Podcasts about cdos

Welcome to The Prompt, a short-format minisode of How I Met Your Data, where hosts Junaid, Karen, and Anjali delve into the evolving landscape of data and AI. In this lively discussion, they explore the pivotal question of whether the Chief Data Officer (CDO) or Chief Data & AI Officer (CDAO) should report directly to a CEO, a CIO, or another C-level executive. Each host shares sharp insights based on their professional experience, addressing the challenges facing CDOs, such as their typically short tenures and the essential components required for their success. Throughout the episode, the trio touches on the significance of building a data-driven culture, assessing whether data should be seen as a cost center or a valuable asset, and the complexities of integrating data literacy within corporate strategy. They also tackle the important consideration of hiring CDOs from within the organization versus bringing in external change agents, weighing the benefits and drawbacks of each approach. Join them in this engaging conversation that challenges conventional wisdom and highlights the critical role data plays in defining today's business landscape.

Back in 2016, the Internet and Mobile Association of India set up an all new club for what was then a very small cohort of digital leaders in corporate India. It was called the all-India Chief Digital Officer club. Back then, there were only about five-six CDOs that were members. The point of the initiative was to give legitimacy to this new, emerging role. But soon enough, the initiative fizzled out. Not because the role didn't take off or anything. Actually, the opposite. The initiative became redundant because the role became even more popular than they had anticipated. So it started with 5-6 members, but within the next four years its membership rose to 50 and then doubled the next year. You see, digital transformation has become THE buzzword for corporate India. And in the process, the CDO has become part of the companies top leadership. But the question is — where does that leave the CIO? Tune in. *This episode was originally published on 18 December, 2024 P.S The Ken's podcast team is hiring! Here's what we're looking for.Daybreak is produced from the newsroom of The Ken, India's first subscriber-only business news platform. Subscribe for more exclusive, deeply-reported, and analytical business stories.Listen to the latest episode of Two by Two here

This blog offers five guiding principles to help CIOs, CDOs, and team leaders optimize hybrid data environments. Published at: https://www.eckerson.com/articles/balancing-act-five-principles-to-optimize-hybrid-cloud-environments

Episode OverviewThe expanding data landscape is increasingly complex, making the role of the Data Architect more critical than ever before. On this week's episode of the CDO Matters Podcast, Pete Cooney, the Lead Enterprise Architect with Jackson, shares his wealth of experience on how CDOs can leverage their data architecture to drive maximum value for their organizations.Episode Links and ResourcesFollow Malcolm Hawker on LinkedInFollow Pete Cooney on LinkedIn

In Episode 18, of Season 5 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Rob Kent, Data & AI Leader at Greggs, where they discuss the learnings from reporting to a CEO and being on the board of a UK Plc, which includes;Starting a career in IT over 30 years ago.Building D&A functions from scratch on multiple occasions.How the common thread has been executive sponsorship at the highest level.Anchoring data strategies to business strategies.Being part of a small group of CDOs that have reported to a CEO.Sitting on the board of a UK PLC that publicly declared data as core to its strategy.The business drivers that allowed data to have a seat at a board table.The learnings and experience of reporting to a CEO.Growing an appreciation of competing with priorities of other functions.The importance of empathy when sitting on the board.Balancing wearing a corporate hat and a data hat.Still needing to justify the existence of the data analytics function.A simple three-tier model to calculate value-creation.Accepting that there is a cost of doing business.Delivering tangible benefit in a cost-effective way without a large budget.Whether reporting lines and titles actually matter.The things that the CEO will be thinking about and asking.The things that can influence the position of the data executive in an organisation.The two key questions to ask your CEO that dictate proximity to them.Why CDOs have the power to choose which organisation is right for them.Why the data industry should be pushing on open doors.Why there is often a gap in expectations.The contributing factors to being seen as a cost centre.Why attributing value is easier for B2C businesses than B2B businesses.Why you're doing something wrong if you can't attribute value to data initiatives.Why translation will take you further than most other things.Why business people don't care about our data analytics world.The material differences between having a seat at the table versus being a layer removed.Why any decision is made based on facts, fear, or faith.Where the future of data analytics teams lies.Why we're not being picked on and other functions have it worse than us.Thanks to our sponsor, Data Literacy Academy.Data Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity.If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data literacy first.At Data Literacy Academy, we don't just teach data skills. We empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. We're bridging the gap between business and data teams, so they can all work towards aligned outcomes.From those taking their first steps in data literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that we offer. We embed a deep data culture shift through a transformative change management programme.We take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve.Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at

At the DataDriven Conference, I had a chat with Anshuman Kanwar, EVP of Product, Technology, and Strategy at Reltio. We discussed the evolving priorities for Chief Data Officers and how AI is reshaping enterprise data strategies.We explored:✅ Why customer experience is becoming a top priority for CDOs✅ The biggest data challenges holding back AI adoption✅ How data teams can prove ROI in innovative ways✅ The role of agentic AI in transforming data management✅ How Reltio's multi-modal trusted profiles go beyond traditional MDMSome fascinating insights on where data & AI are headed!What do you think will be the biggest challenge for CDOs in the next five years?#data #ai #reltio #datadriven #theravitshow

Chief Digital Officers (CDOs) play a pivotal role in guiding organizations through digital transformation, aligning technology with business strategies to enhance competitiveness. As companies increasingly recognize the importance of digital leadership, women are making significant strides in CDO positions. Recent data indicates that women now represent approximately 20% of CDO roles, reflecting a positive trend towards gender diversity in technology leadership. This shift not only promotes inclusivity but also leverages diverse perspectives essential for driving innovation and success in the digital age.In this exclusive interview with Shannon Bell, executive vice president and chief digital officer at OpenText, we look at the growing importance of CDOs in the digital transformation of businesses and industries, as well as the role women play in the evolution of the role.1.       The future of many businesses today is defined by the extent to which they embrace digital. In your view why is this so? Why are organisations being driven to become more digital?2.       In this digital-first society, what are the qualifications and qualities that make for a successful chief digital officer (CDO)? Do you see an overlap between the roles of a CDO to that of a Chief Transformation Officer? How about Chief Technology Officer?3.       In your view, are women better suited to be CDOs than their male counterparts? [Please elaborate]4.       What systemic barriers do women face in advancing to CDO positions within Asian companies?a.       What role does corporate culture play in either facilitating or hindering women's progress to CDO roles?5.       How can women leverage their backgrounds in marketing and customer engagement to transition into CDO positions?6.       How important are female role models in the tech space for aspiring women leaders?7.       What is your advice for women aspiring to become leaders? Likewise, what is your advice for male leaders in support of future women leaders?

What if you could redefine enterprise data management with AI—while boosting productivity by 30%? In this episode of Bringing Data and AI to Life, host Amy Horowitz sits down with Gaurav Pathak, VP of Product Management for AI and Metadata at Informatica, to explore how AI is turning data from a manual headache into an enterprise-wide superpower. As a DataIQ 2025 Data and AI Leader of the Year nominee, Gaurav delivers actionable strategies to help CDOs, CIOs, and data architects scale operations, enhance governance, and harness AI-driven automation. Tune in now to embrace innovation—without sacrificing security or trust.

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.

What happens when an industry as heavily regulated and historically slow-moving as pharma is forced to accelerate digital transformation? In today's episode, I welcome Florian Schnappauf, Vice President of Enterprise Commercial Strategy at Veeva Systems, to discuss how Chief Digital Officers (CDOs) are reshaping the pharmaceutical landscape and why their role is now more critical than ever. The pharmaceutical sector faces mounting pressure to innovate faster, manage costs, and compete with digital-first biotechs. Research predicts the industry will spend $4.5 billion on digital transformation by 2030, a shift that has led to the emergence of CDOs in the pharma C-suite. But what does this role actually entail, and how does it help companies navigate the complexities of drug development, clinical trials, and commercialization? Florian shares insights on how CDOs are not just supporting digital initiatives but actively orchestrating, building, and operating them. From managing the sheer volume of data generated by clinical trials to ensuring that digital tools enhance—not hinder—the drug development process, the CDO is now a key differentiator between industry leaders and laggards. We also explore how effective digital leadership can shorten timeframes from drug discovery to patient treatment, improve communication with healthcare providers, and ultimately ensure that pharma companies achieve more with fewer resources. With regulatory hurdles, technological advancements, and shifting market dynamics, the pharmaceutical industry is at a pivotal moment. So, what does the future hold for digital leaders in pharma? How will CDOs continue to evolve, and what lessons can other industries learn from their journey? Join us as we break down the digital transformation of pharma and the leadership required to drive meaningful change. And as always, I'd love to hear your thoughts—do you think pharma is adapting quickly enough, or is there still a long way to go? Check out the What Pharma Needs Next podcast.  

In Episode 9, of Season 5 of Driven by Data: The Podcast. Kyle Winterbottom is joined by Paul Hollands, Chief Data & Analytics Officer at AXA where they talk about value as the North Star, in which they discuss:How structuring the data agenda can drive impact as the first CDAO at the UK & Ireland level. How a clear North Star helps AXA UK & Ireland focus on the commercial impact of data and AI. Building strong foundations to execute data and AI use cases effectively. Balancing central coordination and business unit autonomy with a federated operating model. How business sponsorship from engaged CEOs supports the data mission. The value of having a team fully aligned behind a clear vision. Using a strong North Star to simplify complex processes and stay on track. How understanding the business environment helps measure data leaders' performance. Tackling the irony of CDOs being expected to excel at measuring value but lacking clarity on their metrics. Why being business-focused in theory doesn't always translate to practice. The difficulty of measuring the value of certain data processes. How stakeholder management and strong relationships drive data success. Encouraging CDOs to share more stories about their impact. Building knowledge-sharing communities to promote value-driven data practices. How GenAI is being industrialised and trust is being built around its use. Exploring how AXA and its staff are using GenAI in practical ways. Adapting data strategies over time as GenAI shapes the direction of data and AI. Avoiding distractions from shiny new trends in data and analytics by staying focused on long-term value. Thanks to our sponsor, Data Literacy Academy.Data Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity.If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data literacy first.At Data Literacy Academy, we don't just teach data skills. We empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. We're bridging the gap between business and data teams, so they can all work towards aligned outcomes.From those taking their first steps in data literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that we offer. We embed a deep data culture shift through a transformative change management programme.We take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve.Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com.

In Episode 8, of Season 5 of Driven by Data: The Podcast. Kyle Winterbottom is joined by Rosh Awatar, Managing Director at Sky, and they discuss the importance of getting data's seat at the table, which they discuss:The challenge of getting data leaders involved in key business decisionsWhy data leaders need to steer the business, not just support itHow the right sponsorship can create business valueThe importance of networking and building connections across all levelsBuilding strong relationships with the businessThe difference between what Data & Analytics teams see as “value” versus what business leaders see as “value”Adapting your message depending on the data capability you're focusing onAligning data priorities with business goals and strategiesBeing involved in the right conversations to make better decisionsBalancing commercial success with long-term benefitsThe view some companies took on CDO roles in 2024 and how CDOs can show their valueDelivering value through strong data governance and enablementDeciding whether a centralised or decentralised/federated data model works best for your company's cultureThe collaborative and innovative environment at SkyWhy it's important to ask for help when neededHow culture and purpose shape a company's view of dataStriking the right balance when talking about the value of dataThanks to our sponsor, Data Literacy Academy.Data Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity.If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data literacy first.At Data Literacy Academy, we don't just teach data skills. We empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. We're bridging the gap between business and data teams, so they can all work towards aligned outcomes.From those taking their first steps in data literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that we offer. We embed a deep data culture shift through a transformative change management programme.We take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve.Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com.

"KI und Führung – Strategien für die neue Arbeitswelt“ mit Marc Hammer   Das KI-Summit Germany 2025 bietet am 31. Januar und 1. Februar die perfekte Gelegenheit, neue Impulse und wertvolle Kontakte zu gewinnen. Im inspirierenden Ambiente des Güterbahnhofs in Bad Homburg treffen Vordenker und Visionäre aufeinander, um die Zukunft der KI gemeinsam zu gestalten. Freu Dich auf ein abwechslungsreiches Programm mit spannenden Keynotes, praxisorientierten Workshops und einzigartigen Networking-Möglichkeiten. Sichere Dir jetzt Dein Ticket und werde Teil dieses richtungsweisenden Events! Marc Hammer ist bei Division One Executive Search tätig und spezialisiert auf die Besetzung von Führungspositionen im Kontext der digitalen Transformation. Sein Fokus liegt auf der Vermittlung von C-Level-Führungskräften wie CIOs und CDOs, insbesondere in Bereichen, die von KI beeinflusst werden. Sein Ziel ist es, Unternehmen bei der Neudefinition von Führungsrollen in einer KI-getriebenen Welt zu unterstützen. Marc Hammer auf LinkedIn: LinkedIn - https://www.linkedin.com/in/marc-hammer/ KI-Summit Germany 2025: Hier anmelden - www.ki-summit-germany.de/   Was sind die Kerninhalte seines Impulsvortrags? Der Vortrag trägt den Titel: „Künstliche Intelligenz – Ein Einblick in die neue Arbeitswelt und Strategien für Führungskräfte“. Marc Hammer beleuchtet die Veränderungen, die KI auf Arbeitsweisen und Führungsstrukturen in Unternehmen ausübt. Die zentralen Punkte sind:   Wie sich die Arbeitswelt durch KI wandelt und welche neuen Anforderungen an Führungskräfte entstehen. Die aktive Rolle von Führungskräften bei der Integration von KI in Unternehmen. Methoden zur Umgestaltung von Jobprofilen und Arbeitsprozessen im Zeitalter der KI. Praktische Ansätze, wie Führungskräfte ihre Rolle in der KI-getriebenen Transformation definieren und gestalten können.   Was ist das Besondere an seinem Impulsvortrag? Marc hebt hervor, dass sein Vortrag kein rein technischer Blick auf KI ist, sondern einen organisatorischen und leadership-orientierten Ansatz verfolgt. Besonders macht ihn:   1. Praktikabilität: Führungskräfte erhalten nicht nur theoretische Erkenntnisse, sondern konkrete, anwendbare Methoden und Werkzeuge. 2. Reflexion: Er fordert die Teilnehmer aktiv heraus, ihre eigene Rolle als Führungskraft zu überdenken und zu definieren. 3. Langfristige Wirkung: Der Vortrag soll Impulse setzen, die über den Summit hinaus nachwirken, indem sie Diskussionen und Veränderungen in Unternehmen anstoßen.   Welche Kernbotschaft lässt sich aus dem Vortrag ableiten? 1. Reflektiere deine Rolle als Führungskraft: Führungskräfte müssen ihre Position in einer KI-getriebenen Arbeitswelt aktiv gestalten. Sie sollten nicht nur die Skepsis ihrer Teams adressieren, sondern auch ihre eigene Haltung zu KI hinterfragen. Wer sich nicht proaktiv mit KI auseinandersetzt, riskiert, von der Technologie und den Marktanforderungen überholt zu werden. 2. Gestalte aktiv die Jobprofile deiner Mitarbeitenden: Führungskräfte sollten frühzeitig analysieren, wie sich die Aufgaben und Rollen ihrer Teams durch KI verändern. Dies erfordert ein aktives Management von Veränderungsprozessen, das Mitarbeitende einbezieht und sie für neue Aufgaben und Chancen befähigt.     Noch mehr von den Koertings ...  Das KI-Café ... jede Woche Mittwoch (>300 Teilnehmer) von 08:30 bis 10:00 Uhr ... online via Zoom .. kostenlos und nicht umsonstJede Woche Mittwoch um 08:30 Uhr öffnet das KI-Café seine Online-Pforten ... wir lösen KI-Anwendungsfälle live auf der Bühne ... moderieren Expertenpanel zu speziellen Themen (bspw. KI im Recruiting ... KI in der Qualitätssicherung ... KI im Projektmanagement ... und vieles mehr) ... ordnen die neuen Entwicklungen in der KI-Welt ein und geben einen Ausblick ... und laden Experten ein für spezielle Themen ... und gehen auch mal in die Tiefe und durchdringen bestimmte Bereiche ganz konkret ... alles für dein Weiterkommen. Melde dich kostenfrei an ... www.koerting-institute.com/ki-cafe/   Das KI-Buch ... für Selbstständige und Unternehmer Lerne, wie ChatGPT deine Produktivität steigert, Zeit spart und Umsätze maximiert. Enthält praxisnahe Beispiele für Buchvermarktung, Text- und Datenanalysen sowie 30 konkrete Anwendungsfälle. Entwickle eigene Prompts, verbessere Marketing & Vertrieb und entlaste dich von Routineaufgaben. Geschrieben von Torsten & Birgit Koerting, Vorreitern im KI-Bereich, die Unternehmer bei der Transformation unterstützen. Das Buch ist ein Geschenk, nur Versandkosten von 6,95 € fallen an. Perfekt für Anfänger und Fortgeschrittene, die mit KI ihr Potenzial ausschöpfen möchten. Das Buch in deinen Briefkasten ... www.koerting-institute.com/ki-buch/   Die KI-Lounge ... unsere Community für den Einstieg in die KI (>800 Mitglieder) Die KI-Lounge ist eine Community für alle, die mehr über generative KI erfahren und anwenden möchten. Mitglieder erhalten exklusive monatliche KI-Updates, Experten-Interviews, Vorträge des KI-Speaker-Slams, KI-Café-Aufzeichnungen und einen 3-stündigen ChatGPT-Kurs. Tausche dich mit über 900 KI-Enthusiasten aus, stelle Fragen und starte durch. Initiiert von Torsten & Birgit Koerting, bietet die KI-Lounge Orientierung und Inspiration für den Einstieg in die KI-Revolution. Hier findet der Austausch statt ... www.koerting-institute.com/ki-lounge/   Starte mit uns in die 1:1 Zusammenarbeit Wenn du direkt mit uns arbeiten und KI in deinem Business integrieren möchtest, buche dir einen Termin für ein persönliches Gespräch. Gemeinsam finden wir Antworten auf deine Fragen und finden heraus, wie wir dich unterstützen können. Klicke hier, um einen Termin zu buchen und deine Fragen zu klären. Buche dir jetzt deinen Termin mit uns ... www.koerting-institute.com/termin/   Weitere Impulse im Netflix Stil ... Wenn du auf der Suche nach weiteren spannenden Impulsen für deine Selbstständigkeit bist, dann gehe jetzt auf unsere Impulseseite und lass die zahlreichen spannenden Impulse auf dich wirken. Inspiration pur ... www.koerting-institute.com/impulse/   Die Koertings auf die Ohren ... Wenn dir diese Podcastfolge gefallen hat, dann höre dir jetzt noch weitere informative und spannende Folgen an ... über 370 Folgen findest du hier ... www.koerting-institute.com/podcast/   Wir freuen uns darauf, dich auf deinem Weg zu begleiten!

Send us a textContent is everywhere, but managing it has never been harder. Did you know that 30% of Fortune 500 companies rely on Contentful for their digital experiences, or that Gen Z prefers platforms like TikTok for shopping over traditional e-commerce sites? As AI, personalisation, and omnichannel strategies evolve, businesses must rethink how they create, manage, and deliver content to meet ever-changing demands.Welcome back to another episode of Marketing in the Madness. This week, our host Katie Street speaks with Thomas Clayson, Head of EMEA Solution Engineering at Contentful, and Simon Ward, Senior Experience and Design Consultant at VML, to explore how businesses can manage the content chaos in today's fast-paced digital landscape.If like many of us, you're feeling overwhelmed by content chaos, this episode will provide actionable strategies to streamline workflows, enhance personalisation, and prepare you for the future of content management at scale.Here's a glimpse of what you'll discover in this week's episode:

Episode OverviewData leaders have a massive opportunity to drive transformational value with AI, but many are running on outdated operating models. On this episode of the CDO Matters Podcast, Katharine Shaw Paffett, the Cross Solution AI lead for UK and Ireland at Avanade, shares insights on how CDOs can re-envision their organizations to be more AI-ready. Katharine is at the forefront of the early adoption of GenAI for many large organizations, and her guidance for any company seeking to implement AI is not to be missed.Episode Links and ResourcesFollow Malcolm Hawker on LinkedInFollow Katharine Shaw Paffett on LinkedIn

In this episode, host Jason Foster sits down with Barry Panayi, Chief Data and Insight Officer at John Lewis Partnership to discuss the evolving role of the Chief Data Officer (CDO). Barry shares his journey from coding and analytics to leading data and insights at iconic brands like John Lewis and Waitrose. He offers a unique perspective on how CDOs can transition from technical experts to strategic business leaders. Barry's candid reflections and actionable advice make this episode essential listening for data professionals, aspiring CDOs, and anyone interested in the intersection of data, technology, and business leadership. Don't miss this engaging and insightful conversation!    ***********      Cynozure is a leading data, analytics and AI company that helps organisations to reach their data potential. It works with clients on data and AI strategy, data management, data architecture and engineering, analytics and AI, data culture and literacy, and data leadership. The company was named one of The Sunday Times' fastest-growing private companies in both 2022 and 2023, and recognised as The Best Place to Work in Data by DataIQ in 2023 and 2024.  

Tom Bodrovics welcomes back long-term contrarian investor and entrepreneur Simon Mikhailovich for a discussion centered around first principles, focusing on precious metals, commodities, economics, geopolitics, trade, and monetary matters. The conversation begins with the acknowledgement of high levels of uncertainty and complexity, making accurate forecasts challenging. Mikhailovich distinguishes between speculating on precious metals versus using them as a reserve asset. For speculation, market drivers are pertinent. However, for gold as a reserve asset, its unique property as the only financial asset without a counterparty makes it inversely correlated to confidence and trust in other people's promises. The conversation touches upon the concept of the fourth turning and where we are in this cycle. Mikhailovich underscores the significance of understanding current problems before predicting future demand for gold. He also discusses how post-World War II arrangements have led to the United States' hegemonic role economically and militarily, and the start of financialization and globalization. Mikhailovich raises concerns about understated inflation and its potential impact on real economic growth or contraction. He also highlights the lack of clear guidance from Federal Reserve Chairman Jay Powell in navigating through uncertain conditions. They explore the winners and losers of the global economy, with tactical gains for Wall Street investors, technology industries, and certain countries like China. However, working people have been losing due to job outsourcing. Mikhailovich mentions China's growing power and desire for independence from the United States as potential challenges to the current economic order. The conversation delves into geopolitical tensions in the Middle East, with borders becoming less inviolable after World War One and World War Two. The Suez Canal's declining traffic and resulting increased costs serve as an example of inflationary pressures. Mikhailovich discusses the significance of gold as a financial asset and its increasing demand, particularly from China and other countries, as a response to a loss of confidence in the global financial system. He also mentions the relationship between digital currencies like Bitcoin and the US dollar, suggesting that regulatory actions could impact their independence from the dollar and the broader financial system. Lastly, Simon emphasizes understanding the complexities, considering various data points, focusing on resiliency, and looking at first principles. Time Stamp References:0:00 - Introduction0:44 - Uncertainties & Metals4:22 - The Fourth Turning9:00 - Statistics & Reality17:00 - Wars, Rumors & Borders26:47 - Economic Fragility33:55 - Gold & Eastern Buying38:30 - Trump & U.S. Dollar41:18 - Gold & Confidence50:07 - Trump & Bond Markets53:56 - World Has Changed1:03:02 - Inflation Vs. Panic1:05:20 - Socialism & Competence1:10:02 - A Serious Situation1:13:13 - Wrap Up Talking Points From This Episode Gold as a reserve asset is inversely correlated to confidence in other people's promises. Understanding current problems before predicting future demand for gold is crucial. Concerns about understated inflation, lack of clear guidance from Jay Powell, and China's growing power pose challenges. Guest Links:Twitter: https://c.com/S_MikhailovichWebsite: https://www.bullionreserve.com Simon A. Mikhailovich is a co-founder, lead manager of The Bullion Reserve, and a director. Mr. Mikhailovich is an entrepreneur and contrarian investor who predicted and profited from the financial crises of 2000 and 2008. Before co-founding TBR in 2014, Mr. Mikhailovich co-founded Eidesis Capital, a special situations investment firm. Between 1998 and 2014, the Eidesis team deployed over $2.5B of capital through special opportunity funds focused on high yield corporate bonds and loans, credit derivatives, distressed CDOs and MBS, and gold.

Back in 2016, the Internet and Mobile Association of India set up an all new club for what was then a very small cohort of digital leaders in corporate India. It was called the all-India Chief Digital Officer club. Back then, there were only about five-six CDOs that were members. The point of the initiative was to give legitimacy to this new, emerging role. But soon enough, the initiative fizzled out. Not because the role didn't take off or anything. Actually, the opposite. The initiative became redundant because the role became even more popular than they had anticipated. So it started with 5-6 members, but within the next four years its membership rose to 50 and then doubled the next year. You see, digital transformation has become THE buzzword for corporate India. And in the process, the CDO has become part of the companies top leadership. But the question is — where does that leave the CIO? Tune in. P.S The Ken's podcast team is hiring! Here's what we're looking for.Daybreak is produced from the newsroom of The Ken, India's first subscriber-only business news platform. Subscribe for more exclusive, deeply-reported, and analytical business stories.Listen to the latest episode of Two by Two here

– What is a ‘custody' fee? – Keeping track of Bitcoin CGT? – Can Bitcoin become the global reserve currency? – Isn't an ETF just a fancy CDO?  See omnystudio.com/listener for privacy information.

Let us know your thoughts. Send us a Text Message. Follow me to see #HeadsTalk Podcast Audiograms every Monday on LinkedInEpisode Title:

P.M. Edition for Oct. 23. Matt Wirz, who writes about credit for The Wall Street Journal talks about why Wall Street is excited about NAVs, SRTs and CDOs. And U.S. home sales hit another nearly 30-year low. Journal housing reporter Nicole Friedman explains why new buyers are staying on the housing market sidelines. Plus, with deadlocked polls and the memory of 2016, White House reporter Tarini Parti says Democrats are becoming more anxious ahead of Election Day. Tracie Hunte hosts. Sign up for the WSJ's free What's News newsletter. Learn more about your ad choices. Visit megaphone.fm/adchoices

Episode OverviewThere are many companies just now evaluating moving all or a portion of their mission-critical data or infrastructures to the cloud. In this episode of the CDO Matters Podcast, cloud computing expert Ayman Husain shares his insights on the key considerations all CDOs must make when considering the role that cloud-based platforms will in their data architectures. From cost drivers to cloud migration plans and everything in-between, Ayman shares a wealth of insights from years of experience in helping companies move their data to the cloud. Episode Links and ResourcesFollow Malcolm Hawker on LinkedInFollow Ayman Husain on Linkedin

Highlights from this week's conversation include:Previewing the Next Cynical Data Guy Episode (0:13)Story Time: Coolest Data Project You've Worked On (1:13)Failed Web Scraping Project (3:40)Building a Neural Net for Matching (5:22)Rebuilding the Project Strategy (7:04)Project Completion and Politics (9:35)Agreeable Data Guy's Pricing Story (11:00)Balancing Advanced and Simple Solutions (14:15)Insights from Pricing Team Meetings (16:19)Building for Scale vs. Immediate Needs (18:29)Open Source Data Formats (19:46)Disaster Recovery Experiences (22:34)Reflections on Chief Data Officers (25:01)Cynicism in Data Projects (28:19)Final Thoughts and Takeaways (30:20)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

In this episode, host Jason Foster sits down with Steven Pimblett, CDO at Rightmove, to discuss how data and AI can be leveraged as an asset to create value in a company. They explore the different approaches that CDOs take in implementing new data practices into an organisation, as well as the process of creating and demonstrating data value.  Additionally, they examine the shifts in marketing efficiency and data monetisation that have resulted from increased digitisation. ********** Cynozure is a leading data, analytics and AI company that helps organisations to reach their data potential. It works with clients on data and AI strategy, data management, data architecture and engineering, analytics and AI, data culture and literacy, and data leadership. The company was named one of The Sunday Times' fastest-growing private companies in both 2022 and 2023, and recognised as The Best Place to Work in Data by DataIQ in 2023.

This interview with Brett Jefferson discusses trust preferred CDOs, why they can provide such great opportunities, and how Brett's fund capitalizes on this niche market. Brett and Jack also discuss the private credit market, the risks and benefits of securitization, the performance of the collateralized loan market, and much more. Recorded on September 10, 2024. Follow Jack Farley on Twitter https://twitter.com/JackFarley96 Follow Forward Guidance on Twitter https://twitter.com/ForwardGuidance Follow Blockworks on Twitter https://twitter.com/Blockworks_ Follow the new host of Forward Guidance, Felix Jauvin https://x.com/fejau_inc Jack's upcoming podcast, Monetary Matters: https://www.youtube.com/@Monetary-Matters __ Timestamps: (00:00) Introduction (00:59) Trust Preferred CDOs (08:44) Starting Hildene Capital Management (10:44) Buying Trust Preferred CDOs (13:22) Deferred Payments (20:13) Trust Preferreds Regulated Out (23:15) Philadelphia Fed Paper (27:38) Taking Advantage Of Inefficient Markets (34:33) Private Credit (37:55) Hildene's Market Niche (44:11) The Structured Credit Market (51:44) Structured Security Correlations (55:20) Credit Market Risks (01:00:00) The Realities Of Private Credit (01:08:27) TruPS CDOs (01:11:03) Private Credit CLO Market (01:14:48) Risks & Rewards (01:18:58) Collateralized Loan Market Performance (01:25:28) Closing __ Disclaimer: Nothing discussed on Forward Guidance should be considered as investment advice. Please always do your own research & speak to a financial advisor before thinking about, thinking about putting your money into these crazy markets.

Earlier this summer, Google announced that its Chrome browser would after all keep third party cookies. This interview with Robin de Wouters is the first of two episodes exploring the consequences of that update from the point of view of our usual stakeholders (DPOs, CMOs, CDOs).  Robin de Wouters is the Director General for the Federation of European Data & Marketing (FEDMA), in Brussels. He has a strong background in communication and public relations across the private, non-profit and institutional spheres. He previously worked in the field of human rights with Euromed Rights, the ONE Campaign and the United Nations. Robin is also the Vice-Chair of the Board of the European Interactive Digital Advertising Alliance (EDAA) and the Communications Director and Spokesperson for Democrats Abroad Belgium, the international arm of the US Democratic Party. References: Federation of European Data and Marketing (FEDMA) Robin de Wouters on LinkedIn Sergio Maldonado, Nobody was ready for the Privacy Sandbox, but deprecating cookie banners is long overdue Google announces they are not deprecating third-party cookies Peter Cradock (Masters of Privacy): Could core advertising components fall under the “strictly necessary” ePrivacy exemption?  CNIL publishes study on alternatives to third-party advertising cookies (Freevacy)  

In this episode, hosts Reed Smith and Chris Boyer explore the "flat pancake" metaphor as it applies to the evolving role of the Chief Digital Officer (CDO) in healthcare and discuss how with conflicting priorities and lacking a clear mandate, CDOs face unique challenges in driving digital transformation within complex healthcare systems. They are joined by Jerry Grady of the Ward Group, who shares his perspective on how health systems are evolving their approach in recruiting digital talent.  Mentions from the Show: Jerry Grady on LinkedIn The Ward Group Reed Smith on LinkedIn Chris Boyer on LinkedIn Chris Boyer website Learn more about your ad choices. Visit megaphone.fm/adchoices

Welcome to the Tearsheet Podcast, where we explore financial services together with an eye on technology, innovation, emerging models, and changing expectations. I'm Tearsheet's editor in chief, Zack Miller In today's episode, we explore what it takes to build world-class data governance in financial services. Our guests are Jay Como, the global head of data governance at T. Rowe Price, and Glenn Kurban, partner at Capco. We talk about how generative AI and new data strategies are transforming finance, sharing insights on the transformation of the Chief Data Officer role. The discussion also focuses on the challenges of large-scale data migrations. Jay Como reflects on the convergence of data and digital roles. He states, "What we've seen is there used to be kind of two shapes of CDOs. There was a chief data officer and there was a chief digital officer. And what I think in the last five years is what we've seen is those roles have really come together." Glenn Kurban adds depth to this perspective, emphasizing the shift towards more proactive data strategies. Glenn says, "You're seeing much more being asked of CDOs in terms of, how are we moving now to an offensive posture around data? That is, how am I going to monetize this data? How can I use it to drive better decisions, reduce costs, and actually outpace our competitors?" As our discussion unfolds, it becomes clear that the financial services industry is at a pivotal moment. AI tools and cloud technologies are reshaping traditional approaches to data governance and migration. The insights shared by Como and Kurban offer a glimpse into the future of data management in finance. AI-driven solutions and strategic data governance converge to create new opportunities and challenges. Here's my conversation with Jay and Glenn.

Follow me @samirkaji for my thoughts on the venture market, with a focus on the continued evolution of the VC landscape.Today we're thrilled to be joined by Glenn Solomon, managing partner at Notable Capital. Along with Granite Asia, Notable Capital was one of two groups to emerge from GGV Capital, which recently split into two groups with Notable based in Silicon Valley, New York, and covering companies in the U. S., Israel, Europe, and Latin America.Glenn brings nearly 30 years of venture experience to the table, and it was great to draw from his insights in investing, building firms, and working with high performing teams. About Glenn Solomon:Glenn Solomon is the Managing Partner at Notable Capital. He focuses on investing in early to growth-stage companies across different sectors, including cloud infrastructure and business applications. He also serves on the boards of several companies, such as HashiCorp, Opendoor.com, and Orca Security.Before joining Notable, Glenn was a General Partner at Partech International from 1997 to 2006, where he worked on technology investments. Earlier in his career, he was an associate at SPO Partners from 1993 to 1995 and started as a financial analyst at Goldman Sachs from 1991 to 1993.Glenn Solomon earned his MBA and BA from Stanford University.In this episode, we discuss:(01:42) Glenn's journey from playing tennis at Stanford to discovering a passion for technology and investing(02:44) A pivotal moment when encountering the internet for the first time, which sparked a deeper interest in technology(04:06) The transition from Partech International to joining Granite Global Ventures in the mid-2000s(05:03) The appeal of GGV's global perspective and innovative approach in venture capital(07:48) The early strategy at GGV, focusing on differentiation in the venture space(09:01) The necessity of adapting to the evolving nature of the industry(10:29) The rebranding to Notable Capital and the strategic decisions following the split from GGV's Asia team(12:39) The guiding principles at Notable Capital, emphasizing the importance of speed and maintaining a sector-focused strategy(15:19) An example of a recent deal showcasing how the firm's flat structure empowers all team members to contribute significantly(17:33) Staying focused on specific sectors and building a strong support platform for portfolio companies(23:25) Engaging with CSOs and CDOs to maintain an edge in cybersecurity and data sectors.(27:00) Discusses the importance of resourcefulness in venture capital and how they assess this quality during interviews.(36:31) Advice on being a successful VC, stressing the critical role of building strong, lasting relationships(39:30) Success in venture capital fundamentally relies on working with exceptional peopleI'd love to know what you took away from this conversation with Glenn. Follow me @SamirKaji and give me your insights and questions with the hashtag #ventureunlocked. If you'd like to be considered as a guest or have someone you'd like to hear from (GP or LP), drop me a direct message on Twitter.Podcast Production support provided by Agent Bee This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit ventureunlocked.substack.com

The Office of the Director of National Intelligence earlier this summer released an information technology roadmap for the intelligence community. The roadmap is meant to provide technological foresight to guide the Intelligence Community to make transformative decisions about the cloud environment, cybersecurity, advanced computing, data analysis, and artificial intelligence among an array of other information technology issues. Dr. Adele Merritt, CIO for the intelligence community, is the official responsible for seeing that intelligence agencies embrace the new roadmap and vision laid out within it. In an interview, Merritt discusses the near-term and long-term goals from the roadmap, her office's priorities, how the intelligence community is thinking about AI adoption, and more. Also: The National Institute of Standards and Technology has officially released three new encryption standards that are designed to fortify cryptographic protections against future cyberattacks by quantum computers. The finalized standards are meant to prepare for a not-so-far-off future where quantum computing capabilities can crack current methods of encryption, jeopardizing crucial and sensitive information held by organizations and governments worldwide. And, nearly two years after launching its bureau chief data officer program, the Department of State is seeing success and aiming to almost quadruple the size of its current cohort, Farakh Khan, director of communications, culture and training at the agency's Center for Analytics, told FedScoop in a recent interview. In total, the department wants 52 bureau CDOs in place, one for each bureau or major offices across State. The Daily Scoop Podcast is available every Monday-Friday afternoon. If you want to hear more of the latest from Washington, subscribe to The Daily Scoop Podcast on on Apple Podcasts, Soundcloud, Spotify and YouTube.

To kick off a new series of special episodes here at How I Hire, we're taking a close look at the chief digital officer role. Throughout this series, we'll hear from different C-level executives about their unique functions, responsibilities, and skill sets in order to learn just what it takes to successfully hire – or be hired as – top tier talent. Jamaliya Cobine is an accomplished chief digital officer and digital marketing expert, having spent the past decade plus helping businesses develop their user experience, e-commerce platforms, and strategic digital initiatives. Jamaliya has an impressive track record, working for major brands like Urban Outfitters, Burton Snowboards, Wüsthof, and Topshop. She is currently the CDO of Omorovicza Cosmetics, where she continues her legacy of implementing consumer-centric business strategy, omni-channel technology, and transformative approaches to digital marketing. With in-depth experience across the apparel, outdoor, beauty, home goods, and fashion industries, Jamaliya is the perfect leader to help us learn everything there is to know about the chief digital officer role.Jamaliya and Roy discuss the key functions of a CDO (and how they vary, depending on business structure), how competitive the market is for hiring CDOs, important trends affecting CDOs' work, and much more.Highlights from our conversation include:The scope and primary accountabilities of the CDO role (2:13)Strategy and goal-setting when starting at a new company (4:44)Challenges and opportunities facing CDOs today (6:30)How company structure affects a CDO's functions and responsibilities (12:03)The structure of Jamaliya's team (13:08)Competencies and capabilities necessary to be a successful CDO (17:15)What boards, investors, or CEOs should be aware of when initiating a CDO search (20:00)Common hurdles or barriers to success faced by incoming CDOs (21:28)Visit HowIHire.com for transcripts and more on this episode.Follow Roy Notowitz and Noto Group Executive Search on LinkedIn for updates and featured career opportunities.Subscribe to How I Hire:AppleSpotifyAmazon

Digital Leadership: Embracing the New Tech AgeThis week, we sit down with Pedro Sousa Cardoso, Chief Digital Officer at Emirates NBD, to explore the state of banking in the UAE. We have an in-depth conversation on the evolving role of CDOs, increasing collaboration between banks and fintechs, and venture building within banks, using Liv Bank as an example.Tune in to discover how Emirates NBD is leading the charge in banking innovation.#CouchonomicsWithArjun #DigitalTransformation #FinTech #BankingInnovation #EmiratesNBD #Banking #BaaS #BeyondBanking #CDO #InnovationOur website

Heidi Lanford connects data to cocktails and campaigns while considering the nature of data disruption, getting from analytics to AI, and using data with confidence.Heidi studied mathematics and statistics and never looked back. Reflecting on analytics then and now, she confirms the appetite for data has never been higher. Yet adoption, momentum and focus remain evergreen barriers. Heidi issues a cocktail party challenge while discussing the core competencies of effective data leaders.Heidi believes data and CDOs are disruptive by nature. But this only matters if your business incentives are properly aligned. She revels in agile experimentation while counseling that speed is not enough. We discuss how good old-fashioned analytics put the right pressure on the foundational data needed for AI. Heidi then campaigns for endemic data literacy. Along the way she pans JIT holiday training and promotes confident decision making as the metric that matters. Never saying never, Heidi celebrates human experts and the spotlight AI is shining on data.Heidi Lanford is a Global Chief Data & Analytics Officer who has served as Chief Data Officer (CDO) at the Fitch Group and VP of Enterprise Data & Analytics at Red Hat (IBM). In 2023, Heidi co-founded two AI startups LiveFire AI and AIQScore. Heidi serves as a Board Member at the University of Virginia School of Data Science, is a Founding Board Member of the Data Leadership Collaborative, and an Advisor to Domino Data Labs and Linea. A transcript of this episode is here.

Chapter 1:Summary of Book The Big Short "The Big Short: Inside the Doomsday Machine" is a non-fiction book by Michael Lewis that was published in 2010. The book chronicles the build-up of the housing and credit bubble during the 2000s and the subsequent financial crisis that ensued. Michael Lewis tells the story through the eyes of several investors who bet against the US mortgage market before the crash.The key characters featured are Steve Eisman, an eccentric hedge fund manager; Michael Burry, a reclusive and socially awkward doctor who turned to investing; Greg Lippmann, a Deutsche Bank trader; and the team from Cornwall Capital, led by Jamie Mai and Charlie Ledley. Each of these investors, through their own research and observations, came to realize that the booming housing market was built on shaky subprime loans, which were likely to fail in large numbers.Michael Lewis explains complex financial instruments like mortgage-backed securities (MBS), collateralized debt obligations (CDOs), and credit default swaps (CDS) in an accessible manner. These instruments played major roles in both the market's expansion and its collapse. The investors' realization that the market was unsustainable led them to "short" the market, essentially betting against the mortgage-backed securities by buying credit default swaps.Through the story of these investors, "The Big Street" details both the greed and corruption in the banking sector that led to the financial crisis, and the lack of understanding and regulation that allowed such a catastrophic collapse. The book is a critical examination of the practices that nearly destroyed the global financial system and a real-life thriller with a detailed look at the financial products and the human elements that drove the market collapse. It also serves as a critique of the Wall Street model, highlighting how the pursuit of short-term profits led to long-term disaster for the global economy.Chapter 2:The Theme of Book The Big Short "The Big Short: Inside the Doomsday Machine" by Michael Lewis, published in 2010, delves into the build-up of the U.S. housing bubble during the 2000s and the eventual financial crisis of 2007-2008. The book focuses particularly on the individuals and small groups who foresaw the collapse and positioned themselves to profit from it, through a financial instrument called the credit default swap, which is essentially a bet against the housing market. Here's an overview of its key plot points, character development, and thematic ideas: Key Plot Points:1. Introduction to the Housing Bubble: The book begins by introducing the U.S. housing bubble, inflated by subprime mortgages and risky lending practices. Financial institutions bundled these risky loans into securities.2. Invention of Credit Default Swaps (CDS): A few outsiders and skeptics notice the unsustainable housing market and the risky nature of the bundled securities. They use credit default swaps to bet against the housing market, essentially insuring the bonds and making a profit when they fail.3. The Investors: The plot traces several key figures, including Steve Eisman, Dr. Michael Burry, Greg Lippmann, and the team from Cornwall Capital, who were among the few to predict and profit from the eventual collapse.4. Realization of the Crisis: As the housing market begins to collapse, these investors face intense scrutiny and pressure, yet their predictions begin to materialize as mortgage defaults skyrocket.5. Outcome: The climax occurs with the full-blown collapse of the housing market, leading to massive financial losses across the globe. The key characters reap...

Episode OverviewEddie Short is an outspoken and experienced CDO who has managed the data and analytics function for some of the biggest companies on the planet – and he's recently returned to graduate school with the goal of quantifying the value of Chief Data Officers.In this engaging conversation, Eddie shares valuable insights from his decades of experience – focusing on a core message that CDOs *must* develop the leadership skills needed to challenge the status quo and quantify the value of the solutions they provide.Episode Links and ResourcesFollow Malcolm Hawker on LinkedInFollow Eddie Short on LinkedIn

That Solo Life, Episode 254:  The Work of Diversity - A Conversation with Anetra Henry   In this Episode   Anetra Henry, Senior Director of Strategic Initiatives at the Institute for Public Relations joins Karen Swim, APR and Michelle Kane on today's episode of “That Solo Life.” Anetra is a passionate storyteller and strategic messaging guru, known for her expertise in research and advocacy for diversity and inclusion. The episode delved into a recent study, led by Anetra, "Collaborators for Change," which focused on the relationship between Chief Communications Officers (CCOs) and Chief Diversity Officers (CDOs) in organizations. Anetra shared the spark that led to the research, highlighting the importance of understanding how these executives work together, especially during times of crisis. Anetra discussed the obstacles faced during the study, including challenges in recruiting CDOs due to external factors like legislation targeting diversity initiatives. She emphasized the need for empathy and understanding in conducting the interviews, as participants shared personal stories and fears about the future of their work. The conversation also touched on key takeaways from the study, such as the importance of clear communication between CCOs and CDOs, the impact of diversity fatigue, and the need for organizations to define and prioritize diversity, equity, and inclusion initiatives. Anetra shared her personal reflections on conducting the study, highlighting the emotional impact of hearing participants' stories and fears. She emphasized the importance of empathy and the need for continued efforts in diversity and inclusion work. Looking ahead, Anetra expressed hope for the future of CDOs, emphasizing the importance of continued progress and rebranding of diversity initiatives. She discussed potential future studies and the need for ongoing support and advocacy for diversity and inclusion in organizations. This episode provided valuable insights into the complex relationship between CCOs and CDOs, highlighting the challenges and opportunities in advancing diversity and inclusion efforts within organizations.  About Anetra Henry Anetra Henry is not your typical public relations and marketing professional. She's a passionate storyteller and strategic messaging guru. As the Senior Director of Strategic Initiatives at the Institute for Public Relations (IPR), she continues to make waves with her expertise and contributions to research. Anetra's influence extends beyond her role at IPR. She's a sought-after speaker at industry conferences and events, where she shares her insights and expertise. Through her published articles, thought leadership pieces, and active participation in industry forums, she consistently contributes to the advancement of the field. Her dedication to excellence doesn't stop at the office door. She's a tireless advocate for diversity and inclusion, working hard to create opportunities for underrepresented voices and championing initiatives that foster inclusivity. Episode Timeline Introduction of Anetra Henry : 00:00:14 Spark for the Collaborators for Change Study : 00:02:21 Obstacles Faced in Conducting the Study : 00:04:21 Key Takeaways from the Study : 00:09:35 Insights into What 's Working and Recommendations for Improvement: 00:15:10 Personal Impact of Conducting the Research : 00:28:28 Future of Chief Diversity Officers : 00:34:30 Possibility of Longitudinal Study : 00:41:06 Closing Remarks and Thank You : 00:43:19 Resources: Download: Collaborators for Change Study The Costly Business of Discrimination Enjoyed the episode?  Please leave a review here - even a sentence helps. Share and tag us (@SoloPR, @SoloPRPro) on social media so that we can thank you personally! Your support helps us keep bringing you insightful content every week. Thank you for tuning in! Say Thanks to Anetra Henry! If you liked this episode with Anetra Henry, please say thanks on LinkedIn or Instagram. Listen to the episode on our website, Apple Podcasts, Spotify, Amazon Music, or on your favorite podcast platform. You can also watch the interview on YouTube here.

Today's episode provides an in-depth analysis of the 2015 film 'The Big Short', exploring its portrayal of the events leading up to the 2008 financial crisis. Steve and Mustache Chris discuss the movie's interpretation of key figures and financial mechanisms that played a critical role in the crisis, such as CDOs and the housing bubble, while comparing the film's narrative to the actual historical events and broader economic implications. Additionally, we touch on the moral questions and the consequences of the crash, alongside a critique of the banking and financial systems' roles in precipitating the crisis.You can learn more about Beyond the Big Screen and subscribe at all these great places:https://atozhistorypage.start.pagewww.beyondthebigscreen.comClick to Subscribe:https://www.spreaker.com/show/4926576/episodes/feedemail: steve@atozhistorypage.comwww.beyondthebigscreen.comParthenon Podcast Network Home:parthenonpodcast.comOn Social Media:https://www.youtube.com/@atozhistoryhttps://www.facebook.com/groups/atozhistorypagehttps://facebook.com/atozhistorypagehttps://twitter.com/atozhistorypagehttps://www.instagram.com/atozhistorypage/Music Provided by:"Crossing the Chasm" Kevin MacLeod (incompetech.com)Licensed under Creative Commons: By Attribution 3.0 Licensehttp://creativecommons.org/licenses/by/3.0/00:00 Introduction to 'The Big Short' and the 2008 Financial Crisis00:40 Personal Connections to the 2008 Financial Crisis01:35 Deep Dive into Financial Literature and Its Relevance Today02:44 Exploring the Cast of 'The Big Short'04:39 The Evolution of Mortgages and the Housing Bubble16:24 The Rise of Quantitative Trading and Its Impact22:19 Understanding CDOs and the Mechanics of the 2008 Crash34:01 The Ethical Quandaries of Subprime Loans and Their Aftermath38:51 The Psychology of Economic Bubbles39:02 Pop Culture and Economic Mania39:41 The Stanley Cup Mania: A Case Study42:07 The Dark Side of CDOs and Financial Collapse44:30 The Role of Rating Agencies in the Financial Crisis51:08 Analyzing 'The Big Short': Movie vs. Reality53:39 The Aftermath and Ethical Considerations of Bailouts01:13:54 Final Thoughts on Economic Collapse and Cinema

Episode OverviewWhile many CDO's inherit large teams, it's also common for CDO's to be hired into a role with the expectation they will build a data and analytics function slowly over time.In this episode of the CDO Matters podcast, Joyce Myers, the CDO of Modern Technology Solutions Inc (MTSI), shares her insights on how she's overcoming the many challenges that accompany CDOs who are starting small, and are highly reliant on the engagement of other teams for their individual success. Episode Links and ResourcesFollow Malcolm Hawker on LinkedInFollow Joyce Myers on LinkedIn

As part of the 2024 iTnews State of Data report, iTnews speaks with Jared Woodruff,  Head of AI Engineering at Lander & Rogers.iTnews is excited to announce the launch of the inaugural State of Data report on Thursday, June 20th, at a special Data Forum Breakfast at the NextDC Sydney Data Centre.Readers are invited to register their interest here:iTnews will host panel discussions with CIOs, CDOs, Data Analysts, and Data Scientists, covering topics from the report, such as Data Security, Data Analytics/AI, Data Storage, and Data Governance.We love to hear from our readers and advertisers, so please get in touch and let the friendly iTnews team help with your enquiry.

Lesley Robles Sedán, Director General de América Digital, platica acerca del Congreso América Digital, que se llevará a cabo los próximos 19 y 20 de junio, en el WTC de la Ciudad de México.  En su 9° edición, abordará temas con expertos en Inteligencia Artificial, Cloud, Data Analytics, ciberseguridad y transformación digital, entre otros temas, y está dirigido a los C-Levels (CEOs, CDOs, CIOs, CMOs, CTOs, CISOs, CFOs), así como a profesionales de TI, marketing development, cuya experiencia equivale a un MBA en tecnologías digitales y más negocios en 2 días.  --- Send in a voice message: https://podcasters.spotify.com/pod/show/el-economista/message

In Episode 31, of Season 4, of Driven by Data: The Podcast, Kyle Winterbottom is joined by Justin Windle, Head of Data Oversight at First Citizens Bank, where they discuss the purpose of data evangelism and how to set the north-star and sell the value, which includes;Setting your north star Operating in a buzzword-heavy industry Understanding your purpose and where you're heading Setting a North star in the absence of a data strategyWhy too many D&A journeys purely tactical Why your D&A strategy may change but your north star shouldn'tWhy value is the “why”?Why your communication around the north-star should be different for different audiences Why you should brand your data organisation internally The progression of the Data Leader Moving from reactive to value-driven Building to deliver value quickly but also towards your visionThe disparity between average tenure and average roadmap The need for having different CDOs at different stages of the journey Tying value to your north-star Creating advocates across your organisation How 99% of organisations are not sold on the value of dataWhy the primary role of the CDO is to be evangelist The importance of learning to craft the D&A message to the different business functions Why you have to sell the benefits and get people excited Why the last thing you should do is talk about data Why you should give people bragging rights Why you should never build your North Star on a business case Why value and valuable are 2 different things The relationship between culture and evangelism Why you may be struggling to build a data culture Getting the business stakeholders to help define the north-starWhy people are the major roadblock to reaching a north-starWhy it's easier to sell D&A to executivesEvangelism will help change the incorrect perception of the industry Why most companies don't understand what the role of the Data Leader isThanks to our sponsor, Data Literacy Academy.Data Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity.If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data literacy first.At Data Literacy Academy, we don't just teach data skills. We empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. We're bridging the gap between business and data teams, so they can all work towards aligned outcomes.From those taking their first steps in data literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that we offer. We embed a deep data culture shift through a transformative change management programme.We take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve.Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com.

Episode OverviewThe explosion of LLMs and AI is causing a tectonic shift in the world of data and analytics, and on this episode of CDO Matters, Malcolm and Jon Cooke discuss how CDOs can leverage the discipline of product management to best capitalize on these massive changes. From data products, to the data mesh, and beyond – Malcolm and Jon enjoy a lively discussion on the many evolving attributes that will increasingly define a world focused less on data (and datasets), and more on knowledge and business insights. If you're a CDO and you're interested in learning more about how your operating model will necessarily need to shift over the coming years to adapt to a new AI-driven world of insights, then this episode is a must-listen.Episode Links & ResourcesFollow Malcolm Hawker on LinkedInFollow Jon Cooke on LinkedIn

In this episode of the First Day Podcast, host Bill Stanczykiewicz, Ed.D. is joined by Ron Schiller, Founding Partner and Senior Consultant at Aspen Leadership Group. In a detailed exploration of the dynamics within nonprofit organizations, particularly between the Chief Executive Officer (CEO) and Chief Development Officer (CDO), the podcast looks at the evolving landscape of fundraising and leadership. The discussion underscores the critical nature of the CEO-CDO partnership in not just propelling the organization's fundraising efforts but also in enhancing the job satisfaction of both roles. The dialogue reveals that successful fundraising is increasingly seen as a collaborative endeavor that demands a deep understanding and appreciation of each other's capabilities, highlighting the shift towards a more integrated approach in leadership roles within the nonprofit sector. The podcast also sheds light on the changing expectations placed upon CEOs regarding their involvement in fundraising activities. It notes a significant increase in the time CEOs devote to fundraising, reflecting a broader trend across various sectors of the nonprofit industry. This evolution signifies a growing recognition of the importance of fundraising proficiency in the selection and performance of CEOs. Furthermore, the conversation brings to attention the expanded role of CDOs that extends beyond mere fundraising to encompass a wide array of responsibilities such as board engagement, financial planning, and internal politics navigation. This expanded scope underscores the need for CDOs to possess a diverse skill set to effectively manage the complex facets of development and organizational growth. Finally, the podcast emphasizes the importance of adopting an approach to fundraising, advocating for a shift away from traditional transactional methods towards a model that views philanthropy as a partnership. This paradigm shift aims to mitigate common apprehensions toward fundraising by fostering a collaborative environment that aligns the interests of the organization with those of its donors. Through sharing insights and engaging in honest communication, CDOs are encouraged to serve as vital liaisons, facilitating a deeper understanding of the organizational landscape for their CEOs. This approach not only enhances the fundraising process but also enriches the overall organizational culture, paving the way for more successful and fulfilling partnerships.

Description: In this episode, Chris Stephens, Field CTO at Appen, dives into how CDO's are navigating the world of generative AI. From setting clear expectations to driving adoption within organizations, Chris and Cindi explore the challenges and opportunities in this evolving landscape. Chris shares Appen's innovative approach to integrating humans into deep learning processes and discusses the potential of synthetic data. Plus, he shares how crucial human expertise is, in shaping ethical AI practices and touches on the impact of legislation and industry trends on AI's future.Key Moments: The Impact of generative AI on CDOs  [06:19]Appen and the excitement of generative AI [10:34]The potential of synthetic data and content curation [12:13]The importance of CDOs embracing generative AI [17:40]The early stage of generative AI and funding innovation [26:39]The importance of human in the loop [34:09]The role of legislation and industry leadership [38:46]Key Quotes: As a CDO, I think you absolutely have to figure out how to grab onto that, take ownership of it, and provide the leadership that your company needs - if you don't, then of course someone else will.  All of the challenges come on the non-technical side. Being successful in these programs is about more humanistic type skills than it is being a wizard in the technology space, in my opinion.The work that Appen does is working in support of all of these global organizations and the key is getting humans involved in these loops. Mentions: Deep learningDeep fake Synthetic data Gartner Generative AI Bio: Chris has been leading large-scale data transformations for over a decade, bringing advanced analytics capabilities to the world for 25 years.  Most recently, he served in CDO roles at GEICO, Zendesk, and American Eagle Outfitters.  Prior to that he helped lead the Data Science practice at Pivotal Software helping organizations around the world adopt modern data and software practices.  He is Field CTO and Head of AI Solutions at Appen bringing AI systems to life for organizations around the world.  He is an advisor to Insight Partners and Battery Ventures helping shape a new generation of technology and teams.  He is Adjunct Faculty at Carnegie Mellon University teaching our next generation of data and AI leaders.   He is passionate about the human side of data, transformation, and innovation.  He hails from Pittsburgh with his wife and 5 young adult children.  An avid music fan, he reminds us that, "you who choose to lead must follow."Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Over the next several years, the data landscape will undergo significant changes. As AI becomes more prevalent, businesses will become more reliant on applied analytics and machine learning.  ​These advancements bring up questions about strategies for maintaining ethical, trusted data, free from bias. And the application of this data to further the goals of purpose-driven organizations. ​Listen as CDOs explore the critical role of data strategies in today's rapidly evolving business landscape, discussing topics from fueling innovation and transforming the business with data insights to preparing for and utilizing generative AI. This conversation will also touch on the importance of diversity, especially when it comes to data analysis and better decision-making.Featured expertsClaire Thompson, Group Chief Data & Analytics Officer, Legal & GeneralGary Burnette,  Data & Analytics, Kyndryl

Did you know you could scan the eyeballs? Tune in today to hear a lot of great advice from Jeff Hernandez, CDOS, ROUB, a senior sonographer who specializes in ophthalmology ultrasounds. He gives great insight into the career field and shows that you can do whatever you want to do as long as you ask questions and reach for your goals!  Check out the youtube video for better visuals AND BONUS content -- an extra hour of speaking with Jeff and having more candid conversations!       Thank you for listening! Follow us on Instagram & find us on Facebook @sonographersinthecitiesMagic Mindwww.magicmind.com/sonoUse SONO20 for a 20% discount on your purchase!E-mail: sonographersinthecities@gmail.comwww.instagram.com/sonographersinthecitieswww.instagram.com/lolgesellewww.instagram.com/dmsdiaries

Ready to unravel the mysteries of the largest long-term capital market? Join us on an enriching journey as we decode the world of bonds with Edward Finley, a sometime Professor at the University of Virginia and an experienced Wall Street investor. Offering a deep-dive into the intricate workings of the $51 trillion bond market, Edward will shed light on the various types of bonds including Treasuries, corporate, and municipal, providing an invaluable insight into the dynamics of this, the largest long-term capital market.The episode takes a closer look at the differences between different types of bonds, breaking down their distinctive features. Edward's expertise also opens up the complex world of collateralized debt obligations (CDOs), providing a clear understanding of the $12 trillion mortgage-backed securities market and their different risk levels. As we navigate through the intricacies of conforming and subprime mortgages, we'll also journey back in time to explore the historical development of mortgages leading up to the great depression.The discussion culminates by exploring the aftermath of mortgage market innovation, shedding light on how subprime mortgages and adjustable-rate mortgages in the early 2000s played a role in the liquidity crisis that shook the banking system. Get ready to gain a comprehensive understanding of credit default swaps and how they contributed to the impression of these mortgages as less risky than they actually were. With Edward's expert guidance, this episode offers a golden opportunity to expand your knowledge of the bond market and how its evolution has shaped the existing financial landscape. Tune in for an enlightening discussion that promises to deepen your understanding of long-term capital markets.Notes - https://1drv.ms/p/s!AqjfuX3WVgp8uGKeNH2vSx0yuR85?e=TBpx9yThanks for listening! Please be sure to review the podcast or send your comments to me by email at info@not-another-investment-podcast.com. And tell your friends!

In this episode, Amir interviews Aaron Wilkerson, Carhartt's Senior Manager of Data Strategy and Governance. They discuss improving business outcomes and the importance of data in achieving that goal. Aaron shares insights on the strategy and accountability required for data-driven decision-making. They also touch on the role of technology in driving business success. Overall, this episode provides valuable insights for those responsible for data and technology in any industry. Highlights [00:04:26] Tying data strategy to brand.  [00:07:12] Data as the centerpiece.  [00:13:39] Elevated strategic vision  [00:15:54] Data governance and challenges.  [00:19:35] Change management and education.  [00:22:10] Finding the champion.  [00:26:23] Tying data to business outcomes.  [00:28:14] The evolving role of CDOs.  [00:32:39] Attributing work to forecast.  [00:34:35] Attaching work to business outcomes.  [00:37:56] Revenue growth through digital channels. --- Thank you so much for checking out this episode of The Tech Trek, and we would appreciate it if you would take a minute to rate and review us on your favorite podcast player. Want to learn more about us? Head over at https://www.elevano.com Have questions or want to cover specific topics with our future guests? Please message me at https://www.linkedin.com/in/amirbormand (Amir Bormand)

Lumia CEO Noelle Cordeaux is joined by Jim Graziano, founder of Zen Laundry Coaching. Together they discuss the inner workings of corporate America, the need for a revolution in the way leaders lead and what it looks like to push for evolution from the inside out. Jim Graziano helps organizations, leaders and individuals actualize their professional and/or personal potential by providing focused, unconventional, results driven leadership and management. Leading in a way that encourages and allows an employee, peer or colleague to thrive in the corporate culture, and being able to layer coaching philosophies on top of business philosophies, allowing for nuanced approaches to tackling barriers and finding solutions through keen analysis, Both, positively impacting the ability to deliver against business and shareholder expectations. I have a proven track record when working with or supporting new hires, and can equally interact directly with CEOs, CDOs or any other C Suite key players with ease. zenlaundrycoaching.com Everything Life Coaching is brought to you by Lumia-- at Lumia, we train and certify impact-driven coaches, making sure they've got all they need to build a business they love and transform lives, on their terms. Become a life coach, and make a bigger impact on the world around you! Schedule a call with us today to discuss your future as a coach. Music in this episode is by Cody Martin, used under a creative commons license. The Everything Life Coaching Podcast is Produced and Audio Engineered by Amanda Meyncke.

Be sure to visit cultureproof.net   Wil and Meeke look at The Heritage Foundation's Backgrounder on Chief Diversity Officers. How have school districts with CDOs failed Black and Hispanic students? The report is bad, but depending on objectives, not all bad...CDOs did succeed in one important area.   Please consider supporting the Culture Proof Podcast. We aim to bring engaging content that will challenge and equip Christians to live according to the Straight Edge of Scripture.   Culture Proof Podcast Theme "Believers" courtesy of Path of Revelation