Podcasts about cdos

In Episode 16 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Priya Enefer, Chief Information Officer at Hakluyt & Company, where they discuss why the tension between CIOs and CDOs isn't a people problem, but an organisational design problem.They explore how operating models, accountability, product thinking and executive alignment determine whether technology, data and AI become genuine competitive advantages or simply create duplication, politics and confusion.They also discuss:Why the tension between CIOs and CDOs is usually created by organisational design rather than the people themselves.Why every CIO role looks different and how organisational context should define the mandate.Why organisations should define accountabilities before they hire executives or choose job titles.Whether every organisation actually needs a Chief Data Officer.Why technology, data and product leadership must operate as one team if transformation is going to succeed.The lessons learned moving from Chief Product Officer to CIO and why she still thinks like a product leader.How separate technology, product and data strategies create duplication, confusion and competing priorities.Why product operating models fundamentally outperform traditional project delivery.Who should own AI and why there is no universal answer.Why many organisations are measuring AI activity instead of AI value and repeating mistakes made during previous technology waves.How AI risks becoming another executive land grab unless organisations are crystal clear on ownership and accountability.Why centralised data teams can become ivory towers.What the private sector can learn from government about delivering successful transformation.Why dashboards and insights in isolation is not leadership.Whether technology, product and data leadership roles will ultimately converge or simply become much more interconnected.Why organisational design, incentives and culture will ultimately matter far more than whichever AI model an organisation chooses.Thanks to our sponsor, Data & AI Literacy Academy.Data & AI 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 & AI literacy first.At Data & AI Literacy Academy, they don't just teach data skills. They empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. They'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 & AI 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 they offer. They embed a deep data culture shift through a transformative change management programme.They 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.

Welcome to another episode of The Data Debrief, the companion show to Driven by Data: The Podcast, where hosts Catherine Dowden-King and Kyle Winterbottom unpack Tuesday's episode, share what's been on their minds, and explore the realities of leadership, culture, and capability across the data and AI landscape.This week, Catherine and Kyle reflect on the conversation with Peter Crouch, Group Innovation Director at Lloyd's Banking Group, digging into what it really takes to build an innovation function that earns its keep, why "solving the right problem" beats "solving the problem right," and the discipline required to stay pragmatic about AI when the pressure to look busy is everywhere.They cover:Why Peter's insistence that his team isn't a consultancy or a bolt-on, but something fully embedded in the business, matters for how any new function establishes its identity and avoids becoming just another side-of-desk activityThe distinction Peter drew between solving the right problem and solving a problem right, and why so much technical effort gets poured into questions that were never worth asking in the first placeCatherine's tie-in to a Rory Sutherland case study on managing customer perception, and why reframing expectations can matter more than actually speeding up a processThe Porsche brakes analogy: why confidence and trust in the underlying systems, not raw speed or new tech, are what actually give people the courage to move fastPeter's candour about AI decisions ageing quickly given the pace of change, and why the psychological safety to kill a six-month project that isn't working is more valuable than seeing it through for the sake of appearancesThe idea of building repeatable, scalable capability for turning ideas into outcomes, rather than chasing the next isolated "big idea"Kyle's thought of the week: prompted by Catherine, Kyle unpacks a pattern he's seeing across senior searches, talented specialists (using data governance as the example) who've risen to the very top of their track, out-earning some CDOs, only to find themselves boxed in with nowhere left to go. He explains why deep expertise in one domain rarely translates into credibility for a central, cross-value-chain leadership role, and why the people who make that jump early, often before they feel ready, tend to end up better positioned long-term. His advice: get genuinely clear on where you want to end up, be honest about whether your current track can get you there, and be willing to take a sideways or even backward step now if it sets up the bigger move later.Catherine's thought of the week: inspired by Harry Kane losing his voice mid-interview, Catherine reflects on her own voice-loss moment hosting last year's Driven by Data Live, and makes the case for giving everything to your work when it counts, leaving it all out on the pitch without apology, while still knowing that pace isn't sustainable every single day.Plus, a programming note and a community shout-out: Catherine is off for a short camping-holiday hiatus, so the show will pause for a couple of weeks, and the mentorship scheme's winter cohort is now open, get in touch to be paired with someone outside your usual industry and hear how genuinely non-linear most people's career paths really are.This episode explores why real innovation isn't about chasing shiny new ideas, but about building the capability, confidence, and psychological safety to work on the right problems, know when to walk away from the wrong ones, and be intentional about where your own career is actually headed.

Autonomous AI and agentic AI are moving from experiments into real enterprise workflows. But are businesses truly ready to let AI systems plan, decide and take action on their behalf?In this interview, I speak with Shayan Mohanty, Chief Data and AI Officer at Thoughtworks, about the next phase of enterprise AI and what leaders need to do now to prepare.We explore the shift from AI assistants and copilots to autonomous agents, why governance and accountability need to be built into the architecture, and how enterprises can move beyond proof-of-concept projects toward scalable, production-grade AI systems.Shayan also explains why competitive advantage in the AI era may come less from access to the latest model and more from orchestration, AI-ready data, responsible engineering and the ability to redesign work around intelligent systems.This conversation is essential viewing for CEOs, CIOs, CTOs, CDOs and business leaders who want to understand what autonomous AI means for enterprise transformation, governance, software development and the future of work.If you'd like to explore how enterprises are preparing for this next wave of agentic AI, Thoughtworks' latest white paper, The Agentic Enterprise, offers practical perspectives and real-world insights.  https://www.thoughtworks.com/about-us/partnerships/cloud/aws/building-the-agentic-enterprise-ecosystem?utm_source=organic-influencer&utm_medium=influencer-marketing&utm_campaign=eai_tsi_rp-gl-pspt_rewire-for-agents_2026-05&utm_term=bernard-video-interview&utm_content=video#Sponsored Autonomous AI in the enterpriseAgentic AI and AI agentsAI governance and accountabilityEnterprise AI readinessMoving AI from pilots to productionAI-ready data and orchestrationThe future of software development#AutonomousAI #AgenticAI #EnterpriseAI #AIagents #ArtificialIntelligence #AIGovernance #AITransformation #DigitalTransformation #Thoughtworks #AIworks #FutureOfWork #BusinessTechnology

In this episode with Tamara Tomasevic, Head of Program Development at CIONET Germany, we discuss how cloud, data, and AI are driving digital transformation in supply chains. Tamara shares insights on fostering enterprise intelligence, encouraging cross-functional teamwork, and scaling AI through mindset and skill development. The episode explores leadership strategies and the future of supply chain connectivity and sovereignty.Download the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠episode transcript⁠⁠⁠⁠⁠⁠===== In this episode Richard and Sin talk with Tamara Tomasevic, Head of Program Development at CIONET Germany, about what really drives digital transformation. From digital sovereignty and exit strategies to re‑architecting processes around enterprise intelligence, Tamara cuts through the hype and gets real about what works. Learn why data quality makes or breaks AI, why mindset matters more than budget, and how leaders can scale AI with confidence in an increasingly complex supply chain world. ===== Guest: Tamara Tomasevic, Head of Program Development at CIONETTamara is Head of Program Development at CIONET Germany, connecting CIOs, CDOs, and CTOs across the country. She is passionate about building strong communities in the digital era. Previously, she helped establish the Bavarian State's AI Network and, as a communication scientist, believes communication is at the heart of digital disruption.Host: Richard Howells⁠⁠⁠⁠⁠Richard Howells⁠⁠⁠⁠⁠ has been working in the Supply Chain Management and Manufacturing space for over 30 years. He is responsible for driving the thought leadership and awareness of SAP's ERP, Finance, and Supply Chain solutions and is an active writer, podcaster, and thought leader on the topics of supply chain, Industry 4.0, digitization, and sustainability.Host 2: Sin ToSin To brings over 15 years of experience in the digital media and technology industry – primarily in marketing, business development, thought leadership, and editorial. At SAP, they ensure that SAP's supply chain solutions are properly visible with a focus on future trends and sustainable innovations as part of the Thought Leadership & Awareness Supply Chain Team.===== Show Links:SAP Digital Supply Chain: www.sap.com/scmCIONET Germany: https://www.cionet.com/cionet-germanyFollow Us on Social Media : Tamara TomasevicLinkedIn: https://www.linkedin.com/in/tamara-tomasevic-community/ Richard Howells:LinkedIn: ⁠⁠www.linkedin.com/in/richardjhowells⁠⁠ Sin To: LinkedIn: ⁠⁠www.linkedin.com/in/sin-to-5334208⁠⁠ SAP Digital Supply Chain:LinkedIn: ⁠⁠www.linkedin.com/showcase/sapdsc/⁠⁠ Please give us a like, share, and subscribe to stay up-to-date on future episodes!  ===== Chapters: 00:00:00 Sovereignty And Resilience00:00:31 Welcome And Power Triangle00:01:07 Meet Tamara And CIONET00:01:59 Digital Leaders Hot Topics00:05:10 Cloud Strategy In Supply Chain00:08:03 Data Quality And Ownership00:09:52 Turning Data Into Assets00:11:20 AI Misconceptions And Use Cases00:14:21 Scaling AI Beyond Pilots00:16:21 Why Networks Matter00:18:42 People Skills And Culture Shift00:20:46 90 Day Action Plan00:22:00 Future Outlook And Wrap Up

Google just announced the biggest change to search in its history — and not everyone is happy. At Google I/O, the search bar became a search box, signaling a full shift toward conversational, Gemini-powered search. The market is already reacting: DuckDuckGo rocketed from around position 400 to the top 100 in the app stores almost overnight, right around the May 20–21 launch. Mike Ryan and Chris unpack what a privacy-and-no-AI search wrapper suddenly surging tells us about consumer appetite — and why the relentless pace of change is exhausting retailers and experts alike.Then: fresh eMarketer data puts the AI advertising hype into much-needed context. AI ad spend in the US sits around $32B in 2026 (roughly a third of Google's search spend), but ~80% of it is just AI-search-adjacent — ads above and below AI Overviews. The chatbot-driven slice everyone is panicking about? Tiny. We dig into why OpenAI's $100B ad ambition looks unrealistic, what the 2030 projections actually say, and why the real takeaway for CMOs and CDOs is: prepare, but don't abandon your bread and butter.The throughline: everything is changing, and everything is staying the same. With limited budget and talent, the retailers who win are the ones who resist FOMO, pick their battles, and keep investing in the core campaigns that still drive the business.In this episode:Why Google's “bar to box” change is its biggest search shift ever — and what it implies about how Google wants you to searchDuckDuckGo's surge from ~position 400 to top 100: a clear sign some consumers find the new box too muchWhy the pace of change may now be too fast even for Google itselfAI Max for Shopping vs. PMax — the strategic misalignment Google may not even seeWhy standard shopping is here to stay (and the comeback we called early)The new eMarketer data: ~$32B AI ad spend in 2026, ~80% of it AI-search-adjacentWhy OpenAI's $100B ad goal and the 2030 chatbot-ad market projections don't line upThe real CMO/CDO playbook: prepare efficiently, beat the FOMO, protect your bread and butterAbout Smarter Ecommerce (smec):Smarter Ecommerce (smec) empowers e-commerce brands with AI-driven PPC automation that optimizes for profit and business outcomes while maintaining strategic control.The platform activates first-party data - profit margins, customer lifetime value, and key business metrics - to automate campaign optimization toward goals like profitability and efficient growth, while detailed campaign insights provide full transparency and enable PPC teams to focus on strategic oversight rather than manual execution.As a Google Premier Partner and three-time Microsoft Retail Partner of the Year, smec manages over €500 million in ad spend and drives €5B+ in annual e-commerce revenue for 350+ global retail clients including THG, Snipes, REWE, and Intersport.Make sure to follow smec - Smarter Ecommerce for more performance marketing insights:smec - Smarter Ecommerce: https://www.smarter-ecommerce.comLinkedIn: https://linkedin.com/company/smarter-ecommerce-gmbhNewsletter: https://smarter-ecommerce.com/en/newsletter/Instagram: https://www.instagram.com/smarterecommerce/

City leaders are on the front lines of data use, but most lack visibility into the federal data landscape, what's available, what's changing, and how federal policy decisions affect local outcomes. This gap delays emergency response, misdirects resources away from high-need neighborhoods, and undermines AI systems that depend on accurate data and community trust. Host Stephen Goldsmith speaks with Denice Ross,  Director of Federal Data Policy at the Federation of American Scientists, about the relationship between local and federal data, what city CDOs should prioritize, and why cities have untapped power to shape federal data policy. In this episode, you'll learn: The often-hidden relationship between local data needs and federal data infrastructure How to identify and access the federal data your city should be using Why now is the time to prepare for Census 2030 and protect funding How community participation in data decisions prevents disparities and builds legitimacy for AI systems How local data leaders can advocate effectively during federal policy windows Guest: Denice Ross –  Director of Federal Data Policy at the Federation of American Scientists; former United States Chief Data Scientist Listener Survey: bit.ly/datasmartpod Music credit: Summer-Man by Ketsa About Data-Smart City Solutions Data-Smart City Solutions, housed at the Bloomberg Center for Cities at Harvard University, is working to catalyze the adoption of data projects on the local government level by serving as a central resource for cities interested in this emerging field. We highlight best practices, top innovators, and promising case studies while also connecting leading industry, academic, and government officials. Our research focus is the intersection of government and data, ranging from open data and predictive analytics to civic engagement technology. We seek to promote the combination of integrated, cross-agency data with community data to better discover and preemptively address civic problems. To learn more visit us online and follow us on LinkedIn.

Every organization is told they need context for AI to work. Almost none of them know where to start. The answer has been sitting in their metadata all along — but most CDOs haven't connected those dots yet.

Most organizations default to replicating data: copying it from source systems into warehouses and lakes so their tools can reach it. Anu Jain, founder and CEO of Nexus One, thinks that's the wrong answer. Malcolm isn't so sure and that's where it gets interesting.

Enterprises have agents. Most can't run them at scale. IBM's Suzanne Livingston explains what changes when you have hundreds — not two.Full Show NotesScaling agentic AI is not the same problem as building it. At IBM Think 2026 in Boston, I sat down with Suzanne Livingston, VP of Product for IBM watsonx Orchestrate, to talk about where enterprise organizations actually are on this journey — and what it takes to move from a pilot to a production environment running hundreds of agents across dozens of departments.Suzanne walks through the full watsonx portfolio, then goes deep on the challenge she hears from customers constantly: the agent worked in the demo, but now it needs to run reliably at scale, with proper governance, observable across the estate, and permissioned correctly for every user and every system it touches. That is a fundamentally different problem than building the agent in the first place. The new Orchestrate Agent Control Plane is IBM's answer to it.This episode is for enterprise technology leaders who have moved past "should we do agents" and are now asking "how do we run them well." If your organization is somewhere between first pilot and full production deployment, this conversation is the one to listen to this week.What We CoverWhy the jump from generative to agentic AI changes the operating model, not just the technologyWhat agent orchestration means in practice when you have 40 sub-agents reporting to one master agentWhat the Orchestrate Agent Control Plane does and why cross-estate visibility matters more than per-agent optimizationHow enterprises are treating AI agents like digital employees — with identities, goals, managers, and performance reviewsWhy governance isn't optional in an agentic environment and what "governance light" looks like for organizations just getting started.Guest BioSuzanne Livingston is Vice President of Product Management for IBM watsonx Orchestrate, IBM's enterprise AI orchestration platform. She leads the product team responsible for agent building, orchestration, evaluation, and the recently announced Orchestrate Agent Control Plane. Suzanne presented at IBM Think 2026 in Boston.IBM Think profile: https://www.ibm.com/think/author/suzanne-livingstonResources MentionedIBM watsonx Orchestrate 30-day free trial: https://www.ibm.com/products/watsonx-orchestrateIBM Think 2026 content: https://www.ibm.com/thinkLopez Research blog: https://www.lopezresearch.com/research/

Katie and Matt discuss GameStop buying (?) eBay, authorized shares, socks, activism, negging, CDOs, CLOs, structured finance innovations, trading private credit, daily pricing, bad ways to own Anthropic stock, suing dentists and human assembly lines.See omnystudio.com/listener for privacy information.

Dell's CTO built a 4-category agent framework from real production deployments. Most enterprises are ignoring two of the categories that matter most.Full Show NotesEnterprise leaders are mapping AI agents to org charts — building digital employees, agentic teams, AI workers — and then wondering why the results fall short. Dell's Global CTO John Roese has been running agents in production long enough to know exactly why that framing fails, and what to do instead.In this episode, Roese shares a framework Dell developed from actual production deployments, not pilots. It identifies four categories of AI agents defined by two dimensions: how much autonomy you grant the agent, and how complex the underlying process is. Most enterprises are focused on one category. Two of the four are widely overlooked — and they may represent the fastest path to measurable ROI.This is a practical, grounded conversation about where agents are actually delivering value today, how to think about infrastructure cost in the context of agent economics, and why the sequence in which you deploy agents matters as much as which agents you build. If your organization is trying to move from AI experimentation to production, this episode is required listening.3. Chapter titles:[00:00] — Introduction: Dell's dual role as tech vendor and enterprise AI user[01:38] — Why the org chart model for agents fails[03:12] — Decoupling human capacity from work capacity for the first time[04:23] — The two-by-two framework: autonomy vs. process complexity[06:14] — Productivity agents: what most enterprises already have[07:00] — Hygiene agents: the overlooked category that fixes foundational data problems[08:01] — The CRM data example: why every CRM is inaccurate and how agents fix it[10:05] — Latent infrastructure capacity: running agents in GPU white space to cut costs to cents[13:53] — Facilitation agents: removing entropy from complex cross-functional workflows[17:30] — The sequencing insight: hygiene and facilitation as the path to expert agents[19:24] — Why coordination agents aren't agentic bosses — and where human control actually lives[22:21] — Roese's closing advice: become literate, pick a few, get them into production4. Guest BioJohn Roese is the Global Chief Technology Officer and Chief AI Officer at Dell Technologies, where he is responsible for technology strategy, AI deployment, and research and development across the company. He has held senior technology leadership roles at Nortel, Enterasys Networks, Broadcom, and EMC. At Dell, he operates at a rare intersection: leading AI strategy for a major technology vendor while also deploying AI internally at enterprise scale — which means his frameworks are tested against real production constraints, not just market positioning.LinkedIn: linkedin.com/in/johnroeseDell Technologies: dell.comAbout This PodcastAI with Maribel Lopez is a podcast for enterprise technology leaders navigating AI adoption, agentic systems, AI infrastructure, and AI governance. Host Maribel Lopez covers enterprise technology and advises CIOs, CDOs, CMOs, and technology vendors on how to move from AI experimentation to measurable business outcomes. New episodes published bi-weekly.Subscribe on your platform of choice: buzzsprout.com/1947446

Podcast Series: Don't Panic It's Just DataGuest: Mark Duffy, Senior Director, Artificial Intelligence & Analytics at Cognizant and Mark Blake, FSI Industry Practice Lead, Stibo SystemsHost: Scott Taylor, The Data Whisperer and Principal Consultant, MetaMeta ConsultingArtificial intelligence (AI) is prevalent in the insurance industry now, but many firms are not seeing the results they expected. The issue isn't with the AI models; it's pertinent to the data.In the recent episode of the Don't Panic It's Just Data podcast, host Scott Taylor, The Data Whisperer and Principal Consultant at MetaMeta Consulting, is joined by Mark Duffy, Senior Director, Artificial Intelligence & Analytics at Cognizant and Mark Blake, FSI Industry Practice Lead at Stibo Systems. The data industry experts address a key misunderstanding about enterprise AI – that companies can innovate their way out of poor data quality. “Some people think AI is a quick fix for data governance,” said host Scott Taylor. “If I need better data, I just use AI.” Experts warn that this belief is what's holding insurers back. How Frankenstein Data is Impacting AI?Despite significant investments in AI, cloud, and analytics, many insurers remain stuck in pilot mode. According to Mark Blake of Stibo Systems, the problem is the infrastructure. “AI itself isn't the challenge,” he said. “It's the ability to scale it, and that comes back to fixing the data.”In reality, most insurance enterprises face fragmented, siloed data across systems. Customer, policy, claims, and product data often don't align. This results in what Taylor calls “Frankenstein data,” where inconsistent records lead to unreliable outputs.For AI to function effectively at scale, insurers need trusted, governed, and unified data. That's where data governance and master data management (MDM) come in.“For us to truly gain benefits from AI, the end user really has to trust the data,” stated Mark Duffy of Cognizant. “That trust comes from having the right data foundation in place.”Also Watch: Can Your MDM Strategy Survive the Shift to Real-Time AI Decision-Making?How Master Data Management (MDM) Unlocks Scalable AI?One of the key drivers of AI success in insurance is multi-domain master data management, a system that connects core business data across the enterprise. “You always have to have a starting point,” Blake explained. “Then you expand horizontally across the enterprise.”The “horizontal data layer” enables insurers to unify key entities like customers, products, and partners—often referred to as the “nouns of the business.” When these are standardised, AI models can work consistently and accurately.The business impact is substantial, including more accurate underwriting decisions, reduced claims leakage, improved customer experience and retention and better cross-sell and upsell opportunities. Duffy shared a real-world example in which enhancing data management directly sped up AI adoption. “It gave them trust in the data,” he said. “They could run models faster and gain more value because they weren't constantly fixing issues.”Instead of spending 80 per cent of their time cleaning data, teams could finally focus on using it.Why AI Is Coercing a Data Strategy ResetFor years, data governance struggled to gain executives' support, but now AI has shifted that.“There's been a refocus,” Blake said. “They're looking at data in a way they maybe haven't done historically.”Today, AI is a priority for boards, driving alignment among CIOs, CDOs, and IT enterprise leaders. “Every C-suite executive wants to do more AI,” Duffy said. “But they've realised they can't do that without the data foundation.”Still, some enterprises believe AI can fix poor data quality. Experts warn that this is a mistake. “You can use AI to support data quality,” Duffy said. “But you're not going to use AI to build an MDM solution.”What's the Solution to Frankenstein DataAs insurers develop their AI strategies for the next 12 to 24 months, one key ideology was spotlighted – success depends less on speed and more on structure. “Go back to the root cause,” Blake said to Taylor. “Fix that, and then you can move forward with confidence.”In other words, AI highlights the need for strong data foundations; it doesn't eradicate them. For insurers serious about AI transformation, that's no longer optional—it's where they must begin.Also Watch: From Chaos to Launch: Your Product is Ready, Your Data Isn'tKey TakeawaysAI in insurance fails without strong data governance and quality foundations.Master Data Management (MDM) is critical for scaling AI across insurance enterprises.Fragmented “siloed data” is the biggest barrier to AI adoption in insurance.Trusted, unified customer and policy data improves AI accuracy and business outcomes.AI cannot fix bad data—insurers must modernise data management first.Chapters00:00 Introduction to AI Readiness in Insurance03:08 The Importance of Data Foundations06:02 Challenges of Fragmented Data09:06 Modernising Data Foundations for AI11:56 Real-World Use Cases in Insurance15:03 The Role of Master Data Management17:56 Aligning Business and Data Strategies21:06 Final Thoughts on AI and Data GovernanceFor more information, please visit em360tech.com and stibosystems.com.To learn more about AI in the MDM space and how they're progressing enterprise analytics intelligently, follow:Stibo Systems LinkedIn: @StiboSystemsStibo Systems X: @StiboSystemsStibo Systems YouTube: @StiboSystemsGlobalEM360Tech YouTube: @enterprisemanagement360EM360Tech LinkedIn: @EM360TechEM360Tech X: @EM360Tech#MasterDataManagement #DataGovernance #AIinInsurance #EnterpriseTech #BigData #DataStrategy #AIReadiness #InsuranceTechnology #cioinsights #StiboSystems #frankensteindatamaster data management, MDM, data governance, AI strategy, insurance, enterprise technology, big data, chief data officer, CDO, CIO, data quality, data unification, Stibo Systems, Scott Taylor, Mark Duffy, Mark Blake

For a hundred episodes, the data world has been told data is the center of the universe. It isn't — and three of the most credible voices in the industry are finally saying so out loud. Malcolm Hawker sits down with Scott Taylor, Juan Sequeda, and Samir Sharma for a milestone roundtable: why AI isn't Hadoop, why CDOs keep failing for the same reasons, and what it's going to take to survive what's coming.

Jonas Deichmann hat 120 Ironman in 120 Tagen absolviert – mehr als jeder Mensch vor ihm. Die Sportmedizin hat gesagt: körperlich unmöglich. In dieser Folge spreche ich mit ihm darüber, welche Daten im Extremsport wirklich zählen, wann Algorithmen wie der Wearable versagen und was CDOs, Datenteams und Führungskräfte vom Mindset eines Weltrekordlers lernen können. Key Takeaways: → Warum Jonas Deichmanns Wearable ihm jeden Tag '0% Erholung' gesagt hätte – und warum das irrelevant war → Die 2 Datenpunkte, die über 120 Tage wirklich über Erfolg und Verletzung entschieden haben (Spoiler: nicht Watt, nicht VO2max) → Wie sich sein Ruhepuls während des Projekts von 37 auf 70 erhöhte – und der Maximalpuls von 190 auf 115 fiel → Warum klassische Trainingspläne und Tour-de-France-Algorithmen für so ein Projekt scheitern mussten → Wie Deichmann Gewohnheiten designt – und wie Vertriebs-Teams genau dasselbe Prinzip nutzen können → Warum der Kopf entscheidet, wann die Erschöpfung kommt – und was das für langfristige High-Performance heißt Über den Gast: Jonas Deichmann ist Extremsportler, Abenteurer und Keynote-Speaker. Er hat die Welt mit dem Rad umrundet, die Fahrrad-Weltrekorde für alle Kontinente gebrochen und in 120 Tagen 120 Ironman-Distanzen absolviert – Weltrekord. Seine Netflix-Doku 'Das Limit bin ich' zeigt das Projekt. MY DATA IS BETTER THAN YOURS ist ein Projekt von BETTER THAN YOURS, der Marke für richtig gute Podcasts.

This week on the GovNavigators Show, hosts Adam and Robert sit down with Dr. Amanda Cash of the Data Foundation and Dr. Adita Karkera of Deloitte to unpack the latest Federal Chief Data Officer (CDO) Survey and what it reveals about the state of data, AI, and capacity across government.Drawing on six years of survey data, Amanda and Adita explain how the federal CDO role has evolved since the Foundations for Evidence-Based Policymaking Act and why today's environment may be the most challenging yet. With more than half of CDOs operating with five or fewer staff, agencies are being pushed to do more with less, even as expectations around AI, data governance, and transparency continue to rise.The conversation explores the growing overlap between Chief Data Officers and Chief AI Officers, the risks and opportunities of combining those roles, and how agencies can use AI to compensate for workforce gaps. They also highlight the critical role of the federal CDO Council in enabling collaboration and scaling best practices across government.Show Notes:Check out the CDO Survey hereCDO Survey webinar recordingWhat's on the GovNavigators' Radar:Apr 26 – 28: NASCIO's mid year conferenceApr 30: Fed100 Evening of Honors

Welcome to another episode of Data Debrief, the companion show to Driven By Data: The Podcast, where hosts Catherine Dowden-King and Kyle Winterbottom sit down to unpack Tuesday's conversation, share what's been on their minds, and explore what's really happening across the data and AI landscape.Fresh off Kyle's return from holiday, the pair dive into Tuesday's episode with Daragh Kelly, Chief Data Officer at The Economist, unpacking the ideas that stood out most, and a few that challenge the dominant narratives in the market right now.They cover:Why the concept of “Trad AI” (traditional machine learning and data science) is a useful lens, and how the market is blurring the lines between legacy AI and the new wave of generative and agentic capabilitiesThe ongoing hype cycle in AI, why it's nothing new, and how organisations risk getting distracted by buzzwords rather than focusing on real outcomesThe growing gap between building AI solutions and making them scalable, reusable, and commercially viableThe importance of defining what “AI” actually means inside your organisation, and why vague language is creating confusion at the board levelThe tension between speed and direction, and why moving fast means nothing if you're not solving problems that actually matterWhether operating models really need to change for AI, and why Dara's perspective challenges the prevailing narrativeThe shift from analysts as insight generators to “toolmakers”, and what that means for the future of data and analytics rolesThe rise of self-serve capability across organisations, and the risks of uncontrolled experimentation without governanceThe ongoing power struggle between CDOs, CIOs, and CTOs over AI ownership, and why the answer is far from settledThe role of optics, titles, and external brand in shaping career progression for data leaders in an AI-first marketPlus, in this week's Thoughts of the Week, Kyle challenges the long-standing narrative around “having a seat at the table,” arguing that it's often used as an excuse for not delivering value, and that true impact comes from driving outcomes, regardless of reporting lines. Catherine reflects on the role of diversity, equity, and inclusion in the data community, why the conversation is still far from where it should be, and the responsibility leaders have to actively shape a more inclusive industry.Like and subscribe wherever you listen, and if you've got a question or topic you'd like the team to cover, email community@orbitiongroup.com

Podcast: Don't Panic It's Just Data!Guest: Adrian Estala, VP, Field Chief Data & AI Officer, StarburstHost: Doug Laney, Research & Advisory Fellow at BARC and Author of Infonomics & Data JuiceAfter years of heavy investment in data lakes and warehouses, many enterprises still face a frustrating reality. Insights continue to remain slow, fragmented, and hard to trust.In the recent episode of the Don't Panic It's Just Data podcast, host Doug Laney, Research & Advisory Fellow at BARC and Author of Infonomics & Data Juice, is joined by Adrian Estala, VP, Field Chief Data & AI Officer at Starburst. They sat down to discuss why more enterprises are adopting a new architectural approach, the business semantic layer, to speed up AI adoption.What's the Core Issue in AI Data Enterprise?The core issue, Estala argues, is not a lack of infrastructure but an inconsistency between how data is organised and how enterprises think. “No one's really there yet,” he says, reflecting on a decade of backend optimisation. “We don't know what ‘perfect' architecture means, especially in the AI age.”The semantic layer, sometimes called a “context layer,” represents a shift from technical complexity to business usability. Typically, the system requires non-technical users to interpret schemas and pipelines; however, Starburst provides an abstraction that shows data in familiar business terms, along with metadata and governance rules.“If you build it right,” Estala explains, “when a CFO walks in the room and sees their semantic layer, it makes sense to them.”For an enterprise, this is more than just a usability improvement. It reduces duplication, eliminates conflicting metrics, and reduces reliance on IT teams for routine analysis. As Laney notes during the discussion, the goal is not to replace existing systems but to make them “that much more accessible” by layering business meaning on top.Also Watch: AI Is Replacing BI — Here's What CIOs Need to KnowSovereignty, Governance & the European RealityThe conversation is even more acute in regions like Europe, where data sovereignty has become a major concern. Regulatory pressure has led enterprises to rethink not only where data is stored but also how it is accessed and shared.Estala describes a federated model where data stays within national boundaries while still being usable globally. Organisations set up local clusters in countries like Switzerland or the United Kingdom, build data products locally, and apply strict rules for what can be shared centrally.“I can decide which data products are approved to be shared,” he says, alluding to compliance mechanisms that ensure sensitive information cannot be traced back to individuals.This creates a system that satisfies both regulators and business leaders. Executives no longer need to worry about jurisdictional complexities; they work with a unified view of data that has already been filtered, governed, and approved. “For them, it just feels like it's already been brought together,” Estala adds.As AI agents and copilots continue to gain popularity, the discussion also spotlights limitations. One such limitation is trust. Without confidence in the underlying data, even the most advanced AI tools struggle to provide meaningful value.“If they don't trust the answers, it's just a cool toy,” Estala says, describing a common pattern where initial excitement fades once users doubt the reliability of outputs.The semantic layer also tackles this discrepancy by embedding governance, lineage, and business rules directly into data products. Starburst helps enterprises clearly define which data is exposed to AI systems and under what conditions, making it easier to explain and justify decisions.Currently, Estala observes, AI mainly speeds up existing workflows instead of transforming them. Executives are asking the same questions they always have, but getting answers faster and from broader datasets. The real change, he suggests, will come when trust allows leaders to ask entirely new questions and rethink decision-making.How to Drive Business Value in 90 Days?For CIOs and CDOs eager to move past experimentation, the Chief Data and AI officer outlines a focused, business-led approach. Rather than launching large-scale transformations, he suggests starting with a single domain and building momentum from there.The first phase focuses on collaboration, bringing business stakeholders into the design of the semantic layer and defining the data products that are most important. “We design it with the business team in the room,” he explains, stressing ownership from the start.The next stage shifts to enablement, as teams begin to use and expand these data products themselves. This is where self-service takes root, reducing dependence on IT and promoting more exploratory use of data.By the final phase, enterprises are ready to introduce AI agents on top of a trusted foundation. At that stage, technology becomes almost secondary. “Once you get to a semantic layer that you trust, adding an agent is easy,” Estala says.As enterprises continue to adopt AI at larger scales, their competitive edge will come from algorithms and from how effectively they organise, govern, and contextualise their data. In this sense, the semantic layer is quickly becoming the backbone of modern, AI-driven decision-making.Key TakeawaysSemantic layers make governed data accessible for enterprise AI.Data sovereignty drives federated, compliant data architectures.Trusted AI needs governed, metadata-rich data products.Semantic layers deliver business value within 90 days.Virtual layers reduce duplication and speed up analytics.Chapters00:00 The Shift to Business Semantic Layers08:02 Data Sovereignty and Governance in Modern Strategies13:08 Foundational Capabilities for AI Systems18:11 AI Agents and Decision Making23:04 Practical Steps for Implementing Semantic LayersTo learn more about how data products and AI agents are changing enterprise analytics, follow:Starburst LinkedIn: @StarburstStarburst X: @starburstdataStarburst YouTube: @StarburstDataEM360Tech YouTube: @enterprisemanagement360EM360Tech LinkedIn: @EM360TechEM360Tech X: @EM360TechFollow: @EM360Tech on YouTube, LinkedIn and XStay connected for more expert insights, podcast episodes, and enterprise data strategy discussions.#SemanticLayer, #DataGovernance, #EnterpriseAI, #DataStrategy, #DataArchitecture, #AIatScale, #Compliance, #DataSovereignty, #ContextLayer, #AIagents, #DataProducts, #SelfServiceAnalytics, #CIO, #CDO, #Starburst, #AdrianEstala, #DougLaney, #DontPanicItsJustData, #EM360Tech, #TechPodcast

Data governance is the foundation of enterprise AI. If your data is not AI-ready, your copilots, agents, and automations can return bad answers, expose risk, and make the wrong decisions faster.In this episode of the Mostly Unstructured Podcast, Clay and Ed break down why enterprise AI success isn't just about model performance, but starts with data readiness, traceability, audit trails, validation, policy, and clear ownership across the business.Read our KeyMark companion article:https://www.keymarkinc.com/managing-a...Topics explored in this episode:• What data governance for AI actually means• Why many AI failures start with governance failures• How bad data, shadow AI, and weak controls create enterprise risk• Why traceability, monitoring, auditing, and validation matter before agents make decisions• How bias, compliance, privacy, and trust affect enterprise AI rollouts• What CIOs, CDOs, IT leaders, operations leaders, and compliance teams should ask before scaling AIIn this episode, Clay and Ed address key AI questions:• What is data governance for AI?• Why is data governance important for enterprise AI?• What makes enterprise data AI-ready?• Who owns AI governance in an organization?• How do you reduce AI risk without slowing innovation?• How do you govern agentic AI responsibly?If you are evaluating enterprise AI, agentic AI, intelligent document processing, or AI automation, this episode directs seekers in establishing smart AI beginnings with data governance for accurate data, and AI governance for output guardrails.

Welcome to the very first episode of Data Debrief, the companion show to Driven By Data: The Podcast, where hosts Kyle Winterbottom and Catherine Dowden-King sit down every Thursday to unpack Tuesday's episode, share what's been on their minds, and discuss what's really happening across the data and AI market.In this debut episode, Kyle and Catherine reflect on Tuesday's conversation with Kevin Cassar, diving deeper into the themes that sparked the most discussion, and a few that didn't make it into the main episode.They cover:Why the data conversation is shifting from delivering ROI to accelerating it, and what that means for data leaders still stuck in output-factory modeThe growing pressure on organisations to be seen doing AI, regardless of whether it's actually delivering value, and why the optics of inaction are now costlier than wasting budgetThe internal land grab between CDOs and CIOs over who owns AI, and why the answer is often more cultural than technicalWhy rolling out enterprise ChatGPT to an entire bank doesn't necessarily mean your organisation is embracing AIKevin's people-first philosophy, and why understanding where your team actually sits on the AI adoption curve matters more than having an aggressive strategyThe evolving role of the data professional: from coder to builder, from report-puller to AI quality controllerPlus, in their new Thoughts of the Week segment, Catherine raises a question that's hard to shake: are we over-optimising our downtime? From boiling water taps to washing up, she makes the case that friction isn't always the enemy, and that some of our best thinking happens precisely when we're not being productive. Kyle agrees and admits most of his best ideas arrive at 3am or are written on a shower panel.Like and subscribe wherever you listen, and if you've got a question or topic you'd like Kyle and Catherine to cover, email the team at community@orbitiongroup.com

Don and Tom kick things off with a colorful history lesson on 19th-century “bucket shops,” drawing a sharp parallel to today's emerging world of tokenized securities—digital representations of stocks traded on blockchain platforms. While proponents tout 24/7 trading and faster settlement, the hosts question the real value, highlighting added complexity, thin trading, pricing deviations, and unclear ownership structures. They frame tokenized investing as a solution in search of a problem—one that primarily serves speculators rather than long-term investors. The episode reinforces a familiar theme: avoid unnecessary complexity, ignore trading temptations, and stick with disciplined, low-cost investing. Listener questions cover whether retirees still need life insurance (generally no, if financially secure) and clarify that rebalancing means selling winners and buying laggards—not chasing losses.0:05 Intro and setup with historical market story0:24 Bucket shops explained—early stock market gambling1:50 Transition to modern “tokenized securities”2:35 What tokenized stocks are and how they trade 24/75:27 Blockchain explained in plain English6:23 Ownership confusion—what do you actually own?7:53 Custodian risk and structural concerns8:33 Pricing issues and thin trading risks9:01 Tokenization compared to past financial “innovations” (CDOs)10:54 Why investors should ignore tokenized securities11:26 New call-in system for podcast listeners12:03 Listener question: keep or drop term life insurance in retirement13:02 Why life insurance is unnecessary for financially secure retirees15:05 Listener question: selling losers vs. rebalancing16:05 Proper rebalancing strategy explained (sell high, buy low)17:31 Jack Bogle philosophy—do less, win moreQuestions? Comments? Click!

Episode OverviewIn this episode of CDO Matters, Malcolm Hawker sits down with Yext Chief Data Officer Christian Ward to explore how AI is fundamentally reshaping the relationship between data and modern marketing. As traditional playbooks built around search, SEO, and paid media begin to fracture, the conversation dives into what this shift means for CMOs—and why CDOs must step into a far more consultative and strategic role in guiding how organizations prepare their data for an AI-mediated customer journey. The result is a thoughtful discussion on the emerging data realities behind AI-driven discovery, and what data leaders must do today to ensure their marketing partners remain visible, relevant, and competitive in an AI-first world. Episode Links and ResourcesFollow Malcolm Hawker on LinkedInFollow Christian Ward on LinkedIn

Enterprise AI budgets are climbing, but the data foundations beneath them remain uneven. In this episode of Don't Panic, It's Just Data, Kevin Petrie, VP of Research at BARC, and Nathan Turajski, Senior Director, Product Marketing at Informatica, examine the findings of the CDO Insights 2026 report, which argues that executive confidence in AI may be outpacing organisational readiness. The study centres on what it describes as a growing “trust paradox” as Chief Data Officers are accelerating AI initiatives even as data quality, governance maturity, and AI literacy struggle to keep up. The Trust ParadoxThe report exposes a striking disconnect. Turajski points out that while around 65 per cent of data leaders believe employees trust the data powering AI, 75 per cent say upskilling in data and AI literacy is essential. In other words, confidence is high, but readiness is lagging.This is the trust paradox where employees increasingly rely on AI outputs, while data leaders remain cautious about the quality, governance, and lineage behind those results. The risk is not scepticism but rather overconfidence. When AI-generated answers are accepted without scrutiny, flawed data can quietly scale poor decisions. For CDOs, the challenge is cultural as much as technical.AI Adoption Soars While Data Readiness LagsThe harsh reality is that AI experimentation is no longer confined to innovation teams. It's spreading across marketing, operations, finance, and customer experience. As a result, scaling from pilot to production requires more than a model and a use case. To make AI work at scale, organisations need a data strategy that ensures consistency across domains, clear and transparent governance, measurable business impact, and sustainable management of their data assets.Data Quality and GovernanceTurajski explains that organisations are increasingly investing in data management and governance, with 86 per cent expanding data initiatives and 39 per cent prioritising upskilling. Metadata integration also helps unify distributed environments, providing the context AI needs to deliver reliable, trustworthy outputs. Organisations need to remember that AI systems amplify whatever they are given, so if inputs are inconsistent, incomplete, or poorly defined, outputs will reflect those weaknesses which are often at scale. Data quality challenges frequently arise from duplicated or conflicting records, inconsistent definitions across business units, poor lineage visibility, and limited ownership accountability. For example, a retailer might describe the same product in multiple ways across systems. Without standardisation, AI tools trained on that data produce fragmented insights, and when this occurs across thousands of products and regions, the distortions multiply. The takeaway from data leaders is clear: AI performance cannot be separated from disciplined, high-quality data management.Upskilling and Scaling AI AdoptionBoth Petrie and Turajski stress that technology alone won't close the gap. Upskilling employees in data literacy, AI fluency, and governance awareness ensures AI experimentation evolves into measurable, real-world results from improved customer experience to faster, more accurate analytics. The 2026 CDO Insights findings position data leaders at the centre of AI transformation. Their mandate extends beyond infrastructure to trust architecture. The trust paradox isn't a reason to slow down innovation. It's a reminder that lasting results require as much discipline as ambition. In 2026, the organisations that succeed won't be the fastest to adopt new technologies, but those that build the most reliable data foundations to support them.To learn more about this, visit informatica.comTakeawaysThe trust paradox highlights a disconnect between employee confidence in AI and leadership's caution.Data leaders recognise the need for upskilling in data and AI literacy.Building a trusted context is essential for effective AI adoption.The vendor landscape for data management is complex and requires careful navigation.AI is being used to enhance customer experience and loyalty.Measurable results from AI adoption are becoming a priority for organisations.Data governance must keep pace with AI use to mitigate risks.Successful organisations are leveraging unified data management platforms to drive AI value.Chapters00:00 Introduction to the CDO Insights Report03:13 Understanding the Trust Paradox in AI Adoption08:34 Building Trusted Context for AI14:11 The Importance of Data Quality and Completeness20:28 Navigating the Vendor Landscape for Data Management23:09 From Experimentation to Measurable Results27:38 Recommendations for CDOs and CISOs

Discover how enterprise AI and data strategy are operationalized at scale in one of the most highly regulated industries in the world. Louis DiModugno, Global Chief Data Officer at Verisk, shares how he builds AI-ready data foundations across 40+ petabytes of insurance and risk data, and the best practices behind embedding AI into enterprise products. He discusses unstructured data, deepfakes, and the shift from governance to observability, offering practical insights for data leaders scaling AI responsibly. Key Moments: From Military Leadership to Chief Data Officer: Data Integrity as a Competitive Advantage (03:02): Louis shares how his experience as a U.S. Air Force Colonel has shaped his approach to data governance, data quality, and enterprise AI leadership. He explains why integrity, service, and operational excellence are essential foundations for modern CDOs building trusted, decision-ready data environments. Building AI-Ready Data Foundations at a 40+ Petabyte Scale (17:13): Managing more than 40 petabytes of insurance and risk data, Louis breaks down how Verisk transforms complex, multi-source data into AI-ready infrastructure. From entity resolution and master data management to benchmarking and predictive analytics, he outlines what it takes to prepare enterprise data for AI and advanced analytics at scale. Designing an AI-First Data Strategy for Enterprise Decision Intelligence (20:00): Louis breaks down how Verisk evolved toward an AI-first data strategy across more than 150 insurance and analytics products. Rather than treating AI as an add-on, he explains how embedding AI into core workflows enables smarter underwriting, pricing, regulatory reporting, and risk management. He also discusses the strategic role ThoughtSpot plays in delivering natural language search, embedded analytics, and scalable AI-driven decision making. AI Fraud, Deepfakes, and Risk Management in Financial Services (26:11): As AI-generated images and synthetic claims become more sophisticated, Louis discusses how the insurance industry is combating deepfake fraud and AI-driven manipulation. He shares best practices around AI risk management, vendor partnerships, and regulatory collaboration to protect policyholders and maintain trust. Unstructured Data and AI: Why Governance Still Matters (29:28): Louis explores how expanding beyond structured data is reshaping enterprise AI. He explains why incorporating unstructured data into vector databases, graph models, and knowledge systems can significantly improve model accuracy and decision confidence. At the same time, he emphasizes that stronger governance (or observability as he reframes it) is essential as organizations scale AI across regulated industries. Key Quotes: “The more data that you bring to the equation, the more elements that you have in the algorithm, the higher level of accuracy you should be able to reach with your outcomes.” - Louis DiModugno “I've tried to move away from using the word governance as much as I like to use the word observability, because I really think observability shows more aspects of what it is that we are doing with the data.” - Louis DiModugno “The underlying aspect of what ThoughtSpot's delivering to them is our insights that not only give them their answer, but also give them insights that maybe they weren't looking specifically for. One of the big benefits of ThoughtSpot is that it's trying to anticipate what you're asking for.” - Louis DiModugno “We've partnered with ThoughtSpot, which brings AI embedded within its product. By having our data available through the data sets that we populate through the ThoughtSpot products, we've got the opportunity to utilize Spotter and the natural language processing capabilities to interact with the data, so that you can ‘talk with your data'.” - Louis DiModugno Mentions From Months to Weeks: How Verisk Scaled Embedded Analytics Breaking Down Digital Media Fraud for Claims in the AI Era Randy Bean's 2026 AI & Data Leadership Executive Benchmark Survey Guest Bio  Louis DiModugno brings more than 20 years of career experience in data and analytics to his new role. He has held several leadership positions in insurance and (re)insurance at firms including The Hartford and AXA US, where he served as the company's inaugural Chief Data & Analytics Officer. Most recently, DiModugno pioneered the role of Chief Data and Technology Officer for Hartford Steam Boiler. Before entering the private sector, DiModugno served with distinction as a Colonel in the U.S. Air Force and Air Force Reserves. He has held teaching positions at Rensselaer Polytechnic Institute, and he currently serves on the Chief Data Officer Advisory Council for the George Mason University School of Business. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Interim leadership is no longer the exception in nonprofits, it's becoming the norm. As executive tenures shrink and pressure mounts, organizations increasingly rely on interim CEOs, CDOs, and senior leaders to stabilize, reset, or prepare for what's next. But interims don't just affect the C-suite; they reshape staff behavior, donor confidence, and organizational momentum. In this episode, Randall breaks down the three true roles of interim leaders (caretaker, stabilizer, and change agent) and explains what success actually looks like for both the interim and the team navigating the uncertainty. Whether you are the interim or reporting to one, this episode offers practical clarity when leadership feels temporary but the mission isn't.

What happens when leaders are confident about AI, but the people expected to use it are not ready? In this episode of Tech Talks Daily, I sat down with Caroline Grant from Slalom Consulting to explore one of the most persistent tensions in enterprise AI adoption right now. Boards and executives are spending more, moving faster, and expecting returns sooner than ever, yet many organizations are struggling to translate that ambition into outcomes that scale.  Caroline brings fresh insight from Slalom's latest research into how leadership, culture, and workforce readiness are shaping what actually happens next. We unpack a clear shift in ownership for AI transformation, with CTOs and CDOs increasingly leading organizational redesign rather than HR. That change reflects how deeply AI now cuts across technology, operations, and business models, but it also introduces new risks.  Caroline explains why sidelining people teams can create blind spots around skills, incentives, and trust, especially as roles evolve and uncertainty grows inside the workforce. The result is what Slalom describes as a growing AI disconnect between executive optimism and day-to-day reality. Despite the noise around job losses, the data tells a more nuanced story. Many organizations are creating new AI-related roles at a pace, yet almost all are facing skills gaps that threaten progress. We talk about why reskilling at scale is now unavoidable, how unclear career paths fuel employee distrust, and why focusing only on technical capability misses the human side of adoption.  Caroline also challenges assumptions about skill priorities, warning that deprioritizing empathy, communication, and change leadership could undermine effective human-AI collaboration. We also dig into ROI expectations, with most UK executives now  expecting returns within two years. Caroline shares why that ambition is achievable, where it breaks down, and why so many organizations remain stuck in pilot mode. From governance and decision rights to culture and leadership behavior, this conversation goes beyond tools and platforms to examine what separates experimentation from fundamental transformation. As AI becomes a test of leadership as much as technology, how are you closing the gap between vision and execution within your organization, and are you building a workforce that can keep pace with change rather than resist it? Connect With Caroline Grant from Slalom Consulting The Great AI Disconnect: Slalom's Insights Survey Learn More About Slalom

Episode OverviewIn this episode of CDO Matters, Malcolm Hawker sits down with Sarah Levy, the CEO of Euno, to unpack why traditional data governance is collapsing under the weight of AI. They explore how context, metadata, and probabilistic thinking are redefining what “AI-ready” really means - and why CDOs who don't adapt quickly risk becoming irrelevant.Episode Links and ResourcesFollow Malcolm Hawker on LinkedInFollow Sarah Levy on LinkedIn

On today's manager meeting, Kristen VanGelder speaks with Jonathan Lewinsohn. Kristen is Deputy Chief Investment Officer at Evanston Capital, a $4 billion hedge fund of funds whose CEO and CIO, Adam Blitz, was a past guest on the show. She's spent the last eighteen years at Evanston alongside Adam and the team. Jonathan co-founded Diameter Capital four years ago alongside Scott Goodwin and today manages a $6 billion credit-focused hedge fund alongside $1 billion in CDOs and a $1 billion drawdown fund. The two were colleagues at Anchorage Capital, and Jonathan spent some time at Centerbridge Capital as well before starting Diameter.   Their conversation includes insights into the credit markets, Diameter's approach, and how it all comes together. Before we dive in, Kristen and I discuss how Evanston came to back Diameter on day one and how it fits into their portfolio.   Learn More Follow Ted on Twitter at @tseides or LinkedIn Subscribe to the mailing list Access Transcript with Premium Membership   Editing and post-production work for this episode was provided by The Podcast Consultant (⁠https://thepodcastconsultant.com⁠)

A Conversation with Joe Santana; a DEI original Would you agree that most conversations about DEI today sound loud, polarized, and disconnected from the work itself? In this episode of Everyday Conversations on Race, I talk with Joe Santana—advisor, author, and long-time DEI consultant—about where Diversity, Equity, and Inclusion actually came from and how it was originally practiced inside organizations. What really is DEI, (Diversity, Equity, and Inclusion)? Joe and I have both spent decades doing this work. We've watched DEI evolve, get renamed, repackaged, misunderstood, and in some cases quietly dismantled. What often gets lost is that DEI didn't start as a political position. It started as a business conversation—about how organizations function, how people are evaluated, and how talent is either used or ignored. What is the business case for DEI?  Why are people still talking about diversity, equity, and inclusion? The early thinking behind DEI and why it mattered to organizational performance How good intentions gave way to vague language and inconsistent practice What happens when leaders avoid difference instead of learning how to work with it Why "treating everyone the same" sounds fair but rarely works How Employee and Business Resource Groups can either matter—or miss the point entirely This is a grounded conversation between two practitioners reflecting on what we've learned, what we got wrong, and what still holds value—especially for leaders trying to make sense of the current moment. You'll learn more about the challenges, and strategic importance of Diversity, Equity, and Inclusion (DEI) in organizations. From the historical context provided by pioneers like Roosevelt Thomas to practical advice on optimizing business outcomes, Joe shares a wealth of knowledge on how DEI can drive both social good and financial success in companies. The episode also covers the vital role of Employee Resource Groups (ERGs) and what organizations can do to leverage them effectively. You'll gain valuable insights on turning DEI initiatives into strategic business tools. If you're looking for clarity instead of slogans, and experience instead of soundbites, you'll find it in this episode.   Guest Bio Joseph (Joe) Santana is a business strategy coach and futurist specializing in developing CDOs, ERG/BRG leaders, and Executive Sponsors who drive measurable business impact. He is an author, keynote speaker, and member of the Forbes Business Council and the Fast Company Executive Board and a frequent contributor to articles in both organizations' magazines. His insights and ideas have been shared globally in interviews with media outlets such as ABC, PIX, Fox, Ticker News, and The Black List, a streaming business interview show. His two most recent books, "The New DEI and ERG Frontier" and "SuperCharge Your ERGs," are available on Amazon, offering invaluable guidance to those ready to embark on the journey toward 21st-century business-impacting success. As CEO of Joseph Santana, LLC, an Inc Verified company, he leads multiple brands focused on equipping CDOs and ERG/BRG Chairs in national and global enterprises with the skills and strategies needed to enhance organizational performance. Below is a graphic depiction of the brands owned by Joseph Santana, LLC.   Click here to DONATE and support our podcast All donations are tax deductible through Fractured Atlas. Simma Lieberman, The Inclusionist, helps leaders create inclusive cultures. She is a consultant, speaker, and facilitator. Simma is the creator and host of the podcast, Everyday Conversations on Race. Contact Simma@SimmaLieberman.com to get more information, book her as a speaker for your next event, help you become a more inclusive leader, or facilitate dialogues across differences. Go to www.simmalieberman.com and www.raceconvo.com for more information Simma is a member of and inspired by the global organization IAC (Inclusion Allies Coalition)    Connect with me: Instagram Facebook YouTube Twitter LinkedIn Tiktok Website Previous Episodes Curiosity, Not Cancellation: Real Talk with Dr. Julie Pham Voices of Triumph: Stories of African Women Immigrants in America Black Health Matters: Community, Data, and the Journey to Wellness with Kwame Terra Loved this episode?  Leave us a review and rating

Samantha cuts through the AI hype to discuss what businesses are really experiencing, including thoughts on why investment in AI is high, yet value remains elusive - highlighting challenges with legacy technology, data, governance and workforce readiness.   Emphasising the crucial role of HR in shaping the future of work, working alongside tech and data leaders and the importance of cross-functional collaboration, Samantha highlights a striking insight from Slalom's research: empathy and communication are being deprioritised, even as these human skills become more important for trust, adoption and sustainable change.   Introducing the concept of AQ and the SHIFT framework, Samantha shows how organisations can build human and cultural foundations adept at dealing with continuous change, including AI adoption.   Her practical call to action for HR leaders: connect with your CTOs and CDOs, learn from each other, and help lead a future of work that balances technology with human needs.   How is AI really playing out? Slalom's new research Thank you to Slalom for sponsoring this week's podcast episode. Slalom is a business and technology consulting firm that believes meaningful change starts with people, helping organisations turn change into outcomes that actually last. If you're an HR leader navigating AI and wondering how to move from ambition to adoption, Slalom's latest research offers practical insights you can use right away. Slalom surveyed more than 2,000 global executives to understand how AI is really playing out, where investment is translating into value, where it isn't and, what that means for leadership, skills and cross functional alignment. Download your free summary here: Get Slalom's latest AI research Thanks again to Slalom for supporting the podcast, and thank you for being a brilliant HR Changemaker, we're excited to create more positive change together this year.

In dieser Folge des Venture AI Podcasts spricht Norman Müller mit Dr. Annette Doms über die eigentliche Leerstelle der KI-Debatte. Es geht nicht um Modelle, sondern um Vertrauen. Nicht um Tools, sondern um Infrastruktur. Annette verbindet Kunstgeschichte mit KI-Strategie und zeigt, warum Deutschland mit Verantwortung, Präzision und kultureller Tiefe eine führende Rolle im globalen KI-Zeitalter einnehmen könnte.Das Gespräch spannt den Bogen von Gutenberg bis ChatGPT, von Blockchain als Vertrauensinfrastruktur bis zur Frage nach Superintelligenz. Eine Folge für CEOs, CIOs und CDOs, die verstehen wollen, warum KI Transformation kein Technologieprojekt ist, sondern eine Führungsentscheidung.Zu den Shownotes---Wenn du uns dabei unterstützen möchtest, diesen Podcast zu einer Allianz von Zukunftsarchitekten der KI-Transformation zu machen, in der wir offen über Chancen, Risiken und reale Erfahrungen mit Künstlicher Intelligenz sprechen, dann abonniere uns auf YouTube, Spotify oder Apple Podcasts. Dein Abonnement kostet dich nichts, hilft uns aber sehr, noch mehr herausragende Persönlichkeiten für tiefgehende und inspirierende Podcast Gespräche zu gewinnen. Vielen Dank für deinen Support.Darüber hinaus laden wir dich ein, Teil der Plattform des Bundesverbands für KI-Transformation e.V. zu werden. Hier vernetzen sich mittelständische Unternehmen, KI Expertinnen und Experten, Startups sowie Vertreterinnen und Vertreter aus Forschung und Wissenschaft, um Wissen zu teilen, Erfahrungen auszutauschen und um an konkreten KI-Projekten zu arbeiten. In unserer Podcast Community kannst du dich einbringen, mitdiskutieren und den Bundesverband als Mitglied aktiv unterstützen und mitprägen.Zur Plattform:https://www.venture-ai-germany.spaceVernetze dich mit Norman auf LinkedIn:https://www.linkedin.com/in/muellernorman

Five years ago, we started Leader Generation with a simple premise: clients learning from clients. This year proved why that matters. In 2025, CEOs and their leadership teams stopped debating whether to adopt AI and began wrestling with harder questions: How do we bring our people along? How do we measure what matters instead of what's easy to measure? How do we maintain the relationships that built our business while transforming how we operate? Excerpts from these ten conversations capture what worked. Not theory—actual results from CDOs, COOs, and VPs leading change at companies like Southern Glazer's Wine & Spirits, DoubleVerify, and Digital Remedy. The full episodes are linked in this post. Stuart Goldstein explains why technology is the easy part, and people are where transformations actually stall. Alan Wizemann describes how his team measures success by the time freed up for employees to do valuable work, not headcount reduction. Anna Jankowska walks through preparing teams for scenarios they don't expect, because that's where you see who's ready to lead. These aren't polished case studies. They're candid conversations about the messy reality of leading change when your board wants results, your team is exhausted, and the playbook keeps changing. If you're heading into 2026 with aggressive goals and limited patience for more consultants telling you what you already know, these episodes cut through the noise. They're built for leaders who need to make decisions Monday morning, not attend another conference about the future of work. Links to the full episodes from this top 10 list: #10: https://tenloradio.com/e/ep129-building-your-army-of-ai-agents-what-marketers-need-to-know/ - Fabio Fiss, Aaron Grando, and Javier Lopez #9: https://tenloradio.com/e/ep109-ai-data-marketing-how-to-manage-risks/ - Kevin Purvis #8: https://tenloradio.com/e/ep116-future-of-performance-marketing-data-ai-personalization/ - Jeremy Haft #7: https://tenloradio.com/e/ep126-how-scibids-ai-is-redefining-media-buying-at-doubleverify/  - Wadrille Leroy #6: https://tenloradio.com/e/ep112-leading-through-change-the-human-side-of-marketing-leadership/  - Anna Jankowska #5: https://tenloradio.com/e/ep108-ai-playground-microsoft-copilot-google-notebooklm/ - Alyssa Curci and Jonathan Murray #4: https://tenloradio.com/e/ep140-leading-change-that-sticks-people-processes-platforms-with-stuart-goldstein/ - Stuart Goldstein #3: https://tenloradio.com/e/ep143-time-to-value-innovation-that-puts-customers-first-with-alan-wizemann/ * Alan Wizemann #2: https://tenloradio.com/e/ep119-leading-change-transforming-companies-with-alan-wizemann/ - Alan Wizemann #1: https://tenloradio.com/e/ep121-say-hello-to-brand-agent-ai-that-speaks-your-brand-s-language/ - Aaron Grando   About Tessa Burg: Tessa is the Chief Technology Officer at Mod Op and Host of the Leader Generation podcast. She has led both technology and marketing teams for 15+ years. Tessa initiated and now leads Mod Op's AI/ML Pilot Team, AI Council and Innovation Pipeline. She started her career in IT and development before following her love for data and strategy into digital marketing. Tessa has held roles on both the consulting and client sides of the business for domestic and international brands, including American Greetings, Amazon, Nestlé, Anlene, Moen and many more. Tessa can be reached on LinkedIn or at Tessa.Burg@ModOp.com.

What do a 1970 psychology experiment and the 2008 housing crash have in common? In Episode 6 of Built to Divide, Dimitrius Lynch traces how social identity theory—the instinct to form “us vs. them” groups—became a political weapon that helped sell a bipartisan push for mass homeownership, weaken skepticism, and pave the way for subprime mortgages, mortgage-backed securities (MBS), CDOs, and a crisis engineered by incentives.We move from NAFTA-era globalization and Peter Drucker's “core competencies” mindset, to the dot-com bust, Fed rate cuts, and the explosion of “stated income” lending. The episode spotlights Washington Mutual (WaMu)—from community-friendly bank to shareholder-driven mortgage machine—then follows the collapse, the scapegoating of low-income borrowers, and the rise of institutional investors turning foreclosures into portfolios. A story about housing, finance, and the narratives that keep us divided—even when the math says we share the same stakes.Episode Extras - Photos, videos, sources and links to additional content found during research.Episode Credits:Production in collaboration with Gābl MediaWritten & Executive Produced by Dimitrius LynchAudio Engineering and Sound Design by Jeff Alvarez

GreenLite delivers private construction plan review as an alternative to traditional city permitting processes. After spending six months testing both sides of the construction permitting transaction, the company identified owner-developers as their ICP and built a business model around Florida's privatization legislation—legislation that has now expanded to nine additional states including Texas, Tennessee, and California. In this episode of BUILDERS, we sat down with James Gallagher, CEO and Co-Founder of GreenLite, to explore how his fifth startup leveraged regulatory shifts, rejected workflow software in favor of outcomes, and scaled by targeting chief development officers at enterprise retailers struggling with permitting delays. Topics Discussed: How GreenLite discovered architects were heavy users but wrong customers due to two-part sales dynamics Why owner-developers became the ICP after six months of customer discovery across applicants and agencies The accidental discovery of private plan review through conversations with Fort Worth and Miami-Dade agencies GreenLite's platform combining regulatory permissions, licensed AEC professionals, and AI-augmented software How natural disasters and AEC talent shortages are accelerating privatization legislation nationwide Cold email strategies that converted enterprise retailers by surfacing acute pain points GTM Lessons For B2B Founders: Map two-sided markets to find where purchasing authority and pain intersect: GreenLite pitched a CTO at a major architecture firm who responded positively but said "I just need to talk to my client, my customer." This revealed architects required approval from owner-developers despite being the heaviest product users. James pivoted to owner-developers who "carry the land, carry the construction loans" and feel revenue delays most acutely. The lesson: usage intensity doesn't equal buyer authority. In complex ecosystems, systematically test which party controls budget and feels enough pain to sign contracts independently. Recognize when procurement cycles kill early-stage validation velocity: Cities explicitly told James their "crazy procurement cycles" made early partnership impractical despite genuine interest. State and local education and government sales require specialized expertise and extended timelines that prevent rapid iteration. James chose to prove the model with private sector customers first. For founders: government can be a lucrative eventual market, but unless you have sled sales expertise and 12+ month runway per deal, validate PMF elsewhere first. Capitalize on regulatory tailwinds before markets realize they exist: Only Florida permitted private plan review when GreenLite launched in July 2022. By late 2024, nine states passed enabling legislation driven by natural disaster reconstruction needs and talent shortages in city building departments. James positioned GreenLite to ride this wave rather than selling transformation to resistant agencies. Founders should monitor legislative and regulatory changes in their verticals—new compliance requirements or permissions can suddenly open massive TAMs with minimal incumbent competition. Enterprise cold email converts when you surface non-obvious acute pain: GreenLite cold emailed chief development officers at major retail chains and quick-service restaurants with "Are you missing your openings due to permitting?" The response rate validated that permitting delays—not site selection or construction costs—were a critical path blocker for store rollout velocity. James targeted CDOs rather than real estate or design teams because they own the full development timeline. For enterprise sales: identify the executive accountable for the metric your solution impacts, then lead with how you move that specific number. Validate outcome-based models before building sophisticated workflow tools: GreenLite's customers rejected "another workflow product or system of record" that required API integrations with their ERPs and construction management systems. Instead, they wanted "faster, more predictable, more transparent permits." James built a viable business delivering finished permits through licensed professionals augmented by software, with the AI sophistication coming later. The business was "super viable well before the product was" by early 2023. For founders in industries resistant to software adoption: test whether buyers want tools to operate or outcomes to purchase—outcome-based pricing can achieve PMF faster and command premium willingness-to-pay. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role.  Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

While the role of a chief data officers (CDOs) was traditionally focused on regulatory compliance, it has now expanded to empowering the consistent and effective use of data across organizations to improve business outcomes. One of the most effective ways for CDOs to demonstrate their value is by developing a data strategy that is closely aligned with business goals, processes, and outcomes. In the latest episode of Tech Transformed, host Kevin Petrie, VP of Research at BARC, speaks with Brett Roscoe, Senior Vice President and GM of Cloud Data Governance and Cloud Ops at Informatica, about the evolving role of CDOs. Their conversation explores how CDOs are transitioning from data stewards to strategic leaders, the importance of data governance, and the challenges of managing unstructured data.The Role of the CDO in the Agentic EraAs Roscoe notes, “CDOs are now pivotal in AI strategy,” reflecting how the role has grown from compliance oversight to guiding enterprise initiatives that directly support organizational goals.In this day and age, CDOs are tasked with ensuring that data is both accessible and reliable, providing a foundation for informed decision-making across business units. This includes establishing policies for data quality, access, and governance, which Roscoe highlights as essential: “data governance is foundational for AI.” At the same time, unstructured data ranging from documents and emails to multimedia adds complexity that requires careful management to make it useful while minimizing risk. “Unstructured data presents challenges,” he adds, emphasizing the need for structured oversight to fully leverage these assets.AI StrategyAlthough technology and analytics are evolving rapidly, the CDO's role in aligning data with strategic initiatives is critical. By connecting data assets to business processes, CDOs help ensure that initiatives are informed by reliable, well-governed information and can deliver measurable results.For anyone looking to understand the evolving responsibilities of CDOs, the importance of governance, and strategies for handling unstructured data, this episode of Tech Transformed provides a detailed and practical discussion.For more insights, follow Informatica:X: @informaticaInstagram: @informaticacorpFacebook: https://www.facebook.com/InformaticaLLC/LinkedIn: https://www.linkedin.com/company/informatica/TakeawaysCDOs are now central to shaping AI strategies and driving business growth.Robust data governance is crucial for the successful deployment of AI technologies.Unstructured data presents unique challenges and opportunities for AI development.A balance between centralized governance and federated operations is essential.Securing executive...

After 1,500+ conversations with CDOs and VPs of data , guest Malcolm Hawker noticed a disturbing pattern: a "limiting mindset" that causes data leaders to fail. He argues that too many leaders blame external factors such as "culture" , "data literacy", or a lack of support rather than taking accountability for delivering value.In this conversation, Malcolm breaks down how this mindset is reinforced by the analyst and consultant community and why it leads to a "value fatigue" where no one can prove their own ROI. He offers a clear path forward, starting with a simple 3-question framework for any new CDO and explains why "culture" is actually an outcome of delivering value, not a prerequisite for it. We also discuss his new book, "The Data Hero Playbook," tackle the "AI Ready" myth , explaining why conflating it with "BI Ready" is holding companies back and why your data is likely "good enough" to start right now.

Sujay Dutta and Sidd Rajagopal, authors of "Data as the Fourth Pillar," join the show to make the compelling case that for C-suite leaders obsessed with AI, data must be elevated to the same level as people, process, and technology.They provide a practical playbook for Chief Data Officers (CDOs) to escape the "cost center" trap by focusing on the "demand side" (business value) instead of just the "supply side" (technology). They also introduce frameworks like "Data Intensity" and "Total Addressable Value (TAV)" for data.We also tackle the reality of AI "slopware" and the "Great Pacific garbage patch" of junk data , explaining how to build the critical "context" (or "Data Intelligence Layer") that most GenAI projects are missing. Finally, they explain why the CDO must report directly to the CEO to play "offense," not defense.

Welcome to a special author's episode of The Data Chief, where we delve into the minds of three influential authors who are shaping the conversation around data and AI. First, Geoff Woods, author of The AI-Driven Leader, shares his philosophy of prioritizing strategy over technology to make faster, smarter decisions. Next, Wendy Batchelder, author of The Data Governance Handbook, discusses how to transform governance from a rigid bureaucracy into a business accelerator by focusing on business outcomes. Finally, Malcolm Hawker, author of The Data Hero Playbook, challenges data leaders to adopt a heroic mindset by becoming customer-driven and aligning their incentives with business success. Join us to learn how to lead effectively in the AI era by building a strategy-driven, governed, and customer-centric data function.The Data Chief Podcast: Author Episode Key MomentsGeoff Woods: The AI-Driven LeaderFrom "IT Problem" to Strategic Partner (06:20): Woods advocates for viewing AI as a "strategic thought partner" rather than an assistant or replacement, and emphasizes that AI strategy must align with business strategy.The CRIT Framework for Smarter Prompts (12:25): He introduces the CRIT framework for prompt engineering: Context, Role, Interview, Task. This method helps leaders get non-obvious, high-impact strategies from AI by having the AI ask the right questions.Beyond the Bottom Line: AI's Human Impact (22:17): Woods discusses the ROI of AI, including a case where AI identified savings equivalent to 2% of a company's revenue. Wendy Batchelder: The Data Governance HandbookData Governance as an Accelerator (32:33): Wendy Batchelder addresses the myth that data governance is a "dirty word" or a code for "no," arguing that its true purpose is to be an accelerator.Speaking the Language of Business (35:17): Batchelder emphasizes that data governance should be embedded from the start of a project, not as an afterthought. She provides an example of "bad" vs. "good" communication, urging data professionals to speak the language of the business.Measuring Value with Business Outcomes (40:00): She outlines how to measure the value of data governance by connecting it to business outcomes like increased revenue or improved customer service. Malcolm Hawker: The Data Hero PlaybookFrom Limiting Mindset to Growth Mindset (56:00): Hawker discusses why he wrote the book, calling the current moment a "do or die" opportunity for CDOs. He challenges the "limiting mindset" that leads to defeatism.Customer-Driven, Not Data-Driven (1:08:00): He urges data leaders to be "customer-driven, not data-driven," emphasizing the need for data teams to become more business literate.The Power of Product Management (1:14:00): Hawker advocates for bringing product management disciplines into data teams. This approach focuses on putting the customer at the center and ensures that data products are economically viable and tied to ROI.Key Quotes:"It is not technology first, strategy second. It is strategy first, technology second.” - Geoff Woods"The companies that are treating data as something that helps drive business outcomes are thinking about data at the beginning and set up at the end." - Wendy Batchelder“If you deliver value to your customers, if you are the lever of change and transformation in your organization, if you show value from data, you will get a seat at the table." - Malcolm HawkerMentionsThe AI-Driven Leader: Harnessing AI to Make Faster, SmarterHow AI is transforming strategy developmentData Governance Handbook: A practical approach to building trust in data5 key reasons why data analytics is important to businessThe Data Hero Playbook: Developing Your Data Leadership SuperpowersCDOs and CDAOs: Rethink your role or fade awayGuest Bios:About Geoff Woods Geoff Woods is the #1 bestselling author of The AI-Driven Leader, host of the AI-Driven Leader podcast, and Founder of AI Leadership and The AI-Driven Leadership Collective™, a highly vetted network of executives collaborating to harness AI to build better businesses and better lives. As the former Chief Growth Officer of Jindal Steel & Power, Geoff's strategic leadership helped the company grow its market cap from $750 million to over $12 billion in just four years. Prior to that, he co-founded the training and consulting company behind The ONE Thing, advising businesses ranging from $10 million to $60 billion in annual revenue.About Wendy Batchelder Wendy Batchelder is a three-time Chief Data Officer across financial services, technology & healthcare industries, with a wide understanding of how to take highly technical aspects of data management and translate them into simple, concise business valued solutions that are practical and simple to understand. Her background has led her to lead global data & analytics organizations at four Fortune 500 companies. She approaches situations with curiosity and humility, which has led to applying innovative data solutions to challenges with increased complexity to deliver value that companies can measure.A lifelong learner, Wendy graduated from Miami University with a B.S. in Accounting and Information Systems, from Drake University with a Masters of Accountancy, from University of Iowa with an Executive MBA, and pursues ongoing education through Harvard Business School. Her work history includes EY, KPMG, Aviva, Wells Fargo, VMware and Salesforce.About Malcolm HawkerMalcolm helps senior business leaders harness the power of data to transform their businesses. As a former Gartner analyst, he has consulted with some of the world's largest and best-known brands on their enterprise information management strategies and digital transformation initiatives.He is a frequent public speaker on data and analytics best practices with a passion for Master Data Management (MDM) and Data Governance. He welcomes the opportunity to share practical and actionable insights on how companies can become truly data-driven by implementing the cultural, technical, and organizational changes needed for success in the digital age. He is also the author of The Data Hero Playbook. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

In this episode, Generation AI analyzes groundbreaking research from OpenAI and Anthropic that reveals how AI usage is fundamentally different than expected. Hosts Ardis Kadiu and Dr. JC Bonilla dissect OpenAI's study of 1.5 million ChatGPT conversations, uncovering that 70% of usage is now personal rather than work-related - a complete reversal from initial predictions about enterprise productivity gains. They explore how ChatGPT has reached 700 million weekly active users with 90% of usage now outside the US in less than 3 years (compared to 23 years for the internet), while Claude data shows enterprise users focusing heavily on coding (36% of usage) and autonomous workflows (39% of conversations). The discussion reveals critical implications for higher education: while consumer AI adoption explodes globally with gender parity achieved (52% women users), institutions remain stuck with budget constraints, scattered use cases, and talent retention issues. This episode provides essential insights for education leaders on why the shift toward personal productivity and home-based AI usage creates both untapped opportunities and urgent challenges for institutional AI strategy heading into 2026.OpenAI's Massive ChatGPT Usage Study Overview (00:02:08)Analysis of 1.5 million ChatGPT conversations through NBER working paper700 million weekly active users, most comprehensive AI usage study everCollaboration between OpenAI Economic Research, Harvard economist David Deming, and NBERConsumer plans only - excludes enterprise and API usageSample represents massive scale given ChatGPT's global reachExplosive Growth Patterns and Metrics (00:05:27)Reached 100 million weekly users in under one year (unprecedented speed)Message volume growing even faster than user countAverage user sends 7-8 messages per day (up from 2x in 2024)Cohort analysis shows steady usage for existing users, new users driving intensityGrowth accelerates with each major model releaseGlobal Adoption Outpacing All Previous Technologies (00:08:09)90% of usage now outside North America (achieved in under 3 years)Internet took 23 years to reach same international distributionLower-income countries showing fastest adoption ratesImplications for international marketing and student recruitment strategiesGlobal phenomenon across all economic levelsGender Parity Achievement (00:11:30)Women users increased from 37% (January 2024) to 52% (July 2025)Based on analysis of typically feminine vs masculine namesReflects natural population distribution (50/50 split)Usage patterns now mirror general population demographicsThe Personal vs. Work Usage Revelation (00:13:24)Work-related usage dropped from 47% to only 27%Over 70% of ChatGPT usage is personal/non-work relatedHidden economics of home productivity emerging (not captured in GDP)Similar pattern to mobile device "bring your own device" adoptionEnterprise adoption significantly slower than consumerUsage Intent Categories and Detailed Breakdown (00:16:37)Three main categories: Asking (49%), Doing (40%), Expressing (11%)Practical guidance: 28.8% (top use case)Seeking information: 24.4% (up from 18% year-over-year)Writing: 23.9% (declining as users discover new applications)Multimedia: 7.3% (peaked at 12% after GPT-4o image features)Technical help: ~5%Self-expression: ~5%Specific High-Demand Use Cases (00:19:32)Tutoring/teaching: 10.2% (major opportunity for ed-tech)How-to advice: 8.5% (vertical SaaS potential)Personal writing & editing: 18% (demand for AI co-pilots)Coding in ChatGPT: Only 4.2% (compared to 36% in Claude)Each use case bar represents potential startup opportunity or graveyardClaude/Anthropic Enterprise Usage Analysis (00:27:42)Coding dominates: 36% of Claude usageAutonomous workflows: 39% of conversations (up from 27%)API automation: 77% of business API tasks are full automationMore complex multi-step workflows emergingGeographic usage reflects local economies (NYC: finance, Hawaii: tourism, Massachusetts: science)The Context and Data Bottleneck (00:34:52)Major enterprise bottleneck: Data/context readinessShift from prompt engineering to context orchestration for 2026Context engineering becoming the critical capabilityIntegration with existing platforms determines successOrchestration requires both technology and specialized talentEnterprise AI Economics and Priorities (00:37:26)Companies prioritize capability over cost savingsModel capabilities drive adoption more than pricingBusinesses "lean into automation over cost savings"Not yet highly price sensitive - capacity matters moreBudget lines for AI becoming essential planning itemHigher Education Specific Challenges (00:42:41)Minority of institutions identify as AI leaders75% of CDOs see moderate risk to academic integrityMost exploring scattered use cases vs. campus-wide programsBudget constraints remain primary blockerMarketing and enrollment teams leading adoptionStudent support and advising showing strong use casesTalent retention crisis as AI champions leave for better opportunitiesLabor Market Implications and Timeline (00:45:48)Fortune reports AI potentially replacing entry-level workersContext-heavy work remains difficult to fully automateAnthropic predicts powerful automated systems by late 2026-early 2027Low-hanging fruit automation tasks already saturatingNeed to view AI as outcomes rather than featuresKey Strategic Takeaways (00:46:47)Consolidation into integrated platforms expected for 2026Data connectors and ecosystem integration criticalConsumer adoption patterns informing enterprise strategyHome productivity gains creating new economic value unmeasured by GDPInstitutions need separate AI budget lines immediatelyPlatform strategy required vs. point solutions - - - -Connect With Our Co-Hosts:Ardis Kadiuhttps://www.linkedin.com/in/ardis/https://twitter.com/ardisDr. JC Bonillahttps://www.linkedin.com/in/jcbonilla/https://twitter.com/jbonillxAbout The Enrollify Podcast Network:Generation AI is a part of the Enrollify Podcast Network. If you like this podcast, chances are you'll like other Enrollify shows too! Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Episode OverviewIn this episode of CDO Matters, Malcolm Hawker sits down with Andreas Blumauer to explore how knowledge graphs are transforming enterprise data strategies. They discuss why semantics and domain models are critical for making data truly AI-ready, and how CDOs can use these tools to bridge the gap between data governance and AI innovation. If you're leading data initiatives and want to understand the future of knowledge management, this conversation is essential listening.Episode Links and ResourcesFollow Malcolm Hawker on LinkedInFollow Andreas Blumauer on LinkedIn

Many companies spend a lot on data technology, but often forget about the importance of data and AI literacy. Without the right skills, even the best platforms can fail to deliver results. Teams need to understand how to work with data and AI to make any strategy successful.In this episode of Tech Transformed, EM360Tech's Trisha Pillay chats with Greg Freeman, the founder of Data Literacy Academy about why knowing data and AI matters for anyone building a digital strategy.Data and AI LiteracyFreeman points out that many data strategies end up as technical documents rather than actionable roadmaps. He explains that organisations often spend heavily on infrastructure, expecting better tools to solve their problems but without employees who understand how to work with data and why it matters, these investments rarely deliver results.Freeman explains that data strategies often fail because only a small portion of employees less than 20 per cent are truly enthusiastic about data. Most strategies are designed with this minority in mind, creating an echo chamber that leaves the majority behind. As a result, data stays siloed, and business decisions don't improve. The Data Literacy Academy founder stresses that unless organisations engage the 80 per cent of employees who aren't already invested, their strategies are unlikely to succeed. When the focus is on tools rather than people, adoption falls behind.TakeawaysData and AI literacy are key to turning strategy into value.Tools alone don't work; people need confidence and context.Focus on engaging the data-hesitant majority, not just the enthusiasts.Cultural change, not just technical change, is what drives ROIChapters00:00 – Introduction02:07 – Beyond the Tech Stack04:41 – Why Strategies Fail08:41 – Literacy Barriers12:08 – Success in the Real World17:17 – Building Lasting Literacy22:20 – AI Needs Literacy Too26:33 – Final TakeawaysAbout Greg FreemanGreg Freeman is the founder and CEO of Data Literacy Academy, where he works with CDOs, CIOs, and business leaders to drive real cultural change around data. His mission is to help organisations tackle data illiteracy by building confidence and capability from the ground up, especially for employees who feel disengaged or anxious about data.With a background in sales leadership and tech startups, Greg brings both strategic insight and real-world experience.

In this episode, Junaid reflects on the CDOIQ Symposium in Boston, emphasizing the overwhelming focus on AI, especially AI agents, and their impact on white-collar jobs. We discuss the immense value of networking at conferences and debate whether CDOs overemphasize data quality at the expense of other critical areas like culture and literacy. And finally, we explore where CDO's oversteer and what they under value. 

On the 54th episode of Enterprise AI Innovators, hosts Evan Reiser (Abnormal AI) and Saam Motamedi (Greylock Partners) talk with Max Chan, Senior Vice President and Chief Information Officer at Avnet. Avnet is a $20 billion global technology distribution company that plays a critical role in the electronics supply chain, supporting the design, production, and delivery of devices worldwide. In this episode, Max shares how Avnet is utilizing generative AI to transform the way work is done across product design, quoting, customer service, and IT. He outlines their strategic maturity model for AI adoption and why CIOs must lead with experimentation.Quick Hits from Max:On evolving IT's role: "[We] moved away from the monolithic type of solutions into a more cloud-first, digital-centric composable architecture. That change truly helped with driving any and every innovation that we are talking about today.”On framing enterprise AI: "We bucket [AI use] into three types. First, we talk about out-of-the-box generative AI tools… The second bucket is what we like to call embedded AI… Last is truly custom AI solutions."On generative design: "The engineers, instead of coming with three designs [and having] the customer look at it, come back with some recommendations or questions, [and then] they go back to the drawing board; now they can immediately get in front of a customer [and] say, 'hey, look, these are the things that we can do if this is what you want [and] these are the parameters that you're changing.'"Recent Book Recommendation: Competing in the Age of AI by Karim Lakhani.  "A great cheat sheet for CIOs and CDOs on how to get started and what to avoid."-- Like what you hear? Leave us a review and subscribe to the show on Apple, Google, Spotify, Stitcher, or wherever you listen to podcasts.Enterprise AI Innovators is a show where top technology executives share how AI is transforming the enterprise. Each episode covers the real-world applications of AI, from improving products and optimizing operations to redefining the customer experience. Find more great insights from technology leaders and enterprise software experts at https://www.enterprisesoftware.blog/ Enterprise AI Innovators is produced by Josh Meer.

Episode OverviewIn this episode, Malcolm welcomes Mark Stouse back to unpack the Delaware court ruling that's sending shockwaves through boardrooms. They cut through the BS on what fiduciary duty now means for data leaders, why ROI theater won't cut it anymore, and how CDOs must evolve their approach to value or risk becoming irrelevant.Episode Links and ResourcesFollow Malcolm Hawker on LinkedInFollow Mark Stouse on LinkedIn

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.

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

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.  

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.

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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