it's you, that has to do the work. This has been a really great, informative interview and podcast. I want to switch topics. And go to some quick decision questions. So much of what we do, especially on the data side, no, not as quick, involves a little bit more analysis. So it's always good to end on what are some quick decisions, not things to don't overthink anything. You ready?
Randy Bean: I'll do my best.
Lauren Vaccarello: Okay. What is one talent or skill that is not on your resume?
Randy Bean: Wow talent or a skill. Wow, boy. That's a tough question. I think about the only things about the only things that I do well is trying to communicate complex things to a wide audience. You know, I'm not the expert. People often think I'm the expert. They say, oh, well, you know, you're the big data the expert in big data. You're the expert in AI. They'll start talking to me about various platforms and architectures and all this type of stuff. And I'll say, you know, I don't know. And I actually don't really need to know. So yeah, I mean, I'm not the expert. I'm not the person who's the technology or algorithm expert. And frankly, Tom Davenport writes in the forward to the book, you know, if you came looking for a deep technical discussion or deep algorithmic discussion, you know, you've come to the wrong spot. And then he goes on, but this doesn't mean that this isn't an important book for you, because it is really about, well, the business value of these outcomes and how you can take the architecture and the algorithms and learn how to translate that into business values. So, you know, my weakness is that you know, I'm not the deep guy on the technology. There was a time actually, when I was, but that was a long time ago.
Lauren Vaccarello: I think making the complex thing, complex things simple is a great skillset that we could probably all use to do a little bit better. So is there a book, podcast, or TV show that you've been bingeing lately?
Randy Bean: you know, I happened to be the other thing that I do when I'm not doing this business stuff is I'm co-chair of International Writers in Residence program that we bring in writers who have won Pulitzer Prizes and National Book Awards and the Booker Prize. And one of our writers actually won the 2020 received the 2020 Nobel Prize in Literature. So, I spent a lot of my time reading literature and history and even poetry. When people say, oh, have you read the latest business book? I say no, not so much. And when people asked me if I was going to ever write a book, I said, well, you know, maybe if it's a, the sequel to Moby Dick or The Great Gatsby. So I broke down and I did write this book.
Lauren Vaccarello: Awesome. What's what's your favorite book?
Randy Bean: Oh, I saw, I'll always say Moby Dick, you know, you can read it many times in many different ways and it's actually a hilarious book. I actually enjoy it more for the parts that are absolutely hilarious than the serious part. You know, the serious parts, are like over my head or two deep for me, but it's hilarious in terms of the little anecdotes throughout.
Lauren Vaccarello: You know what I think? We could all use a little levity. What advice do you have for data professionals stepping into leadership roles for the first time?
Randy Bean: Yeah. Well, not number one is the, I guess listen, observe have a long-term focus you know, don't suffer from hubris or over overconfidence. You know, many people have come before. Many people have tried. Many people have failed. You know, I don't know how many organizations I go into when the executives say, oh, another data project and their eyes roll. So, you really have to be empathetic. You have to put yourself in the shoes of your business sponsors and business constituents, and think about how you can be helping them, how you can gain their trust and confidence. How you can make data understandable to them. Often have helped organizations go through data lineage exercises, where they understand how data is produced, where data is consumed, who touches it, where new data is created, because that makes it real as opposed to abstract. For many organizations when you use the term data, it can seem awfully abstract, but yet almost virtually everybody within an organization is touched with data in many ways. And it's making it understandable to them. You know, since I wrote the book, you know, I've gone out in various scenarios where I'm with various people and you know, there'll be something like, oh, here's the latest data from the vaccine or here's the latest data on COVID. Or here's some latest data in sports, whatever the case may be, but I'm always pointing out that, oh, big data. Because everything that you see and hear around you, be it political polls or whatever the case may be it's all about the data. So the there's so much value to be gained from people becoming data driven and in the data profession. There's just Loads of opportunity and data continues to proliferate and the demand for managing data and being able to get to the insights and the learnings and the key business benefits and takeaways is just critical. And last question. What's the top piece of advice you'd give yourself 10 years ago.
Take a long-term perspective. You know what? Life is short You know, learn from experience you know, you can't change everything overnight. You know, w what I've tried to do in the book is provide some perspective and some history, because sometimes I see people caught up in the moment and they think the newest capability or the newest type of information or the newest algorithm is You know, it's the only thing that's happening. And I think it's helpful to have some perspective, both in terms of where we've been and where we have the opportunity to go.
Lauren Vaccarello: I think that is great advice. And thank you so much for joining us on the show, for sharing a ton of insights and wisdom. And for everyone out there that is looking to get started in data, become smarter in data, and just learn a little bit bet a little bit more, definitely check out Randy's new book, Fail, Fast, Learn Faster Lessons in data-driven leadership in the age of disruption, big data and AI. It's a ton of case studies. It is really useful and helpful. And thank you again so much for being on the show.
Randy Bean: It was my pleasure being here. Thank you.