Podcast appearances and mentions of bill schmarzo

Are your data science projects failing to deliver real business value?What if the problem isn't the technology or the organization, but your approach as a data scientist?With only 11% of data science models making it to deployment and close to 85% of big data projects failing, something clearly isn't working.In this episode, three globally recognised analytics leaders, Bill Schmarzo, Mark Stouse and John Thompson, join Dr Genevieve Hayes to deliver a tough love wake-up call on why data scientists struggle to create business impact, and more importantly, how to fix it.This episode reveals:Why focusing purely on technical metrics like accuracy and precision is sabotaging your success — and what metrics actually matter to business leaders. [04:18]The critical mindset shift needed to transform from a back-room technical specialist into a valued business partner. [30:33]How to present data science insights in ways that drive action — and why your fancy graphs might be hurting rather than helping. [25:08]Why “data driven” isn't enough, and how to adopt a “data informed” approach that delivers real business outcomes. [54:08]Guest BioBill Schmarzo, also known as “The Dean of Big Data,” is the AI and Data Customer Innovation Strategist for Dell Technologies' AI SPEAR team, and is the author of six books on blending data science, design thinking, and data economics from a value creation and delivery perspective. He is an avid blogger and is ranked as the #4 influencer worldwide in data science and big data by Onalytica and is also an adjunct professor at Iowa State University, where he teaches the “AI-Driven Innovation” class.Mark Stouse is the CEO of ProofAnalytics.ai, a causal AI company that helps companies understand and optimize their operational investments in light of their targeted objectives, time lag, and external factors. Known for his ability to bridge multiple business disciplines, he has successfully operationalized data science at scale across large enterprises, driven by his belief that data science's primary purpose is enabling better business decisions.John Thompson is EY's Global Head of AI and is the author of four books on AI, data and analytics teams. He was named one of dataIQ's 100 most influential people in data in 2023 and is also an Adjunct Professor at the University of Michigan, where he teaches a course based on his book “Building Analytics Teams”.LinksConnect with Bill on LinkedInConnect with Mark on LinkedInConnect with John on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Genevieve Hayes Consulting Episode 53: A Wake-Up Call from 3 Tech Leaders on Why You're Failing as a Data Scientist Are your data science projects failing to deliver real business value?What if the problem isn’t the technology or the organization, but your approach as a data scientist?With only 11% of data science models making it to deployment and close to 85% of big data projects failing, something clearly isn’t working.In this episode, three globally recognised analytics leaders, Bill Schmarzo, Mark Stouse and John Thompson, join Dr Genevieve Hayes to deliver a tough love wake-up call on why data scientists struggle to create business impact, and more importantly, how to fix it.This episode reveals:Why focusing purely on technical metrics like accuracy and precision is sabotaging your success — and what metrics actually matter to business leaders. [04:18]The critical mindset shift needed to transform from a back-room technical specialist into a valued business partner. [30:33]How to present data science insights in ways that drive action — and why your fancy graphs might be hurting rather than helping. [25:08]Why “data driven” isn’t enough, and how to adopt a “data informed” approach that delivers real business outcomes. [54:08] Guest Bio Bill Schmarzo, also known as “The Dean of Big Data,” is the AI and Data Customer Innovation Strategist for Dell Technologies' AI SPEAR team, and is the author of six books on blending data science, design thinking, and data economics from a value creation and delivery perspective. He is an avid blogger and is ranked as the #4 influencer worldwide in data science and big data by Onalytica and is also an adjunct professor at Iowa State University, where he teaches the “AI-Driven Innovation” class.Mark Stouse is the CEO of ProofAnalytics.ai, a causal AI company that helps companies understand and optimize their operational investments in light of their targeted objectives, time lag, and external factors. Known for his ability to bridge multiple business disciplines, he has successfully operationalized data science at scale across large enterprises, driven by his belief that data science’s primary purpose is enabling better business decisions.John Thompson is EY's Global Head of AI and is the author of four books on AI, data and analytics teams. He was named one of dataIQ's 100 most influential people in data in 2023 and is also an Adjunct Professor at the University of Michigan, where he teaches a course based on his book “Building Analytics Teams”. Links Connect with Bill on LinkedInConnect with Mark on LinkedInConnect with John on LinkedIn Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE Read Full Transcript [00:00:00] Dr Genevieve Hayes: Hello, and welcome to Value Driven Data Science, the podcast that helps data scientists transform their technical expertise into tangible business value, career autonomy, and financial reward. I’m Dr. Genevieve Hayes, and today I’m joined by three globally recognized innovators and leaders in AI, analytics, and data science.[00:00:24] Bill Schmarzo, Mark Stouse, and John Thompson. Bill? Also known as the Dean of Big Data, is the AI and Data Customer Innovation Strategist for Dell Technologies AI Spear Team, and is the author of six books on blending data science, design thinking, and data economics from a value creation and delivery perspective.[00:00:49] He is an avid blogger and is ranked as the number four influencer worldwide in data science and big data Analytica. And he’s also an adjunct professor at Iowa State University, where he teaches AI driven innovation. Mark is the CEO of proofanalytics. ai, a causal AI company that helps organizations understand and optimize their operational investments in light of their targeted objectives, time lag and external factors.[00:01:23] Known for his ability to bridge multiple business disciplines, he has successfully operationalized data science at scale across large enterprises. Driven by his belief that data science’s primary purpose is enabling better business decisions. And John is EY’s global head of AI and is the author of four books on AI data and analytics teams.[00:01:49] He was named one of DataIQ’s 100 most influential people in data in 2023. and is also an adjunct professor at the University of Michigan, where he teaches a course based on his book, Building Analytics Teams. Today’s episode will be a tough love wake up call for data scientists on why you are failing to deliver real business value and more importantly, what you can do about it.[00:02:17] So get ready to boost your impact. Earn what you’re worth and rewrite your career algorithm. Bill, Mark, John, welcome to the show.[00:02:25] Mark Stouse: Thank[00:02:26] Bill Schmarzo: Thanks for having us.[00:02:27] John Thompson: to be here.[00:02:28] Dr Genevieve Hayes: Only 11 percent of data scientists say their models always deploy. Only 10 percent of companies obtain significant financial benefits from AI technologies and close to 85 percent of big data projects fail. These statistics, taken from research conducted by Rexa Analytics, the Boston Consulting Group and Gartner respectively, paint a grim view of what it’s like working as a data scientist.[00:02:57] The reality is, you’re probably going to fail. And when that reality occurs, it’s not uncommon for data scientists to blame either the executive for not understanding the brilliance of their work, or the corporate culture for not being ready for data science. And maybe this is true for some organizations.[00:03:20] Particularly those relatively new to the AI adoption path. But it’s now been almost 25 years since William Cleveland first coined the term data science. And as the explosive uptake of generative AI tools, such as chat GPT demonstrate with the right use case. People are very willing to take on AI technologies.[00:03:42] So perhaps it’s finally time to look in the mirror and face the truth. Perhaps the problem is you, the data scientist. But if this is the case, then don’t despair. In many organizations, the leadership just don’t have the time to provide data scientists with the feedback necessary to improve. But today, I’m sitting here with three of the world’s best to provide that advice just for you.[00:04:09] So, let’s cut to the chase what are the biggest mistakes you see data scientists making when it comes to demonstrating their value?[00:04:18] Mark Stouse: I think that you have to start with the fact that they’re not demonstrating their value, right? I mean, if you’re a CEO, a CFO, head of sales really doesn’t matter if you’re trying to make better business decisions over and over and over again. As Bill talks about a lot, the whole idea here is economic,[00:04:39] and it is. About engaging, triggering the laws of compounding you’ve got to be able to do stuff that makes that happen. Data management, for example, even though we all agree that it’s really necessary, particularly if you’re launching, you know, big data solutions. You can’t do this sequentially and be successful.[00:05:04] You’re going to have to find some areas probably using, you know, old fashioned math around causal analytics, multivariable linear regression, things like that, to at least get the ball rolling. In terms of delivering better value, the kind of value that business leaders actually see as valuable[00:05:29] I mean, one of the things that I feel like I say a lot is, you have to have an understanding of your mission, the mission of data science. As somebody who, as a business leader champions it. Is to help people make those better and better and better decisions. And if you’re not doing that, you’re not creating value.[00:05:52] Full stop.[00:05:53] Bill Schmarzo: Totally agree with Mark. I think you’re going to find that all three of us are in violent agreement on a lot of this stuff. What I find interesting is it isn’t just a data scientist fault. Genevieve, you made a comment that leadership lacks the time to provide guidance to data scientists. So if leadership Is it treating data and analytics as an economics conversation if they think it’s a technology conversation is something that should be handled by the CIO, you’ve already lost, you’ve already failed, you already know you failed,[00:06:24] Mark mentioned the fact that this requires the blending of both sides of the aisle. It requires a data scientist to have the right mindset to ask questions like what it is that we’re trying to achieve. How do we create value? What are our desired outcomes? What are the KPIs metrics around which are going to make your success?[00:06:39] Who are our key stakeholders? There’s a series of questions that the data scientist must be empowered to ask and the business Leadership needs to provide the time and people and resources to understand what we’re trying to accomplish. It means we can go back old school with Stephen Covey, begin with an end in mind.[00:07:01] What is it we’re trying to do? Are we trying to improve customer retention? We try to do, you know, reduce unplanned operational downtime or improve patient outcomes. What is it we’re trying to accomplish? The conversation must, must start there. And it has to start with business leadership, setting the direction, setting the charter, putting the posts out where we want to go, and then the data science team collaborating with the stakeholders to unleash that organizational tribal knowledge to actually solve[00:07:32] Dr Genevieve Hayes: think a lot of the problem comes with the fact that many business leaders see data science as being like an IT project. So, if you’ve got your Windows upgrade, the leadership It gives the financing to IT, IT goes along and does it. And then one morning you’re told, when you come into work, your computer will magically upgrade to the latest version of Windows.[00:07:55] So no one really gets bothered by it. And I think many business leaders treat data science as just another IT project like that. They think they can just Give the funding, the data scientists will go away and then they’ll come in one morning and the data science will magically be on their computer.[00:08:15] Bill Schmarzo: Yeah, magic happens, right? No, no, magic doesn’t happen, it doesn’t happen. There has to be that leadership commitment to be at the forefront, not just on the boat, but at the front of the boat saying this is the direction we’re going to go.[00:08:29] John Thompson: That’s the whole reason this book was written. The whole point is that, analytics projects are not tech projects. Analytics projects are cultural transformation projects, is what they are. And if you’re expecting the CEO, CFO, CIO, COO, whoever it is, to go out there and set the vision.[00:08:50] That’s never going to happen because they don’t understand technology, and they don’t understand data. They’d rather be working on building the next factory or buying another company or something like that. What really has to happen is the analytics team has to provide leadership to the leadership for them to understand what they’re going to do.[00:09:12] So when I have a project that we’re trying to do, my team is trying to do, and if we’re working for, let’s say, marketing, I go to the CMO and I say, look, you have to dedicate and commit. that your subject matter experts are going to be in all the meetings. Not just the kickoff meetings, not just the quarterly business review, the weekly meetings.[00:09:36] Because when we go off as an analytics professionals and do things on our own, we have no idea what the business runs like. , we did analytics at one company that I work for. We brought it back and we showed it to the they said, the numbers are wildly wrong. And we said, well, why? And they said, well, you probably don’t understand that what we do is illegal in 10 US states.[00:10:00] So you probably have the data from all those 10 states in the analysis. And we did. So, we took it all out and they look down there and go, you got it right. It’s kind of surprising. You didn’t know what you were doing and you got it right. So, it has to be a marriage of the subject matter experts in the business.[00:10:17] And the data scientists, you can’t go to the leadership and say, tell us what you want. They don’t know what they want. They’d want another horse in Henry Ford’s time, or they glue a, a Walkman onto a radio or something in Steve Jobs time. They don’t know what they want. So you have to come together.[00:10:36] And define it together and you have to work through the entire project together.[00:10:42] Mark Stouse: Yeah, I would add to that, okay, that a lot of times the SMEs also have major holes in their knowledge that the analytics are going to challenge and give them new information. And so I totally agree. I mean, this is an iterative learning exchange. That has profound cultural implications.[00:11:11] One of the things that AI is doing right now is it is introducing a level of transparency and accountability into operations, corporate operations, my operations, your operations, that honestly, none of us are really prepared for. None of us are really prepared for the level of learning that we’re going to have to do.[00:11:36] And very few of us are aware of how polymathic. Most of our challenges, our problems, our objectives really are one of the things that I love to talk about in this regard is analytics made me a much better person. That I once was because it showed me the extent of my ignorance.[00:12:01] And when I kind of came to grips with that and I started to use really the modicum of knowledge that I have as a way of curating my ignorance. And I got humble about it made a big difference[00:12:16] John Thompson: Well, that’s the same when I was working shoulder to shoulder with Bill, I just realized how stupid I was. So, then I just, really had to, come back and, say, oh, God nowhere near the summit, I have a long way to go.[00:12:31] Bill Schmarzo: Hey, hey, Genevie. Let me throw something out there at you and it builds on what John has said and really takes off on what Mark is talking about is that there is a cultural preparation. It needs to take place across organizations in order to learn to master the economies of learning,[00:12:48] the economies of learning, because you could argue in knowledge based industries that what you are learning is more important than what you know. And so if what you know has declining value, and what you’re learning has increasing value, then what Mark talked about, and John as well, both city presenting data and people saying, I didn’t know that was going on, right?[00:13:09] They had a certain impression. And if they have the wrong cultural mindset. They’re going to fight that knowledge. They’re going to fight that learning, oh, I’m going to get fired. I’m going to get punished. No, we need to create cultures that says that we are trying to master the economies and learning and you can’t learn if you’re not willing to fail.[00:13:29] And that is what is powerful about what AI can do for us. And I like to talk about how I’m a big fan of design thinking. I integrate design thinking into all my workshops and all my training because it’s designed to. Cultivate that human learning aspect. AI models are great at cultivating algorithmic learning.[00:13:50] And when you bring those two things together around a learning culture that says you’re going to try things, you’re going to fail, you’re going to learn, those are the organizations that are going to win.[00:13:59] John Thompson: Yeah, you know, to tie together what Mark and Bill are saying there is that, you need people to understand that they’re working from an outmoded view of the business. Now, it’s hard for them to hear that. It’s hard for them to realize it. And what I ask data scientists to do that work for me is when we get a project and we have an operational area, sales, marketing, logistics, finance, manufacturing, whatever it is.[00:14:26] They agreed that they’re going to go on the journey with us. We do something really simple. We do an exploratory data analysis. We look at means and modes and distributions and things like that. And we come back and we say, this is what the business looks like today. And most of the time they go, I had no idea.[00:14:44] You know, I didn’t know that our customers were all, for the most part, between 70 and 50. I had no idea that our price point was really 299. I thought it was 3, 299. So you then end up coming together. You end up with a shared understanding of the business. Now one of two things is generally going to happen.[00:15:05] The business is going to freak out and leave the project and say, I don’t want anything to do with this, or they’re going to lean into it and say, I was working from something that was, as Bill said, declining value. Okay. Now, if they’re open, like a AI model that’s being trained, if they’re open to learning, they can learn what the business looks like today, and we can help them predict what the business should look like tomorrow.[00:15:31] So we have a real issue here that the three of us have talked about it from three different perspectives. We’ve all seen it. We’ve all experienced it. It’s a real issue, we know how people can come together. The question is, will they?[00:15:46] Dr Genevieve Hayes: think part of the issue is that, particularly in the area of data science, there’s a marked lack of leadership because I think a lot of people don’t understand how to lead these projects. So you’ve got Many data scientists who are trained heavily in the whole technical aspect of data science, and one thing I’ve come across is, you know, data scientists who’ll say to me, my job is to do the technical work, tell me what to do.[00:16:23] I’ll go away and do it. Give it to you. And then you manager can go and do whatever you like with it.[00:16:29] Mark Stouse: Model fitment.[00:16:31] Dr Genevieve Hayes: Yeah. And then one thing I’ve experienced is many managers in data science are, you know, It’s often the area that they find difficult to find managers for, so we’ll often get people who have no data science experience whatsoever[00:16:46] and so I think part of the solution is teaching the data scientists that they have to start managing up because they’re the ones who understand what they’re doing the best, but no one’s telling them that because the people above them often don’t know that they should be telling the data[00:17:08] John Thompson: Well, if that’s the situation, they should just fire everybody and save the money. Because it’s never going to go anywhere. But Bill, you were going to say something. Go ahead.[00:17:16] Bill Schmarzo: Yeah, I was going to say, what’s interesting about Genevieve, what you’re saying is that I see this a lot in not just data scientists, but in a lot of people who are scared to show their ignorance in new situations. I think Mark talked about this, is it because they’re, you think about if you’re a data scientist, you probably have a math background. And in math, there’s always a right answer. In data science, there isn’t. There’s all kinds of potential answers, depending on the situation and the circumstances. I see this all the time, by the way, with our sales folks. Who are afraid we’re selling technology. We’re afraid to talk to the line of business because I don’t understand their business Well, you don’t need to understand their business, but you do need to become like socrates and start asking questions What are you trying to accomplish?[00:18:04] What are your goals? What are your desired outcomes? How do you measure success? Who are your stakeholders ? You have to be genuinely interested In their success and ask those kind of questions if you’re doing it to just kind of check a box off Then just get chad gpt to rattle it off But if you’re genuinely trying to understand what they’re trying to accomplish And then thinking about all these marvelous different tools you have because they’re only tools And how you can weave them together to help solve that now you’ve got That collaboration that john’s book talks about about bringing these teams together Yeah[00:18:39] Mark Stouse: is, famously paraphrased probably did actually say something like this, . But he’s famously paraphrased as saying that he would rather have a really smart question than the best answer in the world. And. I actually experienced that two days ago,[00:18:57] in a conversation with a prospect where I literally, I mean, totally knew nothing about their business. Zero, but I asked evidently really good questions. And so his impression of me at the end of the meeting was, golly, you know, so much about our business. And I wanted to say, yeah, cause you just educated me.[00:19:21] Right. You know, I do now. And so I think there’s actually a pattern here that’s really worth elevating. So what we are seeing right now with regard to data science teams is scary similar to what happened with it after Y2K, the business turned around and looked at him and said, seriously, we spend all that money,[00:19:45] I mean, what the heck? And so what happened? The CIO got, demoted organizationally pretty far down in the company wasn’t a true C suite member anymore. Typically the whole thing reported up into finance. The issue was not. Finance, believing that they knew it better than the it people,[00:20:09] it was, we are going to transform this profession from being a technology first profession to a business outcomes. First profession, a money first profession, an economics organization, that has more oftentimes than not been the outcome in the last 25 years. But I think that that’s exactly what’s going on right now with a lot of data science teams.[00:20:39] You know, I used to sit in technology briefing rooms, listening to CIOs and other people talk about their problems. And. This one CIO said, you know, what I did is I asked every single person in my organization around the world to go take a finance for non financial managers course at their local university.[00:21:06] They want credit for it. We’ll pay the bill. If they just want to audit it, they can do that. And they started really cross pollinating. These teams to give them more perspective about the business. I totally ripped that off because it just struck me as a CMO as being like, so many of these problems, you could just do a search and replace and get to marketing.[00:21:32] And so I started doing the same thing and I’ve made that suggestion to different CDOs, some of whom have actually done it. So it’s just kind of one of those things where you have to say, I need to know more. So this whole culture of being a specialist is changing from.[00:21:53] This, which, this is enough, this is okay , I’m making a vertical sign with my hand, to a T shaped thing, where the T is all about context. It’s all about everything. That’s not part of your. Profession[00:22:09] John Thompson: Yeah, well, I’m going to say that here’s another book that you should have your hands on. This is Aristotle. We can forget about Socrates. Aristotle’s the name. But you know. But , Bill’s always talking about Socrates. I’m an Aristotle guy myself. So, you[00:22:23] Bill Schmarzo: Okay, well I Socrates had a better jump shot. I’m sorry. He could really nail that[00:22:28] John Thompson: true. It’s true. Absolutely. Well, getting back , to the theme of the discussion, in 1 of the teams that I had at CSL bearing, which is an Australian company there in Melbourne, I took my data science team and I brought in speech coaches.[00:22:45] Presentation coaches people who understand business, people who understood how to talk about different things. And I ran them through a battery of classes. And I told them, you’re going to be in front of the CEO, you’re going to be in front of the EVP of finance, you’re going to be in front of all these different people, and you need to have the confidence to speak their language.[00:23:07] Whenever we had meetings, we talk data science talk, we talk data and integration and vectors and, algorithms and all that kind of stuff. But when we were in the finance meeting, we talked finance. That’s all we talked. And whenever we talked to anybody, we denominated all our conversations in money.[00:23:25] Whether it was drachma, yen, euros, pounds, whatever it was, we never talked about speeds and feeds and accuracy and results. We always talked about money. And if it didn’t make money, we didn’t do it. So, the other thing that we did that really made a difference was that when the data scientists and data scientists hate this, When they went into a meeting, and I was there, and even if I wasn’t there, they were giving the end users and executives recommendations.[00:23:57] They weren’t going in and showing a model and a result and walking out the door and go, well, you’re smart enough to interpret it. No, they’re not smart enough to interpret it. They actually told the marketing people. These are the 3 things you should do. And if your data scientists are not being predictive and recommending actions, they’re not doing their job.[00:24:18] Dr Genevieve Hayes: What’s the, so what test At the end of everything, you have to be able to say, so what does this mean to whoever your audience is?[00:24:25] Mark Stouse: That’s right. I mean, you have to be able to say well, if the business team can’t look at your output, your data science output, and know what to do with it, and know how to make a better decision, it’s like everything else that you did didn’t happen. I mean it, early in proof, we were working on. UX, because it became really clear that what was good for a data scientist wasn’t working. For like everybody else. And so we did a lot of research into it. Would you believe that business teams are okay with charts? Most of them, if they see a graph, they just totally freeze and it’s not because they’re stupid.[00:25:08] It’s because so many people had a bad experience in school with math. This is a psychological, this is an intellectual and they freeze. So in causal analytics, one of the challenges is that, I mean, this is pretty much functioning most of the time anyway, on time series data, so there is a graph,[00:25:31] this is kind of like a non negotiable, but we had a customer that was feeding data so fast into proof that the automatic recalc of the model was happening like lickety split. And that graph all of a sudden looked exactly like a GPS. It worked like a GPS. In fact, it really is a GPS. And so as soon as we stylized.[00:26:01] That graph to look more like a GPS track, all of a sudden everybody went, Oh,[00:26:10] Dr Genevieve Hayes: So I got rid of all the PTSD from high school maths and made it something familiar.[00:26:16] Mark Stouse: right. And so it’s very interesting. Totally,[00:26:21] Bill Schmarzo: very much mirrors what mark talked about So when I was the new vice president of advertiser analytics at yahoo we were trying to solve a problem to help our advertisers optimize their spend across the yahoo ad network and because I didn’t know anything about that industry We went out and my team went out and interviewed all these advertisers and their agencies.[00:26:41] And I was given two UEX people and zero data. Well, I did have one data scientist. But I had mostly UX people on this project. My boss there said, you’re going to want UX people. I was like, no, no, I need analytics. He said, trust me in UX people and the process we went through and I could spend an hour talking about the grand failure of the start and the reclamation of how it was saved at a bar after too many drinks at the Waldorf there in New York.[00:27:07] But what we’ve realized is that. For us to be effective for our target audience was which was media planners and buyers and campaign managers. That was our stakeholders. It wasn’t the analysts, it was our stakeholders. Like Mark said, the last thing they wanted to see was a chart. And like John said, what they wanted the application to do was to tell them what to do.[00:27:27] So we designed this user interface that on one side, think of it as a newspaper, said, this is what’s going on with your campaign. This audience is responding. These sites are this, these keywords are doing this. And the right hand side gave recommendations. We think you should move spend from this to this.[00:27:42] We think you should do this. And it had three buttons on this thing. You could accept it and it would kick into our advertising network and kick in. And we’d measure how effective that was. They could reject it. They didn’t think I was confident and we’d measure effectiveness or they could change it. And we found through our research by putting that change button in there that they had control, that adoption went through the roof.[00:28:08] When it was either yes or no, adoption was really hard, they hardly ever used it. Give them a chance to actually change it. That adoption went through the roof of the technology. So what John was saying about, you have to be able to really deliver recommendations, but you can’t have the system feel like it’s your overlord.[00:28:27] You’ve got to be like it’s your Yoda on your shoulder whispering to your saying, Hey, I think you should do this. And you’re going, eh, I like that. No, I don’t like this. I want to do that instead. And when you give them control, then the adoption process happens much smoother. But for us to deliver those kinds of results, we had to know in detail, what decisions are they trying to make?[00:28:45] How are they going to measure success? We had to really understand their business. And then the data and the analytics stuff was really easy because we knew what we had to do, but we also knew what we didn’t have to do. We didn’t have to boil the ocean. We were trying to answer basically 21 questions.[00:29:01] The media planners and buyers and the campaign managers had 21 decisions to make and we built analytics and recommendations for each Of those 21[00:29:10] John Thompson: We did the same thing, you know, it blends the two stories from Mark and Bill, we were working at CSL and we were trying to give the people tools to find the best next location for plasma donation centers. And, like you said, there were 50, 60 different salient factors they had, and when we presented to them in charts and graphs, Information overload.[00:29:34] They melted down. You can just see their brains coming out of their ears. But once we put it on a map and hit it all and put little dials that they could fiddle with, they ran with it.[00:29:49] Bill Schmarzo: brilliant[00:29:50] Mark Stouse: totally, totally agree with that. 100% you have to know what to give people and you have to know how to give them, control over some of it, nobody wants to be an automaton. And yet also they will totally lock up if you just give them the keys to the kingdom. Yeah.[00:30:09] Dr Genevieve Hayes: on what you’ve been saying in the discussion so far, what I’m hearing is that the critical difference between what data scientists think their role is and what business leaders actually need is the data scientists is. Well, the ones who aren’t performing well think their role is to just sit there in a back room and do technical work like they would have done in their university assignments.[00:30:33] What the business leaders need is someone who can work with them, ask the right questions in order to understand the needs of the business. make recommendations that answer those questions. But in answering those questions, we’re taking a data informed approach rather than a data driven approach. So you need to deliver the answers to those questions in such a way that you’re informing the business leaders and you’re delivering it in a way that Delivers the right user experience for them, rather than the user experience that the data scientists might want, which would be your high school maths graphs.[00:31:17] Is that a good summary?[00:31:20] John Thompson: Yeah, I think that’s a really good summary. You know, one of the things that Bill and I, and I believe Mark understands is we’re all working to change, you know, Bill and I are teaching at universities in the United States. I’m on the advisory board of about five. Major universities. And whenever I go in and talk to these universities and they say, Oh, well, we teach them, these algorithms and these mathematical techniques and these data science and this statistics.[00:31:48] And I’m like, you are setting these people up for failure. You need to have them have presentation skills, communication skills, collaboration. You need to take about a third of these credits out and change them out for soft skills because you said it Genevieve, the way we train people, young people in undergraduate and graduate is that they have a belief that they’re going to go sit in a room and fiddle with numbers.[00:32:13] That’s not going to be successful.[00:32:16] Mark Stouse: I would give one more point of dimensionality to this, which is a little more human, in some respects, and that is that I think that a lot of data scientists love the fact that they are seen as Merlin’s as shamans. And the problem that I personally witnessed this about two years ago is when you let business leaders persist in seeing you in those terms.[00:32:46] And when all of a sudden there was a major meltdown of some kind, in this case, it was interest rates, and they turn around and they say, as this one CEO said in this meeting Hey, I know you’ve been doing all kinds of really cool stuff back there with AI and everything else. And now I need help.[00:33:08] Okay. And the clear expectation was. I need it now, I need some brilliant insight now. And the answer that he got was, we’re not ready yet. We’re still doing the data management piece. And this CEO dropped the loudest F bomb. That I think I have ever heard from anybody in almost any situation,[00:33:36] and that guy, that data science leader was gone the very next day. Now, was that fair? No. Was it stupid? For the data science leader to say what he said. Yeah, it was really dumb.[00:33:52] Bill Schmarzo: Don’t you call that the tyranny of perfection mark? Is that your term that you always use? is that There’s this idea that I gotta get the data all right first before I can start doing analysis And I think it’s you I hear you say the tyranny of perfection is what hurts You Progress over perfection, learning over absolutes, and that’s part of the challenge is it’s never going to be perfect.[00:34:13] Your data is never going to be perfect, you got to use good enough data[00:34:17] Mark Stouse: It’s like the ultimate negative version of the waterfall.[00:34:22] John Thompson: Yeah,[00:34:23] Mark Stouse: yet we’re all supposedly living in agile paradise. And yet very few people actually operate[00:34:30] John Thompson: that’s 1 thing. I want to make sure that we get in the recording is that I’ve been on record for years and I’ve gone in front of audiences and said this over and over again. Agile and analytics don’t mix that is. There’s no way that those 2 go together. Agile is a babysitting methodology. Data scientists don’t do well with it.[00:34:50] So, you know, I’ll get hate mail for that, but I will die on that hill. But, the 1 thing that, Mark, I agree with 100 percent of what you said, but the answer itself or the clue itself is in the title. We’ve been talking about. It’s data science. It’s not magic. I get people coming and asking me to do magical things all the time.[00:35:11] And I’m like. Well, have you chipped all the people? Do you have all their brain waves? If you have that data set, I can probably analyze it. But, given that you don’t understand what’s going on inside their cranium, that’s magic. I can’t do that. We had the same situation when COVID hit, people weren’t leaving their house.[00:35:29] So they’re not donating plasma. It’s kind of obvious, so, people came to us and said, Hey, the world’s gone to hell in a handbasket in the last two weeks. The models aren’t working and I’m like, yeah, the world’s changed, give us four weeks to get a little bit of data.[00:35:43] We’ll start to give you a glimmer of what this world’s going to look like two months later. We had the models working back in single digit error terms, but when the world goes haywire, you’re not going to have any data, and then when the executives are yelling at you, you just have to say, look, this is modeling.[00:36:01] This is analytics. We have no precedent here.[00:36:05] Bill Schmarzo: to build on what John was just saying that the challenge that I’ve always seen with data science organizations is if they’re led by somebody with a software development background, getting back to the agile analytics thing, the problem with software development. is that software development defines the requirements for success.[00:36:23] Data science discovers them. It’s hard to make that a linear process. And so, if you came to me and said, Hey, Schmarz, you got a big, giant data science team. I had a great data science team at Hitachi. Holy cow, they were great. You said, hey, we need to solve this problem. When can you have it done?[00:36:38] I would say, I need to look at the problem. I need to start exploring it. I can’t give you a hard date. And that drove software development folks nuts. I need a date for when I, I don’t know, cause I’ve got to explore. I’m going to try lots of things. I’m going to fail a lot.[00:36:51] I’m going to try things that I know are going to fail because I can learn when I fail. And so, when you have an organization that has a software development mindset, , like John was talking about, they don’t understand the discovery and learning process that the data science process has to go through to discover the criteria for success.[00:37:09] Mark Stouse: right. It’s the difference between science and engineering.[00:37:13] John Thompson: Yes, exactly. And 1 of the things, 1 of the things that I’ve created, it’s, you know, everybody does it, but I have a term for it. It’s a personal project portfolio for data scientists. And every time I’ve done this and every team. Every data scientist has come to me individually and said, this is too much work.[00:37:32] It’s too hard. I can’t[00:37:34] Bill Schmarzo: Ha, ha, ha,[00:37:35] John Thompson: three months later, they go, this is the only way I want to work. And what you do is you give them enough work so when they run into roadblocks, they can stop working on that project. They can go out and take a swim or work on something else or go walk their dog or whatever.[00:37:53] It’s not the end of the world because the only project they’re working on can’t go forward. if they’ve got a bunch of projects to time slice on. And this happens all the time. You’re in, team meetings and you’re talking and all of a sudden the data scientist isn’t talking about that forecasting problem.[00:38:09] It’s like they ran into a roadblock. They hit a wall. Then a week later, they come in and they’re like, Oh, my God, when I was in the shower, I figured it out. You have to make time for cogitation, introspection, and eureka moments. That has to happen in data science.[00:38:28] Bill Schmarzo: That is great, John. I love that. That is wonderful.[00:38:30] Mark Stouse: And of course the problem is. Yeah. Is that you can’t predict any of that, that’s the part of this. There’s so much we can predict. Can’t predict that.[00:38:42] Bill Schmarzo: you know what you could do though? You could do Mark, you could prescribe that your data science team takes multiple showers every day to have more of those shower moments. See, that’s the problem. I see a correlation. If showers drive eureka moments, dang it.[00:38:54] Let’s give him more showers.[00:38:56] John Thompson: Yep. Just like firemen cause fires[00:38:59] Mark Stouse: Yeah, that’s an interesting correlation there, man.[00:39:05] Dr Genevieve Hayes: So, if businesses need something different from what the data scientists are offering, why don’t they just articulate that in the data scientist’s role description?[00:39:16] John Thompson: because they don’t know they need it.[00:39:17] Mark Stouse: Yeah. And I think also you gotta really remember who you’re dealing with here. I mean, the background of the average C suite member is not highly intellectual. That’s not an insult, that’s just they’re not deep thinkers. They don’t think a lot. They don’t[00:39:37] John Thompson: that with tech phobia.[00:39:38] Mark Stouse: tech phobia and a short termism perspective.[00:39:43] That arguably is kind of the worst of all the pieces.[00:39:48] John Thompson: storm. It’s a[00:39:49] Mark Stouse: It is, it is a[00:39:50] John Thompson: know, I, I had, I’ve had CEOs come to me and say, we’re in a real crisis here and you guys aren’t helping. I was like, well, how do you know we’re not helping? You never talked to us. And, in this situation, we had to actually analyze the entire problem and we’re a week away from making recommendations.[00:40:08] And I said that I said, we have an answer in 7 days. He goes, I need an answer today. I said, well, then you should go talk to someone else because in 7 days, I’ll have it. But now I don’t. So, I met with him a week later. I showed them all the data, all the analytics, all the recommendations. And they said to me, we don’t really think you understand the business well enough.[00:40:27] We in the C suite have looked at it and we don’t think that this will solve it. And I’m like, okay, fine, cool. No problem. So I left, and 2 weeks later, they called me in and said, well, we don’t have a better idea. So, what was that you said? And I said, well, we’ve coded it all into the operational systems.[00:40:43] All you have to do is say yes. And we’ll turn it on and it was 1 of the 1st times and only times in my life when the chart was going like this, we made all the changes and it went like that. It was a perfect fit. It worked like a charm and then, a month later, I guess it was about 6 months later, the CEO came around and said, wow, you guys really knew your stuff.[00:41:07] You really were able to help us. Turn this around and make it a benefit and we turned it around faster than any of the competitors did. And then he said, well, what would you like to do next? And I said, well, I resigned last week. So, , I’m going to go do it somewhere else.[00:41:22] And he’s like, what? You just made a huge difference in the business. And I said, yeah, you didn’t pay me anymore. You didn’t recognize me. And I’ve been here for nearly 4 years, and I’ve had to fight you tooth and nail for everything. I’m tired of it.[00:41:34] Mark Stouse: Yeah. That’s what’s called knowing your value. One of the things that I think is so ironic about this entire conversation is that if any function has the skillsets necessary to forecast and demonstrate their value as multipliers. Of business decisions, decision quality, decision outcomes it’s data science.[00:42:05] And yet they just kind of. It’s like not there. And when you say that to them, they kind of look at you kind of like, did you really just say that, and so it is, one of the things that I’ve learned from analytics is that in the average corporation, you have linear functions that are by definition, linear value creators.[00:42:32] Sales would be a great example. And then you have others that are non linear multipliers. Marketing is one, data science is another, the list is long, it’s always the non linear multipliers that get into trouble because they don’t know how to show their value. In the same way that a linear creator can show it[00:42:55] John Thompson: And I think that’s absolutely true, Mark. And what I’ve been saying, and Bill’s heard this until he’s sick of it. Is that, , data science always has to be denominated in currency. Always, if you can’t tell them in 6 months, you’re going to double the sales or in 3 months, you’re going to cut cost or in, , 5 months, you’re going to have double the customers.[00:43:17] If you’re not denominating that in currency and whatever currency they care about, you’re wasting your time.[00:43:23] Dr Genevieve Hayes: The problem is, every single data science book tells you that the metrics to evaluate models by are, precision, recall, accuracy, et[00:43:31] John Thompson: Yeah, but that’s technology. That’s not business.[00:43:34] Dr Genevieve Hayes: exactly. I’ve only ever seen one textbook where they say, those are technical metrics, but the metrics that really count are the business metrics, which are basically dollars and cents.[00:43:44] John Thompson: well, here’s the second one that says it.[00:43:46] Dr Genevieve Hayes: I will read that. For the audience it’s Business Analytics Teams by John Thompson.[00:43:51] John Thompson: building analytics[00:43:52] Dr Genevieve Hayes: Oh, sorry, Building[00:43:54] Mark Stouse: But, but I got to tell you seriously, the book that John wrote that everybody needs to read in business. Okay. Not just data scientists, but pretty much everybody. Is about causal AI. And it’s because almost all of the questions. In business are about, why did that happen? How did it happen? How long did it take for that to happen?[00:44:20] It’s causal. And so, I mean, when you really look at it that way and you start to say, well, what effects am I causing? What effects is my function causing, all of a sudden the scales kind of have a way of falling away from your eyes and you see things. Differently.[00:44:43] John Thompson: of you to say that about that book. I appreciate that.[00:44:46] Mark Stouse: That kick ass book, kick[00:44:48] John Thompson: Well, thank you. But, most people don’t understand that we’ve had analytical or foundational AI for 70 years. We’ve had generative AI for two, and we’ve had causal for a while, but only people understand it are the people on this call and Judea Pearl and maybe 10 others in the world, but we’re moving in a direction where those 3 families of AI are going to be working together in what I’m calling composite AI, which is the path to artificial, or as Bill says, average general intelligence or AGI.[00:45:24] But there are lots of eight eyes people talk about it as if it’s one thing and it’s[00:45:29] Mark Stouse: Yeah, correct. That’s right.[00:45:31] Dr Genevieve Hayes: I think part of the problem with causal AI is it’s just not taught in data science courses.[00:45:37] John Thompson: it was not taught anywhere. The only place it’s taught is UCLA.[00:45:40] Mark Stouse: But the other problem, which I think is where you’re going with it Genevieve is even 10 years ago, they weren’t even teaching multivariable linear regression as a cornerstone element of a data science program. So , they basically over rotated and again, I’m not knocking it.[00:46:01] I’m not knocking machine learning or anything like that. Okay. But they over rotated it and they turned it into some sort of Omni tool, that could do it all. And it can’t do it all.[00:46:15] Dr Genevieve Hayes: think part of the problem is the technical side of data science is the amalgamation of statistics and computer science . But many data science university courses arose out of the computer science departments. So they focused on the machine learning courses whereas many of those things like.[00:46:34] multivariable linear analysis and hypothesis testing, which leads to things like causal AI. They’re taught in the statistics courses that just don’t pop up in the data science programs.[00:46:46] Mark Stouse: Well, that’s certainly my experience. I teach at USC in the grad school and that’s the problem in a nutshell right there. In fact, we’re getting ready to have kind of a little convocation in LA about this very thing in a couple of months because it’s not sustainable.[00:47:05] Bill Schmarzo: Well, if you don’t mind, I’m going to go back a second. We talked about, measuring success as currency. I’m going to challenge that a little bit. We certainly need to think about how we create value, and value isn’t just currency. John held up a book earlier, and I’m going to hold up one now, Wealth of Nations,[00:47:23] John Thompson: Oh yeah.[00:47:25] Bill Schmarzo: Page 28, Adam Smith talks about value he talks about value creation, and it isn’t just about ROI or net present value. Value is a broad category. You got customer value, employee value, a partner stakeholder. You have society value, community value of environmental value.[00:47:43] We have ethical value. And as we look at the models that we are building, that were guided or data science teams to build, we need to broaden the definition of value. It isn’t sufficient if we can drive ROI, if it’s destroying our environment and putting people out of work. We need to think more holistically.[00:48:04] Adam Smith talks about this. Yeah, 1776. Good year, by the way, it’s ultimate old school, but it’s important when we are As a data science team working with the business that we’re broadening their discussions, I’ve had conversations with hospitals and banks recently. We run these workshops and one of the things I always do, I end up pausing about halfway through the workshop and say, what are your desired outcomes from a community perspective?[00:48:27] You sit inside a community or hospital. You have a community around you, a bank, you have a community around you. What are your desired outcomes for that community? How are you going to measure success? What are those KPIs and metrics? And they look at me like I got lobsters crawling out of my ears.[00:48:40] The thing is is that it’s critical if we’re going to Be in champion data science, especially with these tools like these new ai tools causal predictive generative autonomous, these tools allow us to deliver a much broader range of what value is And so I really rail against when somebody says, you know, and not trying to really somebody here but You know, we gotta deliver a better ROI.[00:49:05] How do you codify environmental and community impact into an ROI? Because ROI and a lot of financial metrics tend to be lagging indicators. And if you’re going to build AI models, you want to build them on leading indicators.[00:49:22] Mark Stouse: It’s a lagging efficiency metric,[00:49:24] Bill Schmarzo: Yeah, exactly. And AI doesn’t do a very good job of optimizing what’s already happened.[00:49:29] That’s not what it does.[00:49:30] John Thompson: sure.[00:49:31] Bill Schmarzo: I think part of the challenge, you’re going to hear this from John and from Mark as well, is that we broaden this conversation. We open our eyes because AI doesn’t need to just deliver on what’s happened in the past, looks at the historical data and just replicates that going forward.[00:49:45] That leads to confirmation bias of other things. We have a chance in AI through the AI utility function to define what it is we want our AI models to do. from environmental, society, community, ethical perspective. That is the huge opportunity, and Adam Smith says that so.[00:50:03] John Thompson: There you go. Adam Smith. I love it. Socrates, Aristotle, Adam[00:50:08] Bill Schmarzo: By the way, Adam Smith motivated this book that I wrote called The Economics of Data Analytics and Digital Transformation I wrote this book because I got sick and tired of walking into a business conversation and saying, Data, that’s technology. No, data, that’s economics.[00:50:25] Mark Stouse: and I’ll tell you what, you know what, Genevieve, I’m so cognizant of the fact in this conversation that the summer can’t come fast enough when I too will have a book,[00:50:39] John Thompson: yay.[00:50:41] Mark Stouse: yeah, I will say this, One of the things that if you use proof, you’ll see this, is that there’s a place where you can monetize in and out of a model, but money itself is not causal. It’s what you spend it on. That’s either causal or in some cases, not[00:51:01] That’s a really, really important nuance. It’s not in conflict with what John was saying about monetizing it. And it’s also not in conflict with what. My friend Schmarrs was saying about, ROI is so misused as a term in business. It’s just kind of nuts.[00:51:25] It’s more like a shorthand way of conveying, did we get value[00:51:31] John Thompson: yeah. And the reason I say that we denominated everything in currency is that’s generally one of the only ways. to get executives interested. If you go in and say, Oh, we’re going to improve this. We’re going to improve that. They’re like, I don’t care. If I say this project is going to take 6 months and it’s going to give you 42 million and it’s going to cost you nothing, then they’re like, tell me more, and going back to what Bill had said earlier, we need to open our aperture on what we do with these projects when we were at Dell or Bill and I swapped our times at Dell, we actually did a project with a hospital system in the United States and over 2 years.[00:52:11] We knocked down the incidence of post surgical sepsis by 72%. We saved a number of lives. We saved a lot of money, too, but we saves people’s lives. So analytics can do a lot. Most of the people are focused on. Oh, how fast can we optimize the search engine algorithm? Or, how can we get the advertisers more yield or more money?[00:52:32] There’s a lot of things we can do to make this world better. We just have to do it.[00:52:36] Mark Stouse: The fastest way to be more efficient is to be more effective, right? I mean, and so when I hear. CEOs and CFOs, because those are the people who use this language a lot. Talk about efficiency. I say, whoa, whoa, hold on. You’re not really talking about efficiency. You’re talking about cost cutting.[00:52:58] Those two things are very different. And it’s not that you shouldn’t cut costs if you need to, but it’s not efficiency. And ultimately you’re not going to cut your way into better effectiveness. It’s just not the way things go.[00:53:14] John Thompson: Amen.[00:53:15] Mark Stouse: And so, this is kind of like the old statement about physicists,[00:53:18] if they’re physicists long enough, they turn into philosophers. I think all three of us, have that going on. Because we have seen reality through a analytical lens for so long that you do actually get a philosophy of things.[00:53:38] Dr Genevieve Hayes: So what I’m hearing from all of you is that for data scientists to create value for the businesses that they’re working for, they need to start shifting their approach to basically look at how can we make the businesses needs. And how can we do that in a way that can be expressed in the business’s language, which is dollars and cents, but also, as Bill pointed out value in terms of the community environment.[00:54:08] So less financially tangible points of view.[00:54:11] Bill Schmarzo: And if I could just slightly add to that, I would say first thing that they need to do is to understand how does our organization create value for our constituents and stakeholders.[00:54:22] Start there. Great conversation. What are our desired outcomes? What are the key decisions? How do we measure success? If we have that conversation, by the way, it isn’t unusual to have that conversation with the business stakeholders and they go I’m not exactly sure.[00:54:37] John Thompson: I don’t know how that works.[00:54:38] Bill Schmarzo: Yeah. So you need to find what are you trying to improve customer retention? You’re trying to increase market share. What are you trying to accomplish and why and how are you going to measure success? So the fact that the data science team is asking that question, because like John said, data science can solve a whole myriad of problems.[00:54:54] It isn’t that it can’t solve. It can solve all kinds. That’s kind of the challenge. So understanding what problems we want to solve starts by understanding how does your organization create value. If you’re a hospital, like John said, reducing hospital acquired infections, reducing long term stay, whatever it might be.[00:55:09] There are some clear goals. Processes initiatives around which organizations are trying to create value[00:55:18] Dr Genevieve Hayes: So on that note, what is the single most important change our listeners could make tomorrow to accelerate their data science impact and results?[00:55:28] John Thompson: I’ll go first. And it’s to take your data science teams and not merge them into operational teams, but to introduce the executives that are in charge of these areas and have them have an agreement that they’re going to work together. Start there.[00:55:46] Bill Schmarzo: Start with how do you how does the organization create value? I mean understand that fundamentally ask those questions and keep asking until you find somebody in the organization who can say we’re trying to do this[00:55:57] Mark Stouse: to which I would just only add, don’t forget the people are people and they all have egos and they all want to appear smarter and smarter and smarter. And so if you help them do that, you will be forever in there must have list, it’s a great truth that I have found if you want to kind of leverage bills construct, it’s the economies of ego.[00:56:24] Bill Schmarzo: I like[00:56:24] John Thompson: right, Mark, wrap this up. When’s your book coming out? What’s the title?[00:56:28] Mark Stouse: It’s in July and I’ll be shot at dawn. But if I tell you the title, but so I interviewed several hundred fortune, 2000 CEOs and CFOs about how they see go to market. The changes that need to be made in go to market. The accountability for it all that kind of stuff. And so the purpose of this book really in 150, 160 pages is to say, Hey, they’re not all correct, but this is why they’re talking to you the way that they’re talking to you, and this is why they’re firing.[00:57:05] People in go to market and particularly in B2B at an unprecedented rate. And you could, without too much deviation, do a search and replace on marketing and sales and replace it with data science and you’d get largely the same stuff. LinkedIn,[00:57:25] Dr Genevieve Hayes: for listeners who want to get in contact with each of you, what can they do?[00:57:29] John Thompson: LinkedIn. John Thompson. That’s where I’m at.[00:57:32] Mark Stouse: Mark Stouse,[00:57:34] Bill Schmarzo: And not only connect there, but we have conversations all the time. The three of us are part of an amazing community of people who have really bright by diverse perspectives. And we get into some really great conversations. So not only connect with us, but participate, jump in. Don’t be afraid.[00:57:51] Dr Genevieve Hayes: And there you have it, another value packed episode to help you turn your data skills into serious clout, cash, and career freedom. If you found today’s episode useful and think others could benefit, please leave us a rating and review on your podcast platform of choice. That way we’ll be able to reach more data scientists just like you.[00:58:11] Thanks for joining me today, Bill, Mark, and John.[00:58:16] Mark Stouse: Great being with[00:58:16] John Thompson: was fun.[00:58:18] Dr Genevieve Hayes: And for those in the audience, thanks for listening. I’m Dr. Genevieve Hayes, and this has been value driven data science. The post Episode 53: A Wake-Up Call from 3 Tech Leaders on Why You're Failing as a Data Scientist first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Key Moments: Focusing on Value with Bill Schmarzo 1:48Unlocking the Collective Genius with Walid Mehanna 4:07Building a Data-Literate Workforce with Valerie Logan 5:58Creating a Human-Centric AI Strategy with Sadie St. Lawrence 7:40Selecting the Right Tools with Katie Russell 11:23Implementing tools responsibly with Robert Garnett 16:00Why Clean Data Matters with Barr Moses 19:36Ensuring Responsible AI for the Long-Term with Dr. Gary Marcus 25:45 Key Quotes:“Data-driven is not important. Value-driven—that's what's important. We should focus on value.” — Bill Schmarzo, Head of Customer Data Innovation at Dell Technologies“Our role was rather to activate the organizational muscle… to try things out and tell us what has the highest opportunity and possibility.” — Walid Mehanna, Chief Data and AI Officer at Merck Group“It's really a mindset and a muscle… we need to foster this kind of lasting change.” — Valerie Logan, CEO of the Datalodge“Teaching people to ask better questions is more about critical thinking than technology.” — Sadie St. Lawrence, Founder of the Human Machine Collaboration Institute“We wanted to make analytics accessible to everyone, combining real-time data and intuitive tools so every team member can gain insights and contribute to our mission to decarbonize.” — Katie Russell, Head of Data and Analytics at OVO Energy As we are looking at applications of AI within our environment, we are focused first on responsibility, making sure that we have a broad enough data set when we're building machine learning models, for instance. And so that's at the heart of anything that we do.” – Robert Garnett, Vice President for Government Analytics and Health Benefits Cost of Care at Elevance Health“Our world is moving towards a place where data is the product—and in that world, directionally accurate just doesn't cut it anymore.” — Barr Moses, CEO and Co-Founder of Monte Carlo“The tech policy that we set right now is going to really affect the rest of our lives.” —  Dr. Gary Marcus, Scientist, Advisor to Governments and Corporations, and Author of Taming Silicon ValleyGuest Bios Bill Schmarzo Bill Schmarzo has extensive hands-on experience in the areas of big data, data science, designthinking, data monetization, and data economics. Bill is currently part of Dell Technology's core data management leadership team, where he is responsible for spearheading customer co-creation engagement to identify and prioritize the key data management, data science, and data monetization requirements.Walid MehannaWalid Mehanna is Chief Data & AI Officer at Merck KGaA, Darmstadt, Germany, where he leads the company's Data & AI organization, delivering value, governance, architecture, engineering, and operations across the company globally. With many years experience in startups, IT, and consulting major corporations, Walid encompasses a strong understanding of the intersection between business and technology. Katie RussellKatie Russell is the Data Director at OVO Energy, leading teams of Data Scientists, Data Engineers and Analysts who are transforming OVO's data capability. As part of a technology led business, leveraging data using artificial intelligence keeps OVO truly innovative, delivering the best possible service for our customers. Rob GarnettRobert Garnett serves as Vice President for Government Analytics and Health Benefits Cost of Care at Elevance Health. In this role, he leads a data-driven organization supporting analytics and insights for Medicaid, Medicare, Commercial and enterprise customers in the areas of population health, cost of care, performance management, operational excellence, and quality improvement. Valerie LoganFounding The Data Lodge in 2019, Valerie is as committed to data literacy as it gets. With train-the-trainer bootcamps, and a peer community, she's certifying the world's first Data Literacy Program Leads. In 2023, The Data Lodge was acquired as the basis of a newly formed venture, Data Society Group (DSG), aimed at fostering data and AI literacy and cultural change at scale. Valerie is excited to also serve as the Chief Strategy Officer of DSG. Previously, Valerie was a Gartner Research VP in the CDO team where she pioneered Data Literacy research and was awarded Gartner's Top Thought Leadership Award.Sadie St. LawrenceSadie St. Lawrence  is on a personal mission to create a more compassionate and connected world through technology. Having grown up on a farm in Iowa she witnessed first-hand how advancements in technology rapidly changed how we work and earn a living, which in turn affected the overall success of a community. Through her work, she noticed that while many organizations and individuals have good intentions when it comes to D&I in data careers, there was a lack of progress.Dr. Gary MarcusGary Marcus is a leading voice in artificial intelligence. He is a scientist, best-selling author, and serial entrepreneur (Founder of Robust.AI and Geometric.AI, acquired by Uber). He is well-known for his challenges to contemporary AI, anticipating many of the current limitations decades in advance, and for his research in human language development and cognitive neuroscience. An Emeritus Professor of Psychology and Neural Science at NYU, he is the author of six books. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

I'm thrilled to announce that I'll be interviewing my friend and industry expert, Bill Schmarzo, on a topic that's reshaping the business landscape: "Unleashing Innovation: Leveraging GenAI Tools for Organizational Growth." In this conversation, we'll dive into how organizations can go beyond traditional productivity improvements and harness the power of GenAI tools like ChatGPT to foster creativity, innovation, and strategic growth. Don't miss out on Bill's insights and real-world examples of how forward-thinking companies are breaking new ground with GenAI. Stay tuned for an enlightening discussion that could transform your approach to business innovation! #data #ai #dell #theravitshow

Delve into the hot topic of unified analytics, aiming to provide organizations with a comprehensive view of their operations despite the myriad of data sources, tools, and technologies available. Join an All-Star cast including Bill Schmarzo, Mark Palmer, Ron Itelman, and Juan Cruz Viotti, to reflect on the capabilities of generative AI, highlight the potential pitfalls of blindly adopting AI-generated content and the importance of understanding its capabilities for effective utilization in an enterprise setting.  Tune in as the experts explore strategies for harnessing the power of AI while ensuring its safe and impactful integration into organizational workflows.

Bill Schmarzo is The Dean of Big Data, author of four books, and currently Customer Advocate, Data Management Incubation at Dell Technologies.

In this episode, I am interviewing Bill Schmarzo, the brilliant author behind 'AI & Data Literacy: Empowering Citizens of Data Science.' Join us as we delve into the fascinating world of data, AI, and the power of becoming a 'Citizen of Data Science.' Bill unlocks the secrets to navigating data privacy, ethics, and the inner workings of AI, all while empowering individuals and organizations to make informed, impactful decisions. Whether you're a data enthusiast, a tech leader, or simply curious about the future of AI, this conversation is your gateway to understanding, leveraging, and thriving in the data-driven landscape. 

Episode OverviewIn this 30th episode of the CDO Matter Podcast, Malcolm has an inspired exchange with the ‘Dean of Big Data,' Bill Schmarzo. Bill is an accomplished author, teacher, and C-level executive with a depth of knowledge in building and running large-scale data operations in service of some of the biggest companies in the world.In the discussion of his most recent book, “AI & Data Literacy,” Bill shares his insights into the six key responsibilities that all consumers of AI must fulfill to optimize their relationship with this transformative technology. In becoming more AI literate, we increase the likelihood that the utility of AI will be maximized for the good of society and not just the corporate bottom line. A critical component of this includes quantifying the concept of ‘ethics,' which Bill strongly advocates is not only possible, but is absolutely necessary.Beyond the topic of AI, Bill and Malcolm touch on some of the bigger challenges CDOs face daily, including the importance of connecting data investments to business outcomes and how to potentially solve the ‘value' equation inherent to every data estate. For CDOs seeking inspiration about the future of data management in an AI-enabled future, look no further than this episode of CDO Matters.Episode Links & Resources:• Follow Malcolm Hawker on LinkedIn• Follow Bill Schmarzo on LinkedIn• Read Bill Schmarzo's new book, 'AI & Data Literacy: Empowering Citizens of Data Science'

What is the true value of data? Whether you've been collecting data for years or are just getting started, the reality is that data holds zero value on its own. In fact, it's a huge cost. In this episode of The Data Chief, industry veteran Bill Schmarzo, currently in Customer Data Innovation at Dell Technologies, shares why modern data leaders must let go of the notion of being data-driven and focus on business outcomes. He also discusses how having more data isn't always the right move, especially when considering larger business goals.Tune in to learn:Demonstrating data's value in an uncertain economy (7:36)Defining data KPIs (16:54)The importance of data-driven decision-making (22:43)Moving from “data-driven” to “value-driven” (33:24)Modern data culture (42:08)Mentions:Data Analytics Recession PlaybookThe top 10 books every data and analytics leader must readGet even more insights from data and analytics leaders like Bill on The Data Chief.  Mission.org is a media studio producing content for world-class clients. Learn more at mission.org.

In today's episode of the engatica interview series, And today we have BILL SCHMARZO the chief technology officer of the Big Data Practice of EMC Global Services on the show! We spoke about disciplines that drive organizational empowerment and digital transformation and so much more. The engatica interview series is a powerhouse of insights from industry experts and influencers from around the world. A platform that provides the latest news on AI, Automation, and technologies that will help you grow your business. Website: www.engatica.com Follow us on- LinkedIn: https://www.linkedin.com/company/join-engatica/ Twitter: https://twitter.com/joinEngatica Facebook: https://www.facebook.com/joinEngatica Instagram: https://www.instagram.com/joinengatica/ Come, be a part of our community - learn, share and grow with us. About Engati: Engati believes that the way you deliver customer experiences can make or break your brand. Our mission is to help you deliver unforgettable experiences to build deep, lasting connections with our Chatbot and Live Chat platform. It is a one-stop platform for powerful customer engagements. With our intelligent bots, we help you create the smoothest of customer experiences, with minimal coding. And now, we're even helping you answer your customers' most complicated questions in real-time with Engati Live Chat. Website: https://www.engati.com/ Talk to us: contact@engati.com  #technology #ai #artificialintelligence #data #business #data #bigdata

Do you really need a data-driven culture? Maybe not. According to Bill Schmarzo, the CEO's mandate is to become value-driven, not data-driven. For analytics teams that means one thing: no one cares about your data, they want results! In this episode of Leaders of Analytics, Bill and I explore the economics of data & analytics and how to drive powerful decisions with data. Decisions that turn into business value. Bill is the author of four text books and one comic book on generating value with analytics. He is a long-serving business executive, adjunct professor, university educator and global influencer in the sphere of big data, digital transformation and data & analytics leadership. In this episode of Leaders of Analytics, we discuss: Why Bill has split his career between corporate leadership and education What value engineering is and how it pertains to data and analytics How to determine the economic value of data and analytics Why data management the single most important business discipline in the 21st century, and much more. Bill's website: https://deanofbigdata.com/ Bill on LinkedIn: https://www.linkedin.com/in/schmarzo/ Bill on Twitter: https://twitter.com/schmarzo

Do you really need a data-driven culture? Maybe not. According to Bill Schmarzo, the CEO's mandate is to become value-driven, not data-driven. For analytics teams that means one thing: no one cares about your data, they want results! In this episode of Leaders of Analytics, Bill and I explore the economics of data & analytics and how to drive powerful decisions with data. Decisions that turn into business value. Bill is the author of four text books and one comic book on generating value with analytics. He is a long-serving business executive, adjunct professor, university educator and global influencer in the sphere of big data, digital transformation and data & analytics leadership. In this episode of Leaders of Analytics, we discuss: Why Bill has split his career between corporate leadership and education What value engineering is and how it pertains to data and analytics How to determine the economic value of data and analytics Why data management the single most important business discipline in the 21st century, and much more. Bill's website: https://deanofbigdata.com/ Bill on LinkedIn: https://www.linkedin.com/in/schmarzo/ Bill on Twitter: https://twitter.com/schmarzo  

If data and analytics are going to be taken seriously by business executives, we need to talk about value in their language. Today I speak with the Dean of Big Data Bill Schmarzo about the skills, mindsets and mental models we need as data practitioners to find a revenue connection in everything we do. Follow Bill Schmarzo on https://www.notion.so/Bill-Schmarzo-COPY-e7e2e67907974582b796208b523a7cfa (LinkedIn). Full episode page: https://www.discoveringdata.com/podcast/039-bill-schmarzo-speak-the-language-of-the-business

As you can probably tell from the interview, I've known Bill for a while now and we just click. He grew up playing the trumpet, so did I, we were both in Jazz Bands and to boot he was into his sport Basketball and I was into Tennis as my sport! With a lot in common, it was really great to get to know more about Bill, his character and what lies beneath the skin. We had a great conversation and I'm sure you will hear it and feel it as we meander through the questions. It's clear to me that Bill is a philosophical soul with wisdom oozing out from every pore. I really enjoyed speaking to Bill and getting to know the man more intimately. Thank you for taking the time to listen to this season and when you get a moment please tell everyone about it. :-) Bill is currently working as Customer Advocate, Data Management Incubation at Dell Technologies. Professor and Educator. He has developed and teaches the “Big Data MBA” – a course for integrating data and analytics into the operations of the business – at University of San Francisco School of Management and National University of Ireland-Galway School of Business & Economics. He also lectures to numerous universities and organizations worldwide. As an author he has written 4 books “Big Data: Understanding How Data Powers Big Business,” “Big Data MBA: Driving Business Strategies with Data Science” and the forthcoming “Economics of Data, Analytics and Digital Transformation;” and published over 350 industry-leading articles and educational videos on the application of Big Data, Data Science, AI / ML and IoT to drive data monetization and digital transformation.

Murray Scott is joined by Bill Schmarzo, Data Management Innovation Customer Advocate, Dell Technologies & Honorary Professor in the J.E. Cairnes School of Business & Economics at NUI Galway – in a discussion on the ethical issues surrounding AI and the importance of human creativity.

In Episode 10 of Season 2 on Driven by Data: The Podcast, Kyle Winterbottom is re-joined by Bill Schmarzo, Customer Advocate and Data Management Incubation at Dell Technologies, where they discuss how things have gone since his first appearance on the podcast, which includes: What his new role at Dell Technologies entails Why he's getting the chance to put his money where his mouth is Why the transition away from the CIO was required and has happened How his prediction that there will be a CDMO (Chief Data Monetization Officer) is unfolding How the Data Leader is now the person responsible for quantifying value from Data Dell's approach to motivating and guiding organizing to becoming data-focused The importance of an organization understanding their data Why storing data is not enough and prevents organizations from driving value from their data How are organizations applying data and analytics to drive quantifiable benefits

We talk to the Dean of Big Data, Bill Schmarzo on how Design Thinking and IT are a power couple for transformation and innovation.Author page: http://www.DeanofBigData.comGet the book: https://www.amazon.com/Economics-Data-Analytics-Digital-Transformation-ebook/dp/B08M3XPCHW/Sponsor: Zencastr : http://www.zencastr.comGet 40% off the first 3 months for unlimited audio and HD video recordingsCode: wickedpodcastThe Wicked Podcast:Support us on Patreon: https://www.patreon.com/thewickedpodcastThe Wicked Podcast website: http://www.thewickedcompany.com/podcast/'The Wicked Company' book on Amazon.co.uk: https://www.amazon.co.uk/WICKED-COMPANY-When-Growth-Enough-ebook/dp/B07Y8VTFGY/The Wicked Company website: https:www.thewickedcompany.comMusic:'Inspired' by Kevin MacLeodSong: https://incompetech.filmmusic.io/song/3918-inspiredLicense: http://creativecommons.org/licenses/by/4.0/

The Iowa Idea: Bill Schmarzo “Don't create boxes, create swirls.” In this episode of The Iowa Idea Podcast, I'm joined by Bill Schmarzo. Bill is an author, professor, innovator, and consultant. He is the author of four books including The Economics of Data, Analytics and Digital Transformation. Bill is the former Chief Innovation Officer at […]

Throughout the life of the Enterprise Product Leadership podcast, I’ve had critical conversations on many product leadership topics such as driving a data strategy with Bill Schmarzo, how to test business ideas with David Bland, how to drive innovation with Geoffrey Moore, and much more. All of these topics are very important for product leaders but there’s one key ingredient that’s missing: the human element of alignment and collaboration.   The role of a leader is to ensure everybody is aligned and can contribute to the vision of the product and the company. Without that alignment, there’s no technology, business model, or data strategy that matters.   That is why I’m so excited to be joined by Stefano Mastrogiacomo on the show today. Stefano is a management consultant, professor, and author. His book, High-Impact Tools for Teams: 5 Tools to Align Team Members, Build Trust, and Get Results Fast, is the missing manual to achieve and maintain alignment throughout the life of your product.   From a leadership perspective, this is probably one of the most important episodes I’ll ever publish. I hope you enjoy the conversation as much as I did!   Episode Details: How to Create Clarity and Alignment across your Organization with Stefano Mastrogiacomo: “Alignment is not easy. It’s not an easy process. Especially when we have different backgrounds, different priorities, etc. So there has to be some initial starting point on which we all converge, regardless of where we come from.” — Stefano Mastrogiacomo   About Stefano Mastrogiacomo: Stefano Mastrogiacomo a management consultant, professor, and author. He is passionate about human coordination and he is the designer of the Team Alignment Map, the Team Contract, the Fact Finder, and the other tools presented in this book. He has been leading digital projects and advising project teams in international organizations for more than 20 years while teaching and doing research at the Universities of Lausanne, Switzerland. His interdisciplinary work is anchored in project management, change management, psycholinguistics, evolutionary anthropology, and design thinking.   Topics We Discuss in this Episode: Stefano Mastrogiacomo’s career background and journey How and why Stefano originally discovered the importance of alignment Why you need to focus on alignment and coordination in order to get anything done as a leader The key message of Stefano’s book, High-Impact Tools for Teams: 5 Tools to Align Team Members, Build Trust, and Get Results Fast The creative journey of writing his book Why alignment is crucial to driving innovation Key principles in coordination and alignment How Stefano recommends approaching the task of alignment between multiple departments as a product leader About the Team Alignment Tool how it helps structure an alignment conversation that creates mutual clarity Why understanding the mission is the first step to achieving alignment (and how to effectively create a mission with clarity) The human side of the innovation journey The four key requirements for human coordination What co-planning is and how it enables coordination in your organization How to address resistance towards change What you need to address to achieve alignment and coordination in order to have a successful innovation journey Key components that make the execution journey more smooth The challenges of coordination and how to address them How to foster a more collaborative and communicative team How to address common challenges around human coordination The similarities and differences between achieving alignment in small vs. large organizations Common challenges that leaders will face in implementing collaboration tools in their organizations (and how to overcome them) Stefano’s project roadmap and future goals   Product Leader Tip of the Week: Stefano recommends going to Strategyzer.com or TeamAlignment.co. and downloading their toolkit. Start experimenting on your projects today! If you would like examples, be sure to check out Stefano’s book, High-Impact Tools for Teams.   To Learn More About Stefano Mastrogiacomo: Stefano Mastrogiacomo’s LinkedIn The Team Alignment Co. High-Impact Tools for Teams: 5 Tools to Align Team Members, Build Trust, and Get Results Fast, by Stefano Mastrogiacomo, Alexander Osterwalder, Alan Smith (designer), and Trish Papadakos (designer)   Related Resources: com/Template — Download Daniel’s free IoT Product Strategy Template here! 32: “The Economic Value of Data with Bill Schmarzo” 33: “How to Test Business Ideas with David Bland” 37: “Crossing the Chasm: How to Effectively Drive Innovation with Geoffrey Moore” 38: “Is 5G Worth It? with Rob Tiffany” The Strategyzer Book Series Strategyzer   Want to Learn More? Sign up for my newsletter atcom/Join for weekly advice and best practices directly to your inbox! Visit com/Podcast for additional information, show notes, and episodes. Subscribe on Apple Podcasts so you don’t miss out on any of my conversations with product and thought leaders!   Pull Quotes (for Social-Media Use):   “Every innovation journey has at least two components. One is the product and service that we’re supposed to deliver — but this is delivered by people.” — Stefano Mastrogiacomo   “The message behind [High-Impact Tools for Teams] is that we have to take care of the innovation journey from a team perspective; from a people perspective.” — Stefano Mastrogiacomo   “An innovation journey carries very specific characteristics. By definition, if it’s innovation, it’s never been done before. So by definition, we’re almost all incompetent when we start. That’s the very idea of exploration.” — Stefano Mastrogiacomo   “Alignment is not easy. It’s not an easy process. Especially when we have different backgrounds, different priorities, etc. So there has to be some initial starting point on which we all converge regardless of where we come from.” — Stefano Mastrogiacomo   “I don’t see resistance to change when the mission is compelling and people are clear.” — Stefano Mastrogiacomo   “To enter an uncertain journey without having explicitly discussed [and] negotiated as a team, [will create] a very different journey than … be[ing] on the same page so that everyone feels confident … and knows what this journey is going to look like.” — Stefano Mastrogiacomo   “The mission can appear unknown in the beginning but it’s not — it’s a question of framing the mission [with] clarity.” — Stefano Mastrogiacomo  

The key focus of data and analytics should be about data monetization. And Data monetization starts with the understanding of data usage and people who value data. Given that business drivers like inventory reduction and predictive maintenance improvement are key business metrics, data scientists and data engineers should start understanding the business drivers that attach value to data. Bill Schmarzo believes that customer engagement and operational improvement are teh key drivers for monetizing data. Bill also believes that employee learning and adaptation should also be key objectives of technology initiatives such as AI & ML. According to Bill, Data Monetization Officer role is more important than a Chief Data Officer role and the role should be cross functional directly under CEO/COO.

The key focus of data and analytics should be about data monetization. And Data monetization starts with the understanding of data usage and people who value data. Given that business drivers like inventory reduction and predictive maintenance improvement are key business metrics, data scientists and data engineers should start understanding the business drivers that attach value to data. Bill Schmarzo believes that customer engagement and operational improvement are teh key drivers for monetizing data. Bill also believes that employee learning and adaptation should also be key objectives of technology initiatives such as AI & ML. According to Bill, Data Monetization Officer role is more important than a Chief Data Officer role and the role should be cross functional directly under CEO/COO.

With a 30+ year career in data warehousing, BI and advanced analytics under his belt, Bill has become a leader in the field of big data and data science – and, not to mention, a popular social media influencer. Having previously worked in senior leadership at DellEMC and Yahoo!, Bill is now an executive fellow and professor at the University of San Francisco School of Management as well as an honorary professor at the National University of Ireland-Galway. I’m so excited to welcome Bill as my guest on this week’s episode of Experiencing Data. When I first began specializing my consulting in the area of data products, Bill was one of the first leaders that I specifically noticed was leveraging design thinking on a regular basis in his work. In this long overdue episode, we dug into some examples of how he’s using it with teams today. Bill sees design as a process of empowering humans to collaborate with one another, and he also shares insights from his new book, “The? Economics of Data, Analytics and Digital Transformation.” In total, we covered: Why it’s crucial to understand a customer’s needs when building a data product and how design helps uncover this. (2:04) How running an “envisioning workshop” with a customer before starting a project can help uncover important information that might otherwise be overlooked. (5:09) How to approach the human/machine interaction when using machine learning and AI to guide customers in making decisions – and why it’s necessary at times to allow a human to override the software. (11:15) How teams that embrace design-thinking can create “organizational improvisation” and drive greater value. (14:49) Bill take on how to properly prioritize use cases (17:40) How toidentify a data product’s problems ahead of time. (21:36) The trait that Bill sees in the best data scientists and design thinkers (25:41) How Bill helps transition the practice of data science from being a focus on analytic outputs to operational and business outcomes. (28:40) Bill’s new book, “The Economics of Data, Analytics, and Digital Transformation.” (31:34) Brian and Bill’s take on the need for organizations to create a technological and cultural environment of continuous learning and adapting if they seek to innovate. (38:22) Quotes from Today’s Episode There’s certainly a UI aspect of design, which is to build products that are more conducive for the user to interact with – products that are more natural, more intuitive … But I also think about design from an empowerment perspective. When I consider design-thinking techniques, I think about how I can empower the wide variety of stakeholders that I need to service with my data science. I’m looking to identify and uncover those variables and metrics that might be better predictors of performance. To me, at the very beginning of the design process, it’s about empowering everybody to have ideas. – Bill (2:25) Envisioning workshops are designed to let people realize that there are people all across the organization who bring very different perspectives to a problem. When you combine those perspectives, you have an illuminating thing. Now let’s be honest: many large organizations don’t do this well at all. And the reason why is not because they’re not smart, it’s because in many cases, senior executives aren’t willing to let go. Design thinking isn’t empowering the senior executives. In many cases, it’s about empowering those frontline employees … If you have a culture where the senior executives have to be the smartest people in the room, design is doomed. – Bill (10:15) Organizational charts

In my 19th Episode I speak to the incredible and enigmatic Bill Schmarzo innovator, educator, influencer and the Dean of Big Data. We talk about: 1) The origin of the "Dean of Big Data! 2) How organisations identify sources of value creation with data 3) Why the "pre-work" or "foundations" are the investments companies need to forge at the beginning of their data strategies 4) Why Design Thinking is important in the process of data transformation 5) Why there is a need to marry Design Thinking with Data Science 6) Why data initiatives die of passive / aggressive behaviour 7) Why culture is so hard and why exec management need to let go and get out of the way 8) Why command and control structures continue to impede transformation 9) What is value engineering and why it's important in the process of data and business transformation 10) Why companies need to do "proof of value" before committing to use cases 11) Why the economics of learning are more powerful than the economies of scale 12) What is "orphaned analytics" and how companies need to avoid this analytics debt 13) Why looking at the Tesla FSD model should be the model and inspiration for data initiatives 14) Why organisation improvisation leads to an empowerment of teams 15) Why swirls are better than boxes for successful collaboration, innovation and successful outcomes 16) Why there needs to be a person in the company focused solely on "data monetisation! 17) Why data will be the catalyst for economic growth in the 21st Century 18) What is the "analytics chasm" and why does it exist 19) The birth of "nano economics" for data 20) How to optimise the machine - human interface About Bill: The “Dean of Big Data,” I bring a business-first approach to helping organizations leverage advanced analytics and data science to uncover the customer, product and operational insights buried in the organization's data that power digital transformation. He drives data monetization. Uniquely integrated Design Thinking with Data Science and employs a “Rapid exploration, rapid testing, failure-empowering, continuously-learning” data science development methodology that enables the monetization of the insights buried in the data. An influencer and innovator. Recognized as an industry leader in Big Data, Data Science, Design Thinking and Data Monetization, integrating these disciplines to drive organizational empowerment and digital transformation. Professor and Educator. He has developed and teaches the “Big Data MBA” – a course for integrating data and analytics into the operations of the business – at University of San Francisco School of Management and National University of Ireland-Galway School of Business & Economics. He also lectures to numerous universities and organizations worldwide. As an author he has written 4 books “Big Data: Understanding How Data Powers Big Business,” “Big Data MBA: Driving Business Strategies with Data Science” and the forthcoming “Economics of Data, Analytics and Digital Transformation;” and published over 350 industry-leading articles and educational videos on the application of Big Data, Data Science, AI / ML and IoT to drive data monetization and digital transformation.

Always a treat to take a random walk with the Dean of Big Data, Bill Schmarzo. In this conversation, we touch on quite a few bases, from how to print money with your data project, drive innovation through empowerment, leverage design thinking for better outcomes, and pass the ball when the situation dictates. Oh yeah, and we reviewed his latest book. Thanks, Bill, always a pleasure. Transcript & Show Notes  LinkedIn Article YouTube Video

In Episode 12 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Bill Schmarzo (The Dean of Big Data) the Former Chief Innovation Officer at Hitachi Vanatara. Bill is a published author, honorary professor and executive fellow, as well as holding influencer status across a number of industry subject matters. Bill specialises in the field of Data Monetisation and talks us through the key considerations (plus we chat about a bunch of other things) which includes: How "Forest Gump" moments defined his career Why he wrote his 4th book Why organisations need a Chief Data Monetisation Officer That empowerment is the secret to unlocking value in Data Analytics Cultural transformation is the key to Data/Digital Transformation Why everyone should think like a Data Scientist How Data Science is like a game of Final Fantasy II Why value created by selling data is minuscule in comparison to the value you get from using it How Data is an economics problem, not an accounting one Why use cases are the only way to generate value Why economies of learning are greater than economies of scale Link to book: https://www.amazon.co.uk/Economics-Data-Analytics-Digital-Transformation/dp/1800561415

Podcast Topics The StoryBrand Framework in the context of data science Design thinking in data science How to value data as a corporate asset (Current GAAP accounting standards do not permit data (intangible assets) to be capitalized on the balance sheet). Chipotle use case - improving same-store sales using data and analytics We’ll talk about the MMMM of Digital transformation Everything Bill learned about management he learned coaching little league baseball.

In this week's Industrial Talk Podcast and in conjunction with https://www.iotsworldcongress.com/ (IoT Solutions World Congress), we're talking to Bill Schmarzo - The Dean of Big Data about "The Dangers of Legacy Thinking and The Power of Hope in Innovation". Get the answers to your "Culture of Empowerment and Culture of Innovation" questions along with Bill's unique insight on the “How” on this Industrial Talk interview! You can find out more about Bill by the links below. Finally, get your exclusive free access to the https://industrialtalk.com/wp-admin/inforum-industrial-academy-discount/ (Industrial Academy) and a series on “https://industrialtalk.com/why-you-need-to-podcast/ (Why You Need To Podcast)” for Greater Success in 2020. All links designed for keeping you current in this rapidly changing Industrial Market. Learn! Grow! Enjoy! BILL'S CONTACT INFORMATION:Personal LinkedIn: https://www.linkedin.com/in/schmarzo/ (https://www.linkedin.com/in/schmarzo/) Personal Twitter: https://twitter.com/schmarzo (https://twitter.com/schmarzo) Get Your Access to the IoT Solutions World Congress Digital Summit: https://www.iotsworldcongress.com/activities/digital-summit/ (HERE) PODCAST VIDEO:https://youtu.be/TPSPPSue7Jc THE STRATEGIC REASON "WHY YOU NEED TO PODCAST":https://industrialtalk.com/why-you-need-to-podcast/ () OTHER GREAT INDUSTRIAL RESOURCES:CAP Logistics: https://www.caplogistics.com/ (https://www.caplogistics.com/) Hitachi Vantara: https://www.hitachivantara.com/en-us/home.html (https://www.hitachivantara.com/en-us/home.html) Industrial Marketing Solutions:  https://industrialtalk.com/industrial-marketing/ (https://industrialtalk.com/industrial-marketing/) Industrial Academy: https://industrialtalk.com/industrial-academy/ (https://industrialtalk.com/industrial-academy/) Industrial Dojo: https://industrialtalk.com/industrial_dojo/ (https://industrialtalk.com/industrial_dojo/) Safety With Purpose Podcast: https://safetywithpurpose.com/ (https://safetywithpurpose.com/) YOUR INDUSTRIAL DIGITAL TOOLBOX:LifterLMS: Get One Month Free for $1 – https://lifterlms.com/ (https://lifterlms.com/) Active Campaign: https://www.activecampaign.com/?_r=H855VEPU (Active Campaign Link) Social Jukebox: https://www.socialjukebox.com/ (https://www.socialjukebox.com/) Industrial Academy (One Month Free Access And One Free License For Future Industrial Leader):https://industrialtalk.com/wp-admin/inforum-industrial-academy-discount/ () Business Beatitude the Book Do you desire a more joy-filled, deeply-enduring sense of accomplishment and success? Live your business the way you want to live with the BUSINESS BEATITUDES...The Bridge connecting sacrifice to success. YOU NEED THE BUSINESS BEATITUDES! TAP INTO YOUR INDUSTRIAL SOUL, RESERVE YOUR COPY NOW! BE BOLD. BE BRAVE. DARE GREATLY AND CHANGE THE WORLD. GET THE BUSINESS BEATITUDES! https://industrialtalk.com/business-beatitude-reserve/ ( Reserve My Copy and My 25% Discount) PODCAST TRANSCRIPT: 

Welcome back to Enterprise Product Leadership! I’m your host, Daniel Elizalde. With a new season, comes new changes — one of which is the title of the podcast! From IoT Product Leadership to Enterprise Product Leadership, the podcast is broadening its scope beyond IoT to focus on the overall challenges facing enterprise and industrial product leaders to drive solutions from idea to first release. Joining me today as my first guest of this brand new season is Bill Schmarzo, Chief Innovation Officer at Hitachi Vantara. Bill is known as the ‘Dean of Big Data’ for the work he does in academia. He is also the author of three books that advise organizations on where and how to leverage big data and data science to power their business models.   We’ve all heard the phrase, “Data is the new oil,” but what does that really mean? And more importantly, what does it mean for us as product leaders? In this episode, we dive into this topic as well as the concept of the economic value of data. We also get a masterclass on how Bill works with his customers to prioritize opportunities and capitalize on the value of data.   Episode Details: The Economic Value of Data with Bill Schmarzo: “Data is a unique asset. It never wears out [and] it never depletes. You can use it across an infinite number of use cases at a zero marginal cost [which] makes it the single most asset in the world.” — Bill Schmarzo   About Bill Schmarzo: Bill Schmarzo is the Chief Innovation Officer at Hitachi Vantara. Bill is known as the ‘Dean of Big Data’ for the work he does in academia. He is recognized as an industry leader in big data, data science, design thinking, and data monetization. He is also the author of several books and has published over 350 industry-leading articles and educational videos on the application of big data and data science.   Topics We Discuss in this Episode: Exciting changes to the podcast Guest lineup for this season’s upcoming episodes Bill Schmarzo’s career background About Bill’s current role and focus as Chief Innovation Officer at Hitachi Vantara How to get value out of data Bill’s customer-centric, value-engineering approach Key lessons Bill has learned as a data scientist What it really means to empower your frontline How design-thinking has helped address challenges in data science Why being customer-centric is of critical importance Advice on how you can get support from leadership and senior management to allow for forward-thinking innovation and new ideas within your organization Fascinating concepts around the economic value of data from his newest book, The Economics of Data, Analytics, and Digital Transformation Insights on how product management/leadership roles and data scientists have evolved in terms of working together and determining the economic value of data How do you future-proof your career? Advice for product leaders who are new to developing data value-driven solutions   Product Leader Tip of the Week: Bill’s advice to product leaders who are new to developing data value-driven solutions: Design-thinking is critical. Understand your customers, the customer journey, and service design. You’re going to have to get very intimate with your customers about not providing products that put the onus of usage on them, but designing services that put the onus on you and your design team. Teach all of your business stakeholders how to think like a data scientist. The mentality of thinking like a data scientist (of exploring and bringing in a diverse set of perspectives) in order to find variables and metrics that might be better predictors of performance is key.   To Learn More About Bill Schmarzo: Bill Schmarzo’s LinkedIn Bill Schmarzo’s Books Hitachi Vantara   Related Resources: The Art of Thinking Like a Data Scientist, by Bill Schmarzo The Economics of Data, Analytics, and Digital Transformation: The theorems, laws, and empowerments to guide your organization’s digital transformation, by Bill Schmarzo   Want to Learn More?   Sign up for my newsletter at DanielElizalda.com/Join for weekly advice and best practices directly to your inbox!   Visit DanielElizalda.com/Podcast for additional information, show notes, and episodes. Subscribe on iTunes so you don’t miss out on any of my conversations with product and thought leaders!

In this episode of Data Brilliant, our host, Joe DosSantos talks with The Dean of Big Data, Bill Schmarzo, to focus on the importance of looking at data through a granular lens. From understanding where and how to apply data science to power an organization to championing a data-first approach toward decision-making, Joe and Bill explore the importance of viewing data science as a team sport and the ethical responsibility we all have in how we use data today. They discuss how the key to obtaining business value from data is in the granularity of insights, championing diversity of thinking, and monetizing outliers. See acast.com/privacy for privacy and opt-out information.

This week, Chris has a great conversation with Bill Schmarzo, Chief Innovation Officer at Hitachi Vantara. Bill maintains a consultancy practice within Hitachi that helps customers build processes and identify data that can be used to create business value within an organisation. In this discussion, Bill outlines the process for identifying opportunities, capturing the data […] The post #178 – Monetising the Value of Data appeared first on Storage Unpacked Podcast.

In this week's Industrial Talk Podcast we're talking to the Titans of Data, Bill Schmarzo, Chief Innovation Officer with Hitachi Vantara and Kirk Borne, Data Science Fellow and Executive Advisor at Booz Allen Hamilton about "The Power of Data and Impact to Business and Culture". Get your answers to the real power behind data analytics along with Bill's and Kirk's unique insight on the “How” on this Industrial Talk interview! You can find out more about Bill and Kirk and the wonderful team at Hitachi Vantara and Booz Allen Hamilton at the links below. Finally, get your exclusive free access to the https://industrialtalk.com/wp-admin/inforum-industrial-academy-discount/ (Industrial Academy) and a series on “https://industrialtalk.com/why-you-need-to-podcast/ (Why You Need To Podcast)” for Greater Success in 2020 and beyond. All links designed for keeping you current in this rapidly changing Industrial Market. Survive! Rebuild! Prosper! BILL SCHMARZO'S CONTACT INFORMATION:Personal LinkedIn: https://www.linkedin.com/in/schmarzo/ (https://www.linkedin.com/in/schmarzo/) Company LinkedIn: https://www.linkedin.com/company/hitachi-vantara/ (https://www.linkedin.com/company/hitachi-vantara/) Company Facebook: https://www.facebook.com/HitachiVantara (https://www.facebook.com/HitachiVantara) Company Website: https://www.hitachivantara.com/en-us/home.html (https://www.hitachivantara.com/en-us/home.html) Company Twitter:  https://twitter.com/HitachiVantara (https://twitter.com/HitachiVantara) Personal Twitter: https://twitter.com/schmarzo (https://twitter.com/schmarzo) KIRK BORNE'S CONTACT INFORMATION:Personal LinkedIn: https://www.linkedin.com/in/kirkdborne/ (https://www.linkedin.com/in/kirkdborne/) Company LinkedIn: https://www.linkedin.com/company/booz-allen-hamilton/ (https://www.linkedin.com/company/booz-allen-hamilton/) Company Facebook: https://www.facebook.com/boozallen (https://www.facebook.com/boozallen) Company Website: https://www.boozallen.com/ (https://www.boozallen.com/) Company Twitter:  https://twitter.com/BoozAllen (https://twitter.com/BoozAllen) Personal Twitter: https://twitter.com/KirkDBorne (https://twitter.com/KirkDBorne) EVENTS TO PUT ON YOUR CALENDAR:Deliver on bottom line, Realize the economic value of data: https://www.brighttalk.com/webcast/15913/435811?utm_source=Hitachi+Vantara+%28Formerly+Pentaho%29&utm_medium=brighttalk&utm_campaign=435811 (Register Here!) Speed Discovery, Comprehension and Trust in Data at Scale: https://www.brighttalk.com/webcast/15913/409387?utm_source=Hitachi+Vantara+%28Formerly+Pentaho%29&utm_medium=brighttalk&utm_campaign=409387 (Register Here!) Adopting to Corona Virus Supply Chain Disruptions: https://ucdenver.zoom.us/webinar/register/WN_WUFwSFQFQN6tD5g9drEiKg (Register Here!) PODCAST VIDEO:https://youtu.be/PhRRoGeHLmA THE STRATEGIC REASON "WHY YOU NEED TO PODCAST":https://industrialtalk.com/why-you-need-to-podcast/ () OTHER GREAT INDUSTRIAL RESOURCES:Safeopedia: https://www.safeopedia.com/ (https://www.safeopedia.com/) Industrial Marketing Solutions:  https://industrialtalk.com/industrial-marketing/ (https://industrialtalk.com/industrial-marketing/) Industrial Academy: https://industrialtalk.com/industrial-academy/ (https://industrialtalk.com/industrial-academy/) Industrial Dojo: https://industrialtalk.com/industrial_dojo/ (https://industrialtalk.com/industrial_dojo/) Safety With Purpose Podcast: https://safetywithpurpose.com/ (https://safetywithpurpose.com/) YOUR INDUSTRIAL DIGITAL TOOLBOX:LifterLMS: Get One Month Free for $1 – https://lifterlms.com/ (https://lifterlms.com/) Active Campaign: https://www.activecampaign.com/?_r=H855VEPU (Active Campaign Link) BombBomb: http://www.bombbomb.com/?bbref=INDUSTRIALTALKPODCAST (BombBomb Link) Social Jukebox: https://www.socialjukebox.com/ (https://www.socialjukebox.com/) Industrial Academy (One...

"The Wave of Change" Episode 025 "Chief 'Innovation' Officer" The Silo Stepper! Another great chat with Da Dean of Big Data, Bill Schmarzo @schmarzo ... We get right into it, and Bill gives an overview of what the Chief Innovation Officer role is all about, spoiler alert it's all about empowering humans in working on adopting emerging technology.  No compromising, synergize! AI is not at 'The Mahogany Row', it needs to start at the front line.  Don't be scared by the data!  

Today's guest is Bill Schmarzo, Chief Innovation Officer at Hitachi Vantara. Bill Schmarzo is regarded as one of the top Digital Transformation influencers on Big Data and Data Science. His career spans over 30 years in data warehousing, BI and advanced analytics. Currently, “The Dean of Big Data” guides Hitachi Vantara's technology strategy and drives “co-creation” efforts with select customers to leverage IoT and analytics to power digital transformation. Bill formerly served as CTO of Big Data at Dell EMC and as the VP of Analytics at Yahoo! Bill is passionate about teaching his students and followers how to Think Like a Data Scientist, an approach advocating the capacity to learn concepts and methodologies, enabling transferable skills in a world driven by the rapid introduction and adoption of new tools and open source frameworks. In the episode, Bill will tell you about: How he got interested in Data Analytics, What he learned during his time with Yahoo and Dell EMC, Balancing his day-to-day work along with writing books, Building his team at Hitachi Vantara, What you'll learn from ‘The Art of Thinking like a Data Scientist,' What he loves about his job, and Advice on how to make the most out of your career

In this week's Industrial Talk Podcast we're talking to Bill Schmarzo, Chief Innovation Officer with Hitachi Vantara about "The Power of Collaboration, Embracing Ambiguity and the REAL Value of Data". Get the answers to the real power behind data along with Bill's unique insight on the “How” on this Industrial Talk interview! You can find out more about Bill and the wonderful team at Hitachi Vantara at the links below. Finally, get your exclusive free access to the https://industrialtalk.com/wp-admin/inforum-industrial-academy-discount/ (Industrial Academy) and a series on “https://industrialtalk.com/why-you-need-to-podcast/ (Why You Need To Podcast)” for Greater Success in 2020 and beyond. All links designed for keeping you current in this rapidly changing Industrial Market. Survive! Rebuild! Prosper! BILL SCHMARZO'S CONTACT INFORMATION:Personal LinkedIn: https://www.linkedin.com/in/schmarzo/ (https://www.linkedin.com/in/schmarzo/) Company LinkedIn: https://www.linkedin.com/company/hitachi-vantara/ (https://www.linkedin.com/company/hitachi-vantara/) Company Facebook: https://www.facebook.com/HitachiVantara (https://www.facebook.com/HitachiVantara) Company Website: https://www.hitachivantara.com/en-us/home.html (https://www.hitachivantara.com/en-us/home.html) Company Twitter:  https://twitter.com/HitachiVantara (https://twitter.com/HitachiVantara) PODCAST VIDEO:https://youtu.be/5inbfzgQ8-U THE STRATEGIC REASON "WHY YOU NEED TO PODCAST":https://industrialtalk.com/why-you-need-to-podcast/ () OTHER GREAT INDUSTRIAL RESOURCES:Safeopedia: https://www.safeopedia.com/ (https://www.safeopedia.com/) Industrial Marketing Solutions:  https://industrialtalk.com/industrial-marketing/ (https://industrialtalk.com/industrial-marketing/) Industrial Academy: https://industrialtalk.com/industrial-academy/ (https://industrialtalk.com/industrial-academy/) Industrial Dojo: https://industrialtalk.com/industrial_dojo/ (https://industrialtalk.com/industrial_dojo/) Safety With Purpose Podcast: https://safetywithpurpose.com/ (https://safetywithpurpose.com/) YOUR INDUSTRIAL DIGITAL TOOLBOX:LifterLMS: Get One Month Free for $1 – https://lifterlms.com/ (https://lifterlms.com/) Active Campaign: https://www.activecampaign.com/?_r=H855VEPU (Active Campaign Link) BombBomb: http://www.bombbomb.com/?bbref=INDUSTRIALTALKPODCAST (BombBomb Link) Social Jukebox: https://www.socialjukebox.com/ (https://www.socialjukebox.com/) Industrial Academy (One Month Free Access And One Free Licence For Future Industrial Leader):https://industrialtalk.com/wp-admin/inforum-industrial-academy-discount/ () Business Beatitude the Book Do you desire a more joy-filled, deeply-enduring sense of accomplishment and success? Live your business the way you want to live with the BUSINESS BEATITUDES...The Bridge connecting sacrifice to success. YOU NEED THE BUSINESS BEATITUDES! TAP INTO YOUR INDUSTRIAL SOUL, RESERVE YOUR COPY NOW! BE BOLD. BE BRAVE. DARE GREATLY AND CHANGE THE WORLD. GET THE BUSINESS BEATITUDES! https://industrialtalk.com/business-beatitude-reserve/ ( Reserve My Copy and My 25% Discount)

Bill Schmarzo is regarded as one of the top Digital Transformation influencers on big data and data science. His career spans over 30 years in data warehousing, BI and advanced analytics before joining Hitachi Vantara, Bill served as CTO of Big Data at Dell EMC and VP of Analytics at Yahoo! He is the author of “The Art of Thinking Like a Data Scientist,” “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science.” Bill has a Bachelor's degree in math, computer science and business administration from Coe College and his MBA from the University of Iowa. He is an avid blogger and in 2019 was ranked the #4 Big Data influencer, #4 Data Science, and #6 Digital Transformation influencer worldwide by Onalytica.Bill is a fascinating and really insightful guy. We had a great conversation. He shares steps and practical advice you can use to integrate big data into your business and make huge transformations. Armed with the insights from big data, companies can improve customer experience and products, add value, and increase return on investment.Show Notes:[00:28] Bill Schmarzo is the Chief Innovation Officer for Hitachi Vantara.[00:43] This episode is really going to be focused on big data, what you do about it, and how you get people’s heads wrapped around it.[01:17] Most organizations don’t have an appreciation for data as a source of innovation.[02:47] Bill’s turning point for his appreciation of data was when he was at Yahoo.[04:25] Big data has allowed organizations to change their role as business leaders.[05:41] Data is an asset to be exploited and help drive organization and business initiatives.[07:10] The value of data is how you use it. Start with your company’s business and strategic initiatives for the next 12 months.[07:43] How can the data I have help accomplish what is important in our business?[09:23] When you bring the data science process into it the decisions haven’t changed, but the answers have changed.[10:07] The first mistake companies make is not focusing on what is important for the business.[10:43] Empower the front lines people that are close to the action.[13:14] They build their analytic solution cores one use case at a time. They go through a process to decide the data sets they need for that particular case.[16:06] Analytic modules are an asset that don’t depreciate with use, instead they actually appreciate in value.[18:21] Companies want analytic modules that they can quickly deploy and that learn and grow with their unique data.[20:07] Gain organizational consensus on the high-priority use cases to go after.[21:14] Next they do a proof of value, proving they can actually deliver value and solve the problem.[22:42] The digital media marketing, financial services and retail have big opportunities. Healthcare and industrial companies are trying to catch up as well.[23:16] If you don’t have a culture of collaboration this doesn’t work.[23:35] Don’t overcomplicate it. Keep it simple. It will get complicated enough.[24:01] At the end of the day, the only people that are actually creating value are the customers and the customer’s customers.[24:36] Don’t be afraid to make mistakes. You have to be fearless. Fear is only failure if you don’t learn from it. If you’re not failing enough, you're not pushing the edges or trying hard enough.Links and Resources:State of the CIO Podcast WebsiteState of the CIO Podcast on Apple PodcastsDan on LinkedInHypothesis Development CanvasBill on TwitterBill on LinkedInBig Data MBA: Driving Business Strategies with Data ScienceBig Data: Understanding How Data Powers Big BusinessThe Art of Thinking Like a Data Scientist

Episode 016 "To The Internal Win!"  Bill Schmarzo, Chief Innovation Officer at Hitachi Vantara.  Bill had big news that he had shared online, thinking this would be a great podcast, Tman went into action!  Bill said it's just internal news, no way said Tman!  The internal win made will lead to much more outwards success.  In the emerging landscape of change, internal change and winning allies is a huge win and may very well put your organization on a better path to be successful with embracing emerging technologies.  Far too often new technology is thrown out there because it's the latest and greatest without ryhme or reason.  Bill gives a great live demonstration of his framework and design thinking in action.  He also shares a great message about sticking with something and staying doing your best to stay at it even when you get discouraged,.

Episode 004 - "The Decade of Data Value..." Featuring the one and only 'The Dean of #BigData!' and CTO, IoT and Analytics at Hitachi Vantara, Bill Schmarzo! @schmarzo  Bill is a global force in Data Science, AI, and Analytics.  Bill shares insights that if followed will certainly drive value when adopting emerging technologies.  Bill details the importance of the role of the CIO, and not the traditional CIO!, in rolling out AI, Data Science, Analytics, and Automation.  Bill drives a great lesson on importance of defining value and economics as the main drivers for approaching new AI technologies.  The podcast started with the Topic of the new decade, "The Decade of Data Enablement"...Bill quickly changed this to, "The Decade of Data Value..."  a must listen! 

Welcome to episode 6 of the first season of “Your DataOps Advantage,” a podcast series by Hitachi Vantara! In this episode, podcast host Bill Schmarzo (our CTO of IoT and Analytics) sits down with our CIO, Renée Lahti, to highlight outcomes from Hitachi Vantara’s DataOps and Digital Value Enablement journey. This dynamic duo will discuss tips and tricks for overcoming big rocks, what they learned about the process and the big results they’ve produced for the organization and its customers. Tune in – it’s time to plant a money tree of your own!

Welcome to episode 5 of the first season of “Your DataOps Advantage,” a podcast series by Hitachi Vantara! In this episode, podcast host Bill Schmarzo (our CTO of IoT and Analytics) sits down with Mauro Damo (senior data scientist) to highlight the data science ‘gotchas’ along the DataOps and Digital Value Enablement journey. Together, these two data science gurus discuss big surprises, unanticipated tradeoffs and words of wisdom. Cue it up folks – it’s time to get geeky! (Note: the quality of audio in some parts of this podcast may not be great, but the content is, so we encourage you to listen!)

Hello and welcome to Episode 4 of the first season of the “Your DataOps Advantage” podcast series by Hitachi Vantara! In this episode podcast host, Bill Schmarzo (our CTO of IoT and Analytics) sits down with Jonathan Martin (our Chief Marketing Officer) to share insights about the life of a Chief Marketing Officer in today’s data crazed world. Together these two data champions discuss entertaining customers, marketing as the organizational growth catalyst and DataOps as the methodology to make it all happen. Bluetooth connect those headphones folks—it’s time we do more than just interrupt our customers!

Hello and welcome to Episode 2 of the first season of the “Your DataOps Advantage” podcast series by Hitachi Vantara! In this episode podcast host Bill Schmarzo (our CTO of IoT and Analytics) talks with Mike Foley (our Marketing Science Lead) and Joshua Siegel (our Global Co-Creation Lead, IoT and Analytics) as they share details around the Digital Value Enablement process, the upcoming Digital Envisioning Workshop, data culture transformation and how Hitachi Vantara is scaling data science across the organization. Tune in and catch a glimpse of how you can merge the art of the possible with the art of the practical.

Episode 1 of the first season of the “Your DataOps Advantage” podcast series by Hitachi Vantara! In this episode podcast host, Bill Schmarzo (our CTO of IoT and Analytics) sits down with Renee Lahti (our CIO) and Jonathan Martin (our CMO) to hear their story about the moment of epiphany that led Hitachi Vantara down the path of DataOps and Digital Value Enablement. Together, they confess their data sins, the strong partnership they’ve built, how the digital value enablement process can drive cultural change and the upcoming Digital Envisioning Workshop. Listen up—it’s time to have an epiphany of your own!

Hello and welcome to Episode 3 of the first season of the “Your DataOps Advantage” podcast series by Hitachi Vantara! In this episode, podcast host Bill Schmarzo (our CTO of IoT & Analytics) meets again with Renee Lahti (our CIO) and Jonathan Martin (our CMO) as they react to the previous day’s Digital Envisioning Workshop, the importance of knocking down organizational silos, data quality versus data action, and the importance of pragmatism. Plug those wireless headphones in folks—it’s time to unlearn something!

Hello and welcome to the first season of the “Your DataOps Advantage” podcast series by Hitachi Vantara! This podcast, hosted by our very own CTO of IoT and Analytics, Mr. Bill Schmarzo, will explore how our customers use DataOps to get the value out of their data. But to start, we will tell our own journey – the good, the bad and the ugly. This is a story of redemption, people! And, how we applied the power of DataOps and the Digital Value Enablement process to transform our approach to data lake monetization. Hang on—it’s going to be one wild ride.

Bill Schmarzo talks with Max Schmarzo about data utilization, digital twins, and the real value of an idea.

Bill Schmarzo is CTO of IoT and Analytics at Hitachi Vantara and Executive Fellow at the San Francisco School of Management. Our discussion covered Bill’s origins in data warehousing and business intelligence, how and why he had to “unlearn” traditional approaches to embrace Big Data techniques. The conversation explores some of the differences in approach between business intelligence, big data and IoT analytics, along with how Hadoop fundamentally changed the economics of data. He shares some of the vision Hitachi Vantara and the application-first approach along with the benefits that come with having Hitachi’s other businesses as a customer. Companies he’s most interested in are those that are automating machine learning to bring it to the masses including Big Squid.

In this podcast Stephen Gatchell (@stephengatchell) from @Dell talks about the ingredients of a successful data scientist. He sheds light on the importance of data governance and compliance in defining a robust data science strategy. He suggested tactical steps that executives could take in starting their journey to a robust governance framework. He talked about how to take away the scare from governance. He gave insights on some of the things leaders could do today to build robust data science teams and framework. This podcast is great for leaders seeking some tactical insights into building a robust data science framework. Timeline: 0:29 Stephen's journey. 4:45 Dell's customer experience journey. 7:39 Suggestions for a startup in regard to customer experience. 12:02 Building a center of excellence around data. 15:29 Data ownership. 19:18 Fixing data governance. 24:02 Fixing the data culture. 29:40 Distributed data ownership and data lakes. 32:50 Understanding data lakes. 35:50 Common pitfalls and opportunities in data governance. 38:50 Pleasant surprises in data governance. 41:30 Ideal data team. 44:04 Hiring the right candidates for data excellence. 46:13 How do I know the "why"? 49:05 Stephen's success mantra. 50:56 Stephen's best read. Steve's Recommended Read: Big Data MBA: Driving Business Strategies with Data Science by Bill Schmarzo http://amzn.to/2HWjOyT Podcast Link: https://futureofdata.org/want-to-fix-datascience-fix-governance-by-stephengatchell-futureofdata/ Steve's BIO: Stephen is currently a Chief Data Officer Engineering & Data Lake at Dell and serves on the Dell Information Quality Governance Office and the Dell IT Technology Advisory Board, developing Dell's corporate strategies for the Business Data Lake, Advanced Analytics, and Information Asset Management. Stephen also serves as a Customer Insight Analyst for the Chief Technology Office, analyzing customer technology challenges and requirements. Stephen has been awarded the People's Choice Award by the Dell Total Customer Experience Team for the Data Governance and Business Data Lake project, as well as a Chief Technology Officer Innovation finalist for utilizing advanced analytics for customer configurations improving product development and product test coverage. Prior to Stephen's current role, he managed Dell's Global Product Development Lab Operations team developing internal cloud orchestration and automation environments, an Information Systems Executive for IBM leading acquisition conversion efforts, and was VP of Enterprise Systems and Operations managing mission-critical Information Systems for Telelogic (a Swedish public software firm). Stephen has an MBA from Southern New Hampshire University, a BSBA, and an AS in Finance from Northeastern University. About #Podcast: #FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey to create the data-driven future. Wanna Join? If you or any you know wants to join in, Register your interest @ http://play.analyticsweek.com/guest/ Want to sponsor? Email us @ info@analyticsweek.com Keywords: #FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy 0 Comments

In this podcast, Bill Schmarzo talks about the ingredients of successful data science practice, team, and executives. Bill shared his insights on what some leaders in the industries are doing and some challenges seen in the successful deployment. Bill shared his key take on ingredients for some of the successful hires. This podcast is great for growth mindset executives willing to learn about creating a successful data science practice. Timeline: 0:29 Bill's journey. 5:05:00 Bill's current role. 7:04 Data science adoption challenges for businesses. 9:33 The good side of data science adoption. 11:22 How is data science changing business. 14:34 Strategies behind distributed IT. 18:35 Analysing the current amount of data. 21:50 Who should own the idea of data science? 24:34 The right background for a CDO. 25:52 Bias in IT. 29:35 Hacks to keep yourself bias-free. 31:58 Team vs. tool for putting together a good data-driven practice. 34:54 Value cycle in data science. 37:10 Maturity model. 39:17 Convincing culture heavy businesses to adopt data. 42:47 Keeping oneself sane during the technological disruption. 46:20 Hiring the right talent. 51:46 Ingredients of a good data science hire. 56:00 Bill's success mantra. 59:07 Bill's favorite reads. 1:00:36 Closing remarks. Bill's Recommended Read: Moneyball: The Art of Winning an Unfair Game by Michael Lewis http://amzn.to/2FqBFg8 Big Data MBA: Driving Business Strategies with Data Science by Bill Schmarzo http://amzn.to/2tlZAvP Podcast Link: https://futureofdata.org/schmarzo-dellemc-on-ingredients-of-healthy-datascience-practice-futureofdata-podcast/ Bill's BIO: Bill Schmarzo is the CTO for the Big Data Practice, where he is responsible for working with organizations to help them identify where and how to start their big data journeys. He's written several white papers, is an avid blogger, and is a frequent speaker on the use of Big Data and data science to power the organization's key business initiatives. He is a University of San Francisco School of Management Fellow, where he teaches the "Big Data MBA" course. Bill has over three decades of experience in data warehousing, BI, and analytics. Bill authored EMC's Vision Workshop methodology that links an organization's strategic business initiatives with their supporting data and analytic requirements and co-authored with Ralph Kimball a series of articles on analytic applications. Bill has served on The Data Warehouse Institute's faculty as the head of the analytic applications curriculum. Bill holds a master's degree in Business Administration from the University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science, and Business Administration from Coe College. About #Podcast: #FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey to create the data-driven future. Wanna Join? If you or any you know wants to join in, Register your interest @ http://play.analyticsweek.com/guest/ Want to sponsor? Email us @ info@analyticsweek.com Keywords: #FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

Thomas and Cory talk with special guest Bill Schmarzo, the “Dean of Big Data” about the organization challenges and opportunities in monetizing data assets.  Bill is the author of Big Data: Understanding How Data Powers Big Business and Big Data MBA: Driving Business Strategies with Data Science, is responsible for setting strategy and defining the Big Data service offerings for Dell EMC’s Big Data Practice. As a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide. Show Notes: http://bit.ly/BDB_EP13

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting strategy and defining the Big Data service offerings for Dell EMC’s Big Data Practice. As a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Big Data is a term. The adjective ‘big’ has no meaning. Most companies are interested in looking at the ‘boat load of data’ they have but are not sure what to do with it. Right now, companies are only looking at the data to see ‘what happened’. “The biggest challenge from IT side and business side is to understand how they can understand data to effectively power their business model.” Dell is using data to do predictive maintenance on their equipment. The goal is to fix devices before they break. They do this with employees and health care. “We try to drink our own champagne – use data internally, so we can be credible in the marketplace.” Why have data if you aren’t going to use it? “Data by itself is a glob of nothing. You need to have an analytic strategy to tell what data is needed.” Organizations need to know what problems they are trying to accomplish then can make analytics on those. If you know the problem to solve, you know the analytics and data you need. Then it becomes easy. Ask the questions first. Business has to drive IT. Data is a business conversation about economics. Then you can exploit the use of data. There is a new position, the Chief Data Officer. It’s a good idea, but there has been poor execution. What has been happening is taking a CIO and giving them a new title of CDO. However, it should be the Chief Data Monetization Officer. The job is to determine how to monetize the data you have available. This should be an economics person rather than IT person. Schmarzo’s advice for people who are thinking about big data? Business people: Read his book written for business people. Also, check out his blog as he frequently blogs about big data. He recently wrote about how to become intelligent like Netflix. Everyday people: You need to understand the basics. Start reading, attending the free online classes, read blogs. Begin to understand what is machine learning and AI is all about. Don’t be afraid; just spend 15 minutes a day to become more familiar. What you will learn in this episode: ● Why the term Big Data is a misnomer ● How Dell is using data ● The ‘mindset’ of data ● Why big data is about economics, not technology ● How much of a CIO’s background should be in technology vs. business and economics ● What role data plays in AI, wearables and machine learning   Links from the episode: ● Blogs: infocus.emc.com/author/william_schmarzo/ (Blog) ● LinkedIn: linkedin.com/in/schmarzo

Bill Schmarzo is the CTO of EMC's Big Data Consulting Practice and a frequent industry speaker, a blogger, a professor at the University of San Francisco School of Management as well as an author. Schmarzo wrote "Big Data MBA: Driving Business Strategies with Data Science" and "Big Data: Understanding How Data Powers Big Business". Show notes at http://hellotechpros.com/bill-schmarzo-people/ Key Takeaways The companies who are successful with Big Data addressed the cultural or people issues. How do we get people engaged in the process so that we're delivering the analytics in a way that is actionable to our stakeholders? Many companies tend to start with implementing Hadoop and then waiting for magic to happen. It doesn't happen. Data science is really identifying the variables and metrics that MIGHT be a better predictor of performance. "Might" is a license to be wrong and in many companies the idea of wrong is bad. In the BI space, the IT departments over promised and under deliveredwhich has lead business leaders to be skeptical of Big Data initiatives. You have to have a tight alignment between the business and IT to ensure that we are working on the right problems. Pick a topic / problem that business users find is important, focus on strategic business initiatives. Create a link between business and data scientists. Create a culture of creativity across the whole organization. Creativity is a contagious event. All ideas are worthy of consideration. We want people to be unafraid to be creative and sharing their ideas even if they may not work. The best ideas come from front line. Most large organizations struggle with protecting their own fiefdoms which leads to data silos. Big Data is not about big, its about small. Learn as much as possible about individual person, event and situation instead of looking at the average of the data. Aggregated data is the devil. You need to work with raw to serve the individual. Big Data initiatives tend to fail because the teams are focused on too many opportunities instead of being focused and priortizing the most important business problems. Focus on one decision which has high value and high feasibility. Big Data is a team sport. We need everyone involved, including the business, the IT and the data scientists. Success breeds success. Quick wins will catch the attention of other people and build a grassroots movement.  

InformationWeek Senior Editor Jessica Davis talks with Bill Schmarzo, CTO of Big Data Services at EMC, about the impact of big data and big analytics on your IT infrastructure.

The Hot Aisle is hosted by Brent Piatti (@brentpiatti) and Brian Carpenter (@intheDC).  Joining us this Episode is Bill Schmarzo (@schmarzo) the anointed #DeanofBigData, author of Big Data Understanding How Data Powers Big Business, Current EMC Big Data CTO, former VP of Analytics for Yahoo & Business Objects.  Of course, he also somehow finds time to teach the […]