Podcasts about CDO

Richard Entrup is unusual in quantum circles: he's not a physicist, and he doesn't pretend to be. He spent decades as a CIO, CTO, CDO, and CISO at organizations including Verizon, Christie's, Disney/ABC, Time Warner, and Tiffany & Company before joining KPMG to lead its Emerging Solutions practice. That background — deep operational experience on the client side — shapes everything about how he thinks about quantum. He's not selling a hardware roadmap; he's thinking about what it actually takes to get a large, complex organization to change its cryptographic infrastructure before a threat materializes.The conversation matters now because the signals are accelerating. NIST has finalized its first post-quantum cryptography standards, executive orders in the US are pushing federal agencies toward PQC migration, and the algorithmic efficiency gains that reduce the qubit threshold for breaking RSA-2048 keep coming. Listeners who work in enterprise technology, cybersecurity, or quantum strategy — or who advise organizations that do — will find Entrup's practitioner perspective a useful counterweight to the more hardware-focused conversations that dominate the field.What We Get IntoWhy Q-Day's exact date is the wrong question — and why the more important issue is how long it will take enterprises to even inventory their cryptographic exposure, let alone remediate itThe scale of the cryptographic migration problem, including why a single laptop may contain hundreds of individual cryptographic components and why upstream/downstream API dependencies make this a supply-chain-wide challenge, not just an internal IT projectWhy "harvest now, decrypt later" creates urgency today, regardless of when fault-tolerant quantum computers arrive — and how compliance and regulatory timelines interact with that threat modelWhat crypto agility actually means in practice — moving from a "set it and forget it" cryptographic posture to a dynamic, continuously monitored framework, including the pressure SSL certificate renewal windows are already creatingHow KPMG built its PQC practice, incubated it within the firm, and handed it off to the cybersecurity advisory team as a core service offeringThe "good quantum" side of the ledger — how KPMG's emerging research function is approaching quantum computing as a source of competitive advantage, not just risk, and what sectors are furthest along in exploring itThe AI-quantum convergence, including Entrup's observation that AI is already being used to read and crack code — and what that means for the urgency of cryptographic modernizationWhy the enterprise quantum opportunity still has a long tail, and how the current moment compares to the early infrastructure phase of the internet — when everyone was talking about TCP/IP and DNS, not Uber or NetflixResources & LinksGuest & OrganizationRichard Entrup — Worth Magazine Profile — Career arc from CIO/CISO roles at major global brands to KPMG's Emerging Solutions practiceKPMG Quantum Dawn (2025) — KPMG's enterprise quantum readiness hub, introducing the Q-PREP framework and PQC implementation services, with Entrup as named leadReports & ResearchKPMG — "The Quantum Threat Is No Longer Theoretical" (2026) — The threat brief discussed in this episode, charting the rapid decline in qubits needed to crack RSA-2048 and urging immediate PQC migrationKPMG — "From Theory to Impact: Real-World Results in Quantum Machine Learning" (2026) — KPMG's joint report with IBM and Kipu Quantum on measurable quantum ML results on real hardwareKPMG — "Prepare Now for Quantum Cyber Risk" — Board Leadership Article (2026) — C-suite and board-level guidance on integrating quantum risk into enterprise oversightarXiv — "Quantum-enhanced satellite image classification" (2026) — The underlying research paper behind the KPMG/IBM/Kipu Quantum ML resultsEcosystem & EventsChicago Quantum Exchange — KPMG Joins CQE (October 2024) — Announcement of KPMG's formal CQE membership, referenced in the episode as part of the firm's ecosystem-building strategyKPMG 2026 Quantum Consortium — The inaugural KPMG Quantum Consortium event (March 2026, Orlando) discussed in the episodeIndependent CoverageQuantum Computing Report — KPMG joins Chicago Quantum Exchange (2024) — Independent coverage of KPMG's CQE partnership and enterprise quantum strategyQuantum Zeitgeist — Kipu Quantum satellite imagery coverage (Feb 2026) — Independent analysis of the KPMG/IBM/Kipu hybrid QML resultsKey Quotes & Insights> "It's not if but when. And it could be five years, could be three years, could be ten years. The fact is organizations are not gonna be ready. And that's the scary part." — Richard Entrup on Q-Day> "This is not just the CISO. This is gonna be the software engineering app dev guys. This is gonna be all your partners, upstream and downstream, who have to also be compliant — because if you change your crypto and they don't, that stuff's gonna break." — On why PQC migration is an enterprise-wide, supply-chain-wide problemInsight: Entrup draws a sharp distinction between the "bad quantum" (cryptographic risk requiring urgent defensive action) and the "good quantum" (competitive opportunity with a longer tail) — and argues that most organizations aren't adequately addressing either.Insight: The analogy to the early internet is deliberate: just as the 1990s were consumed with TCP/IP and DNS rather than the applications those protocols would eventually enable, the current quantum moment is still largely an infrastructure conversation — and that's normal, not a sign of failure.> "AI is expediting all of this. If AI is doing one thing, the use case is reading code and cracking it. That's pretty scary." — On the intersection of AI capability and cryptographic vulnerabilityRelated EpisodesEp. 81 — Quantum LDPC Error Correction with Larry Cohen and Paul Webster — Directly relevant: Cohen and Webster discuss how QLDPC error correction reduces the qubit overhead needed for RSA cryptanalysis, the technical underpinning of the threat timeline Entrup describesEp. 38 — Quantum Machine Learning with Jessic...

In Episode 20 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Nick Zervoudis, Founder at Value from Data & AI, where they discuss why so many data and AI teams struggle to demonstrate measurable business value — and why the real failure almost always happens upstream, long before anyone tries to articulate it.Nick makes the case that "we can't prove our value" is usually a symptom, not the disease: teams solve the wrong problem, skip the value case, or hand off value realisation to no one. Along the way they get into his five-point diagnostic framework, how to build a credible back-of-the-envelope ROI estimate before a line of code is written, how to prioritise a portfolio of opportunities, and where AI productivity savings are real versus imaginary.They also discuss:Why the inability to demonstrate value is usually an upstream failure, not a communication problem.What Nick's five-point framework reveals: wrong problem, wrong solution, poor execution, no measurement, weak communication.Why data teams keep solving the wrong problem by starting from technology instead of the problem itself.How to separate the "problem space" from the "solution space" before reaching for a tool.Why 70–80% of data teams operate as order takers rather than true collaborators.Why being ROI-positive is only the entry ticket, not a reason to do a project.What criteria actually decide prioritisation: return, payback speed, implementation readiness, and strategic relevance.Why nothing a data team builds has inherent value without an owner on the business side to realise it.How to build a credible back-of-the-envelope value case before anything gets built.Why estimating value is far easier to learn than the technical craft most data people already have.How to get stakeholders to correct a rough estimate rather than hand them a blank sheet.Why "how will we measure success?" is the most useful question you can ask when scoping work.What the bystander effect has to do with data teams quietly failing to create value.How framing work around outcomes turns engineers from code-writers into problem-solvers.Why hours saved rarely become money on the balance sheet.What the five-to-six buckets of productivity value are, and why you must never double-count them.Why some AI investment should deliberately have no business case at all.How Monday.com turned a five-week experimentation window into a $100M ARR product.Why blanket self-serve analytics or company-wide AI licences often set you up for failure.What first steps a CDO should take to re-prioritise a roadmap around measurable value.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/driven

Brannin McBee, co-founder and CDO of CoreWeave (CRWV), talks about the company is "second to none" in the AI space after the stock rallied strong on earnings. He addresses how CoreWeave sets up contracts and utilizes its backlog while juggling energy needs and pushback from some public groups. ======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

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 Marion Shaw, Senior Director of Data, Analytics and Data Management at Cencora, and her new book on data culture, before digging into why the "data culture" debate keeps circling the same questions, and why the answer almost always comes back to people rather than technology.They cover:The wildfires spreading across South Wales and Europe, and how AI and drones are being deployed in France to spot smoke earlier, distinguish dust from smoke, and cut down the false positives that waste emergency resourceThe bigger climate paradox facing the industry, from record-wet winters followed by hosepipe bans and water mismanagement, to the uncomfortable reality that the data centres powering AI advances are themselves enormous consumers of waterWhy absolutism helps no one, and how the healthiest position on AI, sustainability, and change sits somewhere in the messy middle rather than all-in or all-outHow incentives quietly shape behaviour, illustrated by the fact that three flights across Europe can cost less than a single train from Manchester to London, and why people ultimately do what they're incentivised to doCatherine's latest build for the Orbition community: a NED Opportunity Finder, a live, daily-updating table of listed non-executive director roles showing remuneration, location, and whether the board is public or private, free to access for registered community members, and why she's so keen to see more data leaders move into board positionsWhy Marion's candour hit home, especially the reminder that you simply cannot force people to be interested in or care about data, and why that truth is uncomfortable but essentialHow technology becomes a distraction, the "shiny thing syndrome" that pulls focus away from the outcomes that actually matter, and why that's the real answer to "why now"The endlessly debated question of whether "data culture" even exists, why the industry loves arguing over semantics no one outside it cares about, and why every organisation already has a data culture, somewhere on the spectrum from barely-there to full tiltWhy culture and outcomes feed each other rather than being an either/or, and how influencing behaviours and showing results almost always happen in tandemWhy every business claims to be "data-driven," how the reality usually differs, and why a new CDO's first 90 to 100 days is really about working out where the organisation actually sits versus where leadership thinks it doesThe "slippery shoulders" problem, and why nothing improves or gets maintained unless someone genuinely owns itWhy you can't see your own culture from the inside, and how stepping out to network, attend events, and compare notes with peers is often the only way to know whether you're ahead, behind, or better off than you thoughtA look ahead to the Orbition magazine landing in October, featuring a data leader who hasn't spoken publicly in over two years, alongside mentor and mentee stories and perspectives from beyond the CDO communityThe Director of Police AI role at the College of Policing, and how its rigid entry criteria expose the same old problem seen across data leadership: job descriptions that bear little resemblance to what organisations actually want from the roleKyle's thought of the week: most job descriptions are disconnected from what the business actually needs. Organisations have learned to use the right language, asking for leaders who'll work with the board and use data to drive commercial performance, then listing purely technical requirements underneath. Until that gap closes, the mismatch between what's advertised and what's wanted will keep repeating itself. Fundamentally, every organisation already has a data culture; it simply sits somewhere on a spectrum, and the job is to understand where before trying to move it.Catherine's thought of the week: you rarely recognise your own culture until you step outside it. Whether it's trust versus micromanagement, or how your data leaders are really perceived, the comparison only becomes clear when you go out, meet people, and see how others operate. And the honesty applies inward too, because no organisation describes itself as not caring about data, so the real work is uncovering where it genuinely stands.This episode is a candid, wide-ranging conversation on data culture, ownership, and incentives, and a reminder that the hardest problems in data and AI leadership remain stubbornly human, no matter how much the technology moves on.Housekeeping: The podcast is now broadcasting on LinkedIn Live. To watch along in real time, head to the Driven by Data Productions page on LinkedIn and follow it. We go live with each episode every Tuesday at 1pm BST, and our guest often joins the comments to answer your questions. Keep an eye out for upcoming events towards the end of the year, including Driven by Data Live, where Catherine will be handing out physical copies of the new magazine.

Dan Nathan and Guy Adami dig into the biggest story in markets: Nvidia's roundtable with Wall Street's top financiers — Jensen Huang, David Solomon, Jon Gray, and Stephen Schwarzman — and the multi-hundred-billion-dollar backstop deal getting compared to a modern-day CDO. Dan lays out why he thinks this AI CapEx build could make the dot-com bust and the GFC look tame, walks through Nvidia's doubling credit default swaps, and answers a listener question on exactly what would signal the bubble has popped. Plus: the cautionary tale of The Trade Desk's collapse from $140 to $14, why valuations are only richer once before in history (the dot-com peak), and a preview of what to watch in Cisco's earnings after the close today. Show Notes A short history of valuing stocks (FT) Wall Street just endorsed Jensen Huang's ‘big concept' for AI. What now? (CNBC) —FOLLOW USYouTube: @RiskReversalMediaInstagram: @riskreversalmediaTwitter: @RiskReversalLinkedIn: RiskReversal Media The financial opinions expressed in Risk Reversal content are for information purposes only. The opinions expressed by the hosts and participants are not an attempt to influence specific trading behavior, investments, or strategies. Past performance does not necessarily predict future outcomes. No specific results or profits are assured when relying on Risk Reversal. Before making any investment or trade, evaluate its suitability for your circumstances and consider consulting your own financial or investment advisor. The financial products discussed in Risk Reversal carry a high level of risk and may not be appropriate for many investors. If you have uncertainties, it's advisable to seek professional advice. Remember that trading involves a risk to your capital, so only invest money that you can afford to lose. Derivatives are not suitable for all investors and involve the risk of losing more than the amount originally deposited and any profit you might have made. This communication is not a recommendation or offer to buy, sell or retain any specific investment or service.

Today we are bringing a great show for you. Ron Arenas and Ron Kirk are hosting. Our first guest is CDO football coach Scott McKee. Coach McKee brings many years of valuable experience as a coach and as a former walk on player at the University of Arizona.  We'll find out about this season's CDO team.Then, our next guest is Tom Sommerville. A former police officer and current member of Boys In Blue. Tom will share with us the mission of Boys In Blue. And why it means so much to him.

Ein Traditionsunternehmen, das seit 1753 im Geschäft ist – und trotzdem gerade eine der konsequentesten SAP- und KI-Transformationen im deutschen Mittelstand durchzieht. Karsten Rösener, Chief Digital Officer der Haus Cramer Gruppe (Warsteiner), erklärt, warum er sich bewusst gegen eine Greenfield-Neuaufstellung entschieden hat, wie ein eigener KI-Assistent namens „Vitus" entsteht – und warum am Ende die Menschen über den Erfolg jeder Technologie entscheiden. Key Takeaways: → Warum Karsten sich bei der SAP-S/4HANA-Transformation bewusst gegen Greenfield entschieden und stattdessen 40 kleine, flexible Teilprojekte gestartet hat → Wie ein wetterabhängiges Saisongeschäft eine eigene Datenarchitektur und ein komplett neu aufgesetztes Reporting braucht → Warum Warsteiner sein Ticketsystem und sein eigenes Sprachmodell komplett intern betreibt – Stichwort Datensouveränität → Wie der interne KI-Assistent „Vitus" – benannt nach dem Sohn des Firmengründers – aus einzelnen Assistenten-Use-Cases entsteht → Warum KI-Campus, KI-Botschafter und KI-Champions den Unterschied machen und Führungskräfte, die selbst mit KI arbeiten, einen vierfach höheren Durchsatz erzeugen Über den Gast: Karsten Rösener ist seit Februar 2024 Chief Digital Officer der Haus Cramer Gruppe – eine neu geschaffene Position mit Verantwortung für die SAP-/S4HANA-Transformation, Infrastruktur, IT-Security und KI. Zuvor war er von 2016 bis 2024 Chief Information & Digital Officer der Ostfriesischen Tee Gesellschaft (Laurens Spethmann Holding), wo er 2021 mit der SAP-S/4HANA-Migration den 3. Platz beim Wettbewerb „CIO des Jahres" in der Kategorie Mittelstand belegte, und davor CIO der GoodMills Group. 2025 wurde er mit dem Confare ImpactAward ausgezeichnet. MY DATA IS BETTER THAN YOURS ist ein Projekt von BETTER THAN YOURS, der Marke für richtig gute Podcasts.

The world of IT is filled with technical qualifications in all manner of disciplines. They've become an expected baseline for knowledge in broad fields like networking and security, as well as narrower, vendor-specific knowledge and skills. But what happens as IT leaders move up to more senior roles? Steve Clarke, cofounder and director of Freeman Clarke joins host Eric Hanselman to talk about the challenges that senior IT professionals have in establishing their bona fides as technical business people. The role of a CIO, CTO, CISO or CDO requires technical depth, but the more important part is the ability to integrate that knowledge with business operations. It's not an easy transition to undertake and even more complicated to identify.  Discussions that are taking place in the 451 Research 451 Alliance community brought up the idea of a business level certification process and how it would have to characterize that unique knowledge set. The Standard is an approach that Steve and his colleagues began to address this issue. The 451 Alliance membership is free and open to all qualified IT professionals and business leaders who want to contribute to industry-leading research. Members take anonymous online surveys in exchange for access to research results that power their IT strategy.   More S&P Global Content: Join the 451 Alliance The Standard Certification The Problem with the Status Quo For S&P Global Subscribers: US tech spending intent dips in Q2, but projects to grow in Q3 – Tech Demand Indicator Highlights from Q2 2026 Beyond connectivity: A CIO's road map from network APIs to business impact Enterprises cautiously optimistic for AI amid data management challenges Host/Author: Eric Hanselman Guest: Steve Clarke, cofounder and director of Freeman Clarke and Tech Leaders Connect Producer/Editor: Dylan Scheible Published With Assistance From: Feranmi Adeoshun and Sophie Carr

Today I am joined by Misty Farmer and Amanda Osborne! Misty Farmer leads Make a Wish South carolina's mission to grant life-changing wishes for children with critical illnesses across the state.   Her forward-thinking approach has enabled the organization to adapt and flourish, leading to a steady increase in revenue and the number of wishes fulfilled each year for more than a decade. Amanda Osborne currently serves as the CDO at Make-A-Wish South Carolina. In this role, she oversees the fundraising operations, event, major gifts solicitations and other revenue generating initiatives.  Amanda is also a proud graduate of Leadership Greenville, Class 42.  In this episode we talk about the Wish granting process, the impact of a single wish, what working at a non-profit is like, how they both got their start in the non-profit sector, and so much more!Make-A-Wish WebsiteMake-A-Wish FacebookMake-A-Wish Instagram

A Delaware state court ruling rewrote the rules on corporate liability and CDOs are directly in the crosshairs. This is our most-played episode ever, and if you haven't heard it, now's your moment.

In this episode, Ricard Vilá, CDO at LATAM Pass, outlines his strategies for designing a future-ready technology organization. He shares lessons on shifting IT from a cost center to a value engine, fostering genuine team empowerment, managing multilingual organizational structures and navigating rapid AI transformation to meet evolving business needs.

The Small Business Administration is ramping up its relationship with Palantir, announcing a “new phase” in its anti-fraud work with the data analytics and software giant. In a press release Tuesday, the SBA said it's formalizing and expanding its work with Palantir after signing a $300,000 contract in January for a fraud prevention pilot and bootcamp. That deal had a projected end date of April 4, but the agency said in the release that the “collaboration” with Palantir will now continue through “ongoing efforts to identify, investigate, and help prosecute fraud in pandemic-era small business relief programs.” The agency pointed specifically to its Paycheck Protection Program and COVID-19 Economic Injury Disaster Loan program as areas previously beset by fraud. SBA Administrator Kelly Loeffler said in a statement that the Palantir partnership “will strengthen our ability to expose fraudulent actors, support criminal enforcement actions, and recover stolen funds with advanced technology and artificial intelligence.” “No amount of fraud is acceptable — whether it is $10,000 or $10 million — which is why the SBA is deploying these tools to accelerate our work to surface wrongdoing and ensure those who cheated taxpayer-funded programs face consequences,” she added. The National Institutes of Health selected Kristen Honey, a longtime government data and technology official, to head up its coordination of public-private research partnerships. In a social media post, the Office of the National Coordinator for Health IT announced Honey as the inaugural official in the NIH role. As part of her duties, Honey will help “to build robust partnership models, reduce duplication, improve transparency, and move promising ideas from concept to execution with greater speed and consistency in collaboration with” ONC and across the department, per the post. The chief partnerships officer role is housed in the Office of the Director's Division of Program Coordination, Planning, and Strategic Initiatives, per the post. “The ‘wicked problems' that I run toward—complex, interdisciplinary challenges no one wants to own and that require cross-sector solutions—just got bigger. Joining NIH as Chief Partnerships Officer,” Honey said in a LinkedIn post. Honey has served in various HHS and White House roles over the past decade — including as HHS's chief data officer. Honey was initially installed as CDO after President Joe Biden's administration reorganization of its IT, data and artificial intelligence portfolio. Her time as CDO, however, appears to have ended after the Trump administration undid that reorganization in March, per her LinkedIn. Since then, she has listed her role as senior executive service. Currently, Arman Sharma, HHS's deputy chief AI officer, is listed as the agency's top data official on the CDO Council webpage. The Daily Scoop Podcast is available every Monday-Friday afternoon. If you want to hear more of the latest from Washington, subscribe to The Daily Scoop Podcast  on Apple Podcasts, Soundcloud, Spotify and YouTube.

In Episode 15 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom was joined for the third time by Vin Vashishta, CEO and Founder of V-Squared, where they discuss why the organisations getting the most value from AI are focused less on models and use cases, and more on outcomes, information architecture and business transformation, which includes;Why AI strategy has become a revenue growth strategy rather than a technology strategy.Why AI is an information product that depends on context, information architecture and data.Why information flywheels will become the defining capability that separates AI leaders from everyone else.Why organisations are moving from buying AI products to forming outcome-based partnerships with technology and consulting providers.Why CEOs and CFOs are now demanding clear links between AI investment, business outcomes and shareholder value.Why meaningful AI ROI requires organisations to transform operating models rather than simply automate existing processes.Why the fastest-growing organisations are extracting the greatest value from AI by creating entirely new forms of value.Why organisations such as JPMorgan Chase and Eli Lilly are turning AI into sustainable competitive advantage.How organisations can begin building information flywheels.Why technical strategy is becoming a core capability for both executive leaders and technical practitioners as traditional management layers disappear.Why ownership of commercial outcomes matters far more than whether AI sits with the CIO, CDO or a Chief AI Officer.Why robotics, autonomous systems and edge AI could soon eclipse today's generative AI conversation.Why LLMs will become just one small component within far more sophisticated agentic systems.Why we'll see LLMs diminish in importance.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.

Didier Mamma est Chief Data & AI Officer chez Decathlon, l'entreprise préférée des Français qui compte plus de 100 000 collaborateurs, réalise 17 milliards d'euros de chiffre d'affaires et est présente dans 54 pays dans le monde.On aborde :

Dave Trepanier's path into structured credit wasn't linear. Growing up on a French-Canadian farm in rural Ontario, he learned grit, teamwork, and how to make decisions when outcomes are uncertain—lessons that would later show up in an unlikely place: the earliest days of the CLO market. Now Global Head of GCSS-Structured Products in FICC Trading at Bank of America, Dave has helped build one of the industry's leading CLO and CDO trading franchises while navigating every major modern credit cycle.In this episode, Dave walks through the long road from political science and law school plans to financial engineering, options markets in Chicago, and a pivotal move to Charlotte—where CLOs were still modeled by hand in Excel off faxed trustee reports. We discuss what those “stone age” workflows taught him about risk, liquidity, and market structure, how the product evolved through telecom and the GFC, and why electronification, data, and systematic strategies may define the next chapter of credit markets.

In dieser insights! Folge spricht Joubin Rahimi mit Alexander Cohrs, CIO und CDO der Ostfriesischen Tee Gesellschaft (Meßmer, Milford, Onno Behrends), über seinen ungewöhnlichen Weg vom Marketing in die IT-Führung, seine Jahre in Korea und Russland und die wichtigsten Hebel der digitalen Transformation im Mittelstand. Du erfährst, warum die Doppelrolle aus CIO und CDO bei der OTG bewusst zusammengeführt wurde, wie KI auf Quick Wins statt auf perfekte Lösungen setzt und warum Governance kein Bremsklotz, sondern die Grundlage für mutiges Arbeiten ist. Plus: Was ein Konzern mit 8 Milliarden Teebeuteln pro Jahr über Brückenbau zwischen IT und Business gelernt hat.

América Latina no está atrasada en IA: está descoordinada. Tito Neira conversa con Michael Collemiche, presidente y fundador de CDO LATAM (comunidad de +18,000 seguidores en 16 países), sobre por qué la región tiene talento, demanda y datos, pero le falta poder de negociación. Hablan de la brecha de inversión, la nube como "el acueducto", los sesgos que pueden costar vidas y la pregunta que define todo: ¿estamos en la mesa de las discusiones o estamos en el menú?00:00 Intro03:06 La brecha de inversión04:53 El talento existe pero se fuga07:51 ¿Por qué seguimos consumiendo?08:21 Sesgos: data del norte10:21 La nube como acueducto12:00 Data centers: agua y energía14:24 Consumo masivo vs valor17:18 Dónde sí se captura valor20:45 Ventaja de los que ya explotaban data23:03 El riesgo sin gobierno de IA25:53 El rol del CDO se transforma26:52 Sesgos críticos: salud y justicia30:53 Transferencia tecnológica: ley de Chile34:00 De consumidores a generadores37:33 Pensamiento crítico y espejitos39:25 ¿En la mesa o en el menú?41:26 Cierre✩ CURSOS DISPONIBLES

Most companies don’t have a technology problem. They have a complexity problem. In this episode of Technovation, Peter High speaks with Gus Shahin, EVP of Business Technology and Operations at NetApp, about consolidating IT, operations, global security, and enterprise process under a single leader. Shahin joined NetApp in 2024 after 24-plus years at Flex, where he served as CIO and led global supply chain. He argues that transformation stalls on change management, not technology, and that the real goal of enterprise AI is not a handful of pilots but an AI-first culture. Key Highlights: Why consolidating four functions under one leader beats siloed CIO, CDO, and CTO roles How NetApp launched 29 AI projects and deliberately killed 11 to 12 mid-year Why the shift to inferencing makes storage performance the new bottleneck How “fail fast” became a transformation lever, not a slogan Where buy-versus-build is shifting, and the hidden cost of building your own This episode is presented by Celonis — Give AI the context it needs. Learn more at celonis.com/technovation

Subscribe to This Week in Hospitality wherever you get you podcasts: Spotify - https://open.spotify.com/show/5oPExA0txHMjEI5Ye13IUy Apple Podcasts - https://podcasts.apple.com/us/podcast/this-week-in-hospitality/id1849637233 Youtube - https://www.youtube.com/@ThisWeekinHospitality   Sean O'Neill of Skift joins the roundtable this week for a conversation that cuts straight to the fault lines running through the hotel industry right now — distribution versus identity, brand proliferation versus brand meaning, and the wellness promise versus wellness delivery. The panel leads with Pali Society's decision to bring its 16-property California portfolio into Marriott's Design Hotels ecosystem — the single largest addition in the program's history. Edwin draws the line everyone in the independent space is afraid to say out loud: using Marriott as a marketing channel is smart; slowly operating for Marriott guests instead of your own is how you lose the thing that made you worth joining in the first place. Scott is blunter: "Every owner in the world loves independence until they have empty rooms." From there, Sean's own Hilton story lands on the table. Hilton's new CDO is signaling five or six organic brands are coming — and a trademark filing, locked domain, and placeholder social accounts are already pointing toward a lifestyle concept called "Tortoise." The group debates whether the major brand groups, Hilton included, are actually delivering on their supposed superpower. Sean's take: in an AI-discovery world, the question isn't how many brands you have — it's whether any of them mean anything. The conversation gets sharper on wellness — a category where nearly everyone claims the space and very few actually own it. Ben makes the obvious argument: if Oura can sync with fitness apps, why can't Canyon Ranch pull your health data? Edwin adds the counterweight — the freedom to do nothing is the next frontier that nobody's building for. Plus: Soho House gets called out for failing to remember Edwin's green tea order after ten years, Scott's Venice discovery about a pool built in feet instead of meters, and Zach's verdict on San Antonio as the most underrated hotel market in America.   This Week in Hospitality is presented to you by Journey. Journey is a loyalty platform built specifically for independent boutique hotels and high-touch hospitality brands. Our mission is to give operators the same powerful rewards engine, data intelligence, and guest insights that major chains rely on — without asking them to give up the individuality, soul, or story that makes their property extraordinary. If you're an owner or operator of an extraordinary, independently owned and operated hotel or residence — and you want to see whether your property is a fit for the Journey Alliance — you can learn more and apply at https://www.journey.com/alliance   Key Topics & Timestamps 00:00 — Intro 38:13 — Story #1: Palisociety Joins Marriott's Design Hotels 49:52 — Story #2: Hilton's New Brand Factory 01:01:51 — Spice of the Week   Your Hosts: Zach Busekrus — Journey LinkedIn: https://www.linkedin.com/in/zachbusekrus/ Instagram: https://www.instagram.com/behindthestays/   Scott Eddy — Global Travel & Hospitality Expert @MrScottEddy LinkedIn: https://www.linkedin.com/in/mrscotteddy/ Instagram: https://www.instagram.com/mrscotteddy/   Ben Wolff — Founder of Onera & Oasi LinkedIn: https://www.linkedin.com/in/ben-wolff/ Instagram: https://www.instagram.com/iambenwolff/   Edwin Kramer — Luxury Hotelier Consultant & Former GM LinkedIn: https://www.linkedin.com/in/edwinckramer/ Instagram: https://www.instagram.com/edwinkramer/

Send us Fan MailAI is finally in everyone's hands, and that's exactly why it's getting risky. Grant McGaugh sits down with veteran tech leader Darrell T. Black (CIO, CTO, CDO) to discuss what it really takes to bring AI into a business without compromising trust, compliance, or the back office. We get into the difference between experimenting with a chatbot and deploying AI inside the systems that run payroll, HR, finance, and customer data.Darrell shares a grounded view of AI readiness: understand your current processes, map the real workflow (not the version on paper), define the future state, then conduct a gap and impact analysis before you automate anything. We also unpack why AI hallucinations matter, how probabilistic AI differs from deterministic technology, and why “it sounds good” is not the same as “it's accurate.” If you're thinking about agentic AI, this is where the conversation gets practical about audit trails, human oversight, and governance that can scale.We also tackle the bigger societal layer: bias in algorithms, safeguards in hiring and healthcare, and why policy often sets the tone for enterprise behavior. And Darrell leaves us with a simple warning you'll remember: you might think you're adopting an AI puppy, but you're responsible for the full-grown AI dog. If you're a founder, operator, or IT leader trying to use AI to scale responsibly, this one is for you.Subscribe for more conversations like this, share the episode with a friend building with AI, and leave a review with the biggest AI risk you want us to tackle next.Thanks for tuning in to this episode of Follow The Brand! We hope you enjoyed learning about the latest trends and strategies in Personal Branding, Business and Career Development, Financial Empowerment, Technology Innovation, and Executive Presence. To keep up with the latest insights and updates, visit 5starbdm.com.And don't miss Grant McGaugh's new book, First Light — a powerful guide to igniting your purpose and building a BRAVE brand that stands out in a changing world. - https://5starbdm.com/brave-masterclass/See you next time on Follow The Brand!

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 David Krauza, VP of Enterprise Data Strategy, Products & Governance at Comcast, diving deeper into the "arts and crafts" trap that derails data programmes, the discipline of building stakeholder trust before you need it, and what it really takes to drive the bus rather than ride it.They cover:Why David's mandated business school module ended up shaping his outlook on data leadership, and the recurring pattern of guests whose commercial thinking was forged outside a purely technical backgroundThe "arts and crafts project" analogy David's boss used to describe technically impressive work that never moves the needle, and why naming the difference between process-enjoyment and outcome-focus matters as much in data as it does in any creative pursuitWhy so much of the data and AI ecosystem gravitates toward exciting new models, tools, and techniques without tying the work back to specific goals, decisions, and KPIsDavid's framing that you have to help people before you need their help, and why the leaders who consistently land the biggest roles are the ones already putting into their networks and communities long before they need anything backCatherine's take on why this same principle defines external brand building, and why leaders who wait until they're job hunting to invest in relationships are always playing catch-up against those who started years earlierKyle's view that building relationships with future stakeholders is not a side project for a data leader, it is the job, every bit as much as overseeing platform delivery, governance, and architectureWhy David reframed the trust gap many data leaders face as a sequencing problem rather than a communication problem, and what that distinction means for how and when leaders should be reaching outThe "bus riders and bus drivers" analogy at the heart of the episode title, and why organisations hire a data leader precisely because they don't already know the answer, making it the leader's job to shape direction rather than simply execute instructionsWhy being a strong, detailed communicator changes the entire dynamic of a hiring conversation, and how that same skill plays out with stakeholders once someone is in the roleCatherine's practical tip for building interview and communication confidence using AI tools like ChatGPT or Claude as a low-stakes practice partner, and why consistent repetition beats waiting for natural talent to show upKyle's thought of the week: prompted by a message from a CDO at a crossroads in their career, Kyle reflects on why the CDO role isn't disappearing or resurging industry-wide so much as it's becoming entirely dependent on whether a business's leadership views data as a commercial value-creation function or a technology delivery capability. Where it's the latter, that responsibility increasingly sits with the CIO, and Kyle notes the early signs of broader transformation-style mandates emerging that fold CDO, CIO, and Chief AI Officer responsibilities into a single board-level role.This episode explores what it actually takes to drive value rather than just deliver outputs, the discipline of investing in relationships long before you need them, and why naming the gap between busywork and real impact is often the first step to closing it.

In Episode 11 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom was joined by David Krauza, VP of Enterprise Data Strategy, Products & Governance at Comcast, where they discuss why strategic clarity and proactive stakeholder engagement are the keys to unlocking genuine business value from data and AI, which includes;Why the root cause of failed AI and data programmes is almost never the technology and almost always the absence of a clear business outcome.How to tell the difference between an organisation that has genuine strategic clarity and one that just has a compelling PowerPoint.Why a strategy without explicit trade-offs, knowing what you are not going to do, is no strategy at all.How "arts and crafts" projects quietly drain data programmes of focus, credibility, and commercial impact.Why retrofitting goals around work already underway creates a circular dependency that pulls organisations further from real value.Why the "bus riders and bus drivers" framework reframes what it means to be an effective data leader.Why waiting for perfect conditions before driving impact is one of the most common and costly habits of data leaders.How proactively building relationships with CFOs, COOs, and business unit heads before you need them is what separates influence from scrambling.Why the trust deficit most data leaders face is a sequencing problem, not a communication problem.How starting within your own team or with a single friendly stakeholder is the most practical way to begin building the bus driver muscle.Why most CDO mandates are structurally designed to deliver outputs rather than value and how that shapes the type of leader organisations end up hiring.How to navigate a broken mandate in practice and why challenging it in the interview room is riskier than it sounds.Why the incentive structures within data leadership roles have historically rewarded technical delivery over commercial impact.Why the data industry's technical origins created an archetype that is now working against the commercial value organisations actually need.How company size and culture determine whether data is treated as a strategic asset or an internal IT service and why that changes everything.Why organisations that started their data journey for the wrong reasons often find the perception too deeply embedded to shift from within.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.

Your people aren't tired of change — they're saturated. There's a difference, and it's the difference between an AI rollout that lands and one that bounces off your workforce entirely. Kelle Fontenot is the Chief Digital Officer at KPMG US, where the CIO, the CTO, and the Chief Data Officer all report to her. She owns internal innovation, architecture, platform, engineering, and data across a 40,000-person workforce — and she's spent the last four and a half years steering that organization through cloud, data, and now an AI wave reshaping how every one of her people does their job. In this conversation, Kelle reframes 'change fatigue' as 'change saturation,' reveals that KPMG employees built 25,000 AI agents in the last six months alone, walks through the synthetic-data acquisition powering regulated AI testing at scale, and explains the brand-new Anthropic partnership turning a 140-year-old services firm into a products company.   What you'll learn • Why 'change fatigue' is the wrong diagnosis — and what 'saturation' changes about how you roll out AI • Why KPMG refuses to use AI as a head-count lever — and why that decision is actually accelerating adoption • How 40,000 KPMG employees built 25,000 AI agents in six months — and what that means for who counts as a 'builder' • Why the CIO, CTO, and CDO all report to one person — and what would break if they didn't • How synthetic data lets a regulated firm test AI at scale without the breach risk • What KPMG's Anthropic partnership signals about the future of professional services   Connect Kelle Fontenot on LinkedIn KPMG US IT Visionaries Podcast   Chapters 0:00 AI Change Has Become AI Saturation 1:29 Why “Change Fatigue” Is the Wrong Diagnosis 3:27 Prompting Like It's November 4:46 Giving People Space to Innovate 6:38 AI Is Not a Headcount Lever 10:07 Building AI in a Regulated Business 11:24 The Risk Container Around AI 14:12 The AI-Augmented Auditor 17:21 The Agent Governance Problem 20:59 Why Digital, Data, and Tech Sit Together 22:59 Building an Inside Startup 30:04 Innovation Has to Happen at the Edge 36:48 The ROI Math for AI Agents 38:50 Why KPMG Bought a Synthetic Data Company 44:09 KPMG's Anthropic Partnership 51:03 Shipping AI at Scale 52:10 Kelle Fontenot's Advice for Leaders -- This episode of IT Visionaries is brought to you by Meter - the company building better networks. Businesses today are frustrated with outdated providers, rigid pricing, and fragmented tools. Meter changes that with a single integrated solution that covers everything wired, wireless, and even cellular networking. They design the hardware, write the firmware, build the software, and manage it all so your team doesn't have to.That means you get fast, secure, and scalable connectivity without the complexity of juggling multiple providers. Thanks to meter for sponsoring. Go to meter.com/itv to book a demo.---IT Visionaries is made by the team at Mission.org. Learn more about our media studio and network of podcasts at mission.org. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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 Sarah Emerson, Group Director of Insight & Business Partnering at Howden, diving deeper into the growing importance of commercial thinking, business partnering, and the role relationships play in driving value from data.They cover:Why Sarah's background in finance and corporate strategy offers a unique perspective on data leadership, and how commercial acumen can become a powerful differentiator for leaders looking to influence organisational outcomesThe challenge of connecting data strategy to business strategy, why many organisations struggle to articulate strategic priorities clearly, and the practical ways data leaders can uncover them regardlessWhy curiosity about business value shouldn't be reserved for senior leaders, and how analysts at every level can develop a stronger understanding of commercial impactThe growing importance of business partnering as a dedicated capability, and how organisations can bridge the gap between technical teams and business stakeholders more effectivelyThe realities of operating model design, why federated approaches continue to gain traction, and the trade-offs organisations must consider when balancing proximity to the business with cost and complexitySarah's view that self-service analytics has largely failed to deliver on its original promise, and what that means for the future of data enablement and adoptionWhy understanding how business leaders are measured, incentivised, and rewarded can dramatically improve stakeholder engagement and increase adoption of data-led initiativesThe challenges of discussing performance, incentives, and accountability within organisations, and why trust and relationship-building remain critical leadership skillsThe evolving role of the Chief Data Officer, the increasing consolidation of data responsibilities back into CIO organisations, and what this shift could mean for the future of data leadershipHow AI has accelerated organisational debates around ownership, accountability, and transformation, with many businesses still determining where responsibility ultimately sitsThe emergence of broader transformation and innovation leadership roles that combine data, technology, AI, digital, and business transformation under a single mandateKyle's thought of the week: as more organisations place data leadership responsibilities back under the CIO, many of the lessons learned throughout the evolution of the CDO role risk being forgotten. The challenge now is ensuring that value creation, business engagement, and commercial impact remain at the centre of the agenda, regardless of where accountability sits.This episode explores the realities of commercial leadership in data, the importance of business partnering, and why understanding people, incentives, and organisational dynamics is often just as important as understanding data itself.

Rural healthcare systems across the U.S. are facing a growing physician shortage—and traditional solutions aren't enough. In this episode, Stewart Gandolf is joined by Dr. Kenneth Holmen, President and CDO of CentraCare, to discuss how his organization is tackling the problem head-on by helping launch the first new medical school in Minnesota in over 50 years. From workforce shortages and aging populations to siloed systems and outdated thinking, they explore what's driving the crisis—and how a new, community-centered model for education and healthcare delivery could help solve it.

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 Keith Moody, diving deeper into the realities of value creation, stakeholder management, and why the biggest barriers to success in data leadership are often human rather than technical.They cover:Why Keith's candid perspective stood out, and how some of the most honest conversations happen when leaders are able to speak without the constraints of corporate messaging and organisational politicsThe critical relationship between the CDO and CFO, why finance leaders remain the ultimate validators of value, and how a single nod of approval can determine whether an initiative succeeds or stallsWhy proving value still remains the defining challenge for data leaders, despite years of discussion around ROI, business outcomes, and commercial impactThe importance of stakeholder management, trust-building, and relationship development, and why no data leader succeeds without bringing others along on the journeyHow AI can be used as a practical leadership tool, from role-playing difficult stakeholder conversations to helping leaders navigate conflict, influence, and executive communication more effectivelyThe emerging ways data and AI leaders are using AI personally, including as a career coach, meeting assistant, productivity partner, and accessibility toolWhy change management isn't a phase of transformation programmes but the job itself, and how successful leaders recognise that adoption is an ongoing responsibility rather than a project milestoneThe reality that humans remain the most complex variable in any data strategy, and why technical excellence alone will never guarantee successHow previous experiences, organisational history, and leadership baggage influence every new data leader entering a role, whether they're inheriting success, failure, or scepticismWhy data leadership increasingly resembles sales, and how influencing decisions often requires changing perceptions, behaviours, and long-held beliefs rather than deploying new technologyThe growing importance of real-world communities, events, and human connection as AI-generated content becomes more prevalent and increasingly difficult to distinguish from human-created workWhy curiosity and imagination may become the defining skills that separate high-performing leaders in an era where access to technology becomes increasingly democratisedKyle's thought of the week: whilst many organisations claim they lack a clearly defined business strategy, the reality is that strategic priorities almost always exist somewhere. The responsibility for data leaders is to uncover them, build relationships with the people who own them, and connect their work to those outcomes rather than waiting for perfect documentation to appear.Catherine's thought of the week: we often have more control than we think. Whether it's improving stakeholder relationships, influencing difficult conversations, or navigating organisational complexity, the leaders who make progress are typically those willing to take ownership, seek support, and proactively shape their environment rather than waiting for conditions to improve.This episode is a practical discussion on the realities of leading change, proving value, and navigating organisational complexity, whilst exploring how human behaviour, relationships, and influence continue to matter just as much as technology in determining success.

In Episode 9 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom was joined by Keith Moody, a renowned Data & AI Executive, where they discuss the relationship between the mandate of the CDAO and the results that data and analytics teams continue to deliver, which includes;How the absence of a clear value delivery mandate is the root cause of data being treated as a cost centre rather than a business asset.Why most CDO mandates are designed based on what organisations think the job is, not what it actually needs to be to generate commercial impact.How Keith built four analytics organisations across two companies and delivered over $500 million in incremental annual value.Why the data function's reporting line is rarely neutral, and how a once-thriving value-delivery team was reduced to an order-taking service desk.How convincing leadership that "no analytics could happen until data was perfect" brought an entire organisation to a standstill.Why CDOs who push for commercial accountability in interview processes face a catch-22.Why most interview processes focus on capability over delivery expectations leaving the value mandate undefined from day one.Why you should actively push for commercial targets.Why and how to routinely reframe the mandate once inside an organisation.Why the CFO should be the ultimate validator of any value numbers attributed to analytics.How reporting directly to the CEO removes prioritisation deadlock entirely.Why governance committees are a poor substitute for having a single accountable decision-maker at the top.How change management done at the end of a project is the single biggest reason analytics initiatives fail.Why blanket data literacy programmes are largely an admission of failure.How automating decision-making away from VPs was successfully sold internally.Why the AI investment cycle is repeating the exact same hype and collapse pattern seen with data science.How FOMO, shareholder pressure, and competitive optics drive organisations to invest in AI capabilities they haven't defined a use case for.How the first practical step for any CDO stuck in cost-centre mode is to audit their existing portfolio and how to do so.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.

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.

Why Most Enterprise AI Projects Hit a "Value Ceiling" — And How to Break Through | Dr. Fern HalperWhat separates the companies actually winning with AI from the ones burning budget on chatbots that go nowhere? In this upcoming episode of Redefining AI, host Lauren Hawker Zafer sits down with Dr. Fern Halper — VP of Research at TDWI, Founder of the AI Foundations Group, former Bell Labs lead analyst, and one of the most respected voices in enterprise AI strategy — to unpack the ideas behind her highly anticipated new book, Data Makes the World Go 'Round: The Data, Tech, and Trust Behind AI Success.With over 30 years bridging deep technical execution and C-suite strategy, Dr. Halper explains why so many organisations are stuck chasing hype instead of value, and what it actually takes to move AI from lab experiments into production systems that drive real ROI.Inside this upcoming episode, you'll learn:Why generative AI hits a "value ceiling" without trusted, governed data foundationsThe execution traps that sank AI initiatives at Zillow, Amazon, and othersHow data lakehouses and data fabric architectures unify siloed data for AIWhy MLOps is so hard — and why every model eventually degradesThe critical difference between data governance and AI governanceHow agentic AI changes the risk equation when systems start taking autonomous actionsThe shift from controlling what AI produces to overseeing what AI doesHow to tie AI use cases to measurable KPIs instead of vanity metricsEmbedding fairness, explainability, and EU AI Act compliance without killing innovationDefending against shadow AI while democratising analytics across the businessWhether you're a CDO, CIO, VP of Data, AI product leader, or a business executive under pressure from your board to "do something with AI," this is the strategic playbook you've been waiting for.

Why Most Enterprise AI Projects Hit a "Value Ceiling" — And How to Break Through | Dr. Fern HalperWhat separates the companies actually winning with AI from the ones burning budget on chatbots that go nowhere? In this upcoming episode of Redefining AI, host Lauren Hawker Zafer sits down with Dr. Fern Halper — VP of Research at TDWI, Founder of the AI Foundations Group, former Bell Labs lead analyst, and one of the most respected voices in enterprise AI strategy — to unpack the ideas behind her highly anticipated new book, Data Makes the World Go 'Round: The Data, Tech, and Trust Behind AI Success.With over 30 years bridging deep technical execution and C-suite strategy, Dr. Halper explains why so many organisations are stuck chasing hype instead of value, and what it actually takes to move AI from lab experiments into production systems that drive real ROI.Inside this episode, you'll learn:Why generative AI hits a "value ceiling" without trusted, governed data foundationsThe execution traps that sank AI initiatives at Zillow, Amazon, and othersHow data lakehouses and data fabric architectures unify siloed data for AIWhy MLOps is so hard — and why every model eventually degradesThe critical difference between data governance and AI governanceHow agentic AI changes the risk equation when systems start taking autonomous actionsThe shift from controlling what AI produces to overseeing what AI doesHow to tie AI use cases to measurable KPIs instead of vanity metricsEmbedding fairness, explainability, and EU AI Act compliance without killing innovationDefending against shadow AI while democratising analytics across the businessWhether you're a CDO, CIO, VP of Data, AI product leader, or a business executive under pressure from your board to "do something with AI," this is the strategic playbook you've been waiting for.

Juan Fredo brings Bisaya comedy straight from CDO to your feed. A dad of four, OFW, dance instructor, and now 36K-followers-strong creator, he talks reels, local slang, family life, and finding his comedy lane after years of trying.

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 Dru Patel from the FA, diving deeper into the human side of data leadership, from storytelling and self-awareness to the commercial realities of what it actually takes to succeed at the executive level.They cover:Why Dru Patel's approach to storytelling and communication stood out as one of the most compelling conversations the podcast has hosted to dateHow technical capability alone has become “table stakes” in data leadership, and why the differentiator is now influence, communication, and the ability to shape perceptionWhy “soft skills” might be the most damaging phrase in the industry, and how cultural buy-in and human-centred leadership are often the real drivers of ROIThe uncomfortable reality that working hard and being technically brilliant doesn't automatically lead to progression, and why self-awareness is becoming a critical leadership traitHow data leaders can shift conversations away from platforms, dashboards, and governance, and toward decisions, business outcomes, and commercial impactWhy organisations still struggle with the perception of data teams as back-office technical functions, and how that perception shapes hiring, mandates, and ultimately failureThe difference between data literacy and data culture, and why culture is what happens when nobody is watchingHow lived experience, industry context, and organisational history shape expectations around data quality, trust, and value creationWhy many CDO mandates continue to fail, not because the individuals lack capability, but because organisations hire for technical delivery while expecting commercial transformationThe growing disconnect between what data leaders are hired to do and what boards actually expect them to achieveThey also dig into the future of data leadership and organisational accountability:Why businesses are now entering their third, fourth, and even fifth iteration of the CDO role, and what those repeated resets reveal about the maturity of the marketHow hiring behaviour has unintentionally incentivised technical specialisation over commercial leadership for more than a decadeWhy asking questions around decisions, KPIs, revenue targets, and business performance during interviews can quickly reveal an organisation's true perception of data leadershipKyle's thought of the week: why the debate around failed CDO mandates is becoming too polarised between “it's the organisation's fault” and “it's the individual's fault,” and why the reality sits somewhere in the middleCatherine's thought of the week: what happened after asking LinkedIn for web developer recommendations, and what the overwhelming response revealed about vendor outreach, personalisation, and the growing problem of AI-generated sales noiseThey also discuss:Why AI has enabled many organisations to operate “badly at scale, but faster”How senior leaders increasingly avoid broad vendor engagement unless there is an immediate needThe importance of building trusted communities where candid conversations can happen openly and safelyWhy Orbition Group's private membership community continues to grow as leaders look for more meaningful peer-to-peer discussion away from the public spotlightThis episode is a candid exploration of the skills gap that rarely gets discussed in data and AI leadership, not the technical gap, but the commercial, cultural, and human capability gap that increasingly determines who succeeds, who gets overlooked, and why so many organisations still struggle to realise value from data.

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

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 what's really happening across the data and AI landscape.This week, Catherine and Kyle reflect on their conversation with Richard Masters, VP of Data & Analytics at Virgin Atlantic, diving deeper into the themes that matter most right now, from decision-led data strategies to the realities of building for reusability in an AI-driven world.They cover:Why Virgin Atlantic's surprisingly lean fleet of 44 planes is a masterclass in doing more with less, and what data teams can learn from itRichard's astrophysics background and how the principle of signal over noise shapes his entire approach to dataWhy the North Star of any data function should be decision support, and how working backwards from decisions changes everythingThe shift from "collect all the data" to "what decisions are we trying to impact" — and why that transition is still hard for most organisationsThe move from single-use data projects to reusable, scalable products, and why building for one use case is the old way of thinkingHow AI is democratising business capability, the rise of the "builders vs coders" mindset, and what that means for how data teams are structuredWhy fewer platforms, used well, will beat a sprawling vendor stack, and what that means for the vendor community going forwardThey also dig into the future of talent and skills in data:Why critical thinking, curiosity, and imagination are becoming more valuable than technical qualificationsHow the widening talent pool challenges universities and educators to stop being anti-AI and start teaching people how to use it responsiblyWhy neurodiversity and unconventional backgrounds will be a competitive advantage in an AI-augmented worldCatherine's thought of the week: why data professionals have a duty to educate those around them as AI misinformation spreads, from the boardroom to the toddler groupKyle's thought of the week: why the CDO role may be heading toward a fractional, advisory model, and what that split between strategy and execution means for the future of data leadership hiringThis episode is a candid look at where data is heading, where the real value is created, and why the leaders who thrive will be the ones who connect commercial strategy to the decisions that actually move the needle.

I'm back after my wedding and honeymoon in March—and I'm coming in with a renewed mindset.One thing it reminded me of was how much I get out of this show. This podcast has always been about three things: gaining understanding, building community, and elevating voices in our field. I hope that experience is mirrored for all of you.This week, Wendy Connors talks with me about her professional journey and her shift from CDO to CEO—and the different levels of stress that come with it.What it means to work differently.What real work-life balance actually looks like. How to recognize good work. What happens when your database grows fast—and expectations grow even faster.Wendy Connors is the President of the Fannie and John Hertz Foundation. She is a passionate leader and advocate for science, technology, and education. As president of the Hertz Foundation, she leads the nation's most prestigious doctoral fellowship in applied science, engineering, and mathematics equipping exceptional students with unparalleled resources and lifelong support.

Tito Neira y Oz analizan por qué el CDO no murió, está en juicio. 70% de los CDAOs ya lidera la estrategia de IA (Gartner) pero 74% de empresas aún no ve valor tangible (BCG). En este episodio Tito comparte qué buscan los líderes de data hoy en sus equipos, por qué el AI Engineer está mal definido en la mayoría de empresas, y un roadmap de 12 meses para defender o rediseñar el rol del CDO en la era de la IA.00:09 Intro y bienvenida01:45 Roles tradicionales y cómo evolucionan03:38 ¿El CDO está en juicio?04:17 Dónde debería vivir el CDO en la organización06:25 Cómo cambió el día a día con IA generativa09:37 Equipos de data como cuello de botella11:30 70% de high performers dice: los datos son el desafío #114:02 Bases productivas, réplicas y agentes proxy16:40 Visualizaciones simples vs complejas con IA19:36 Qué buscar en perfiles de data hoy23:17 Roles que mutan dentro del equipo de data25:37 Job description real del AI Engineer30:49 CDO ofensivo (85.5%) vs defensivo (14.5%)33:55 Cómo entrevistar para visión de negocio40:22 Recomendaciones para futuros CDOs45:26 Cierre✩ CURSOS DISPONIBLES

In this Omni Talk Retail interview, recorded live from World Retail Congress 2026 in Berlin, Chris Walton speaks with Brian Tilzer, former CTO and CDO of Best Buy and current Board Member at Signet Jewelers, about why AI represents retail's “third major technology wave” and what retailers can learn from the ecommerce and mobile revolutions that came before it. Drawing on leadership experience across Best Buy, CVS Health, Staples, and now Signet Jewelers, Brian explains why the retailers that win with AI won't simply use it to drive efficiency, but instead to create more human, personalized, and responsive customer experiences. The conversation explores how AI can help retailers better understand customer intent, empower frontline employees with richer information, and accelerate decision-making across the organization. Chris and Brian also discuss why traditional retail planning cycles may no longer move fast enough for the pace of AI innovation, how adaptive cross-functional operating models are becoming essential, and why testing, learning, and iteration will define the next generation of retail leaders. Throughout the conversation, one theme remains clear: AI may transform retail operations, but human connection will remain the industry's greatest differentiator. Key Topics Covered: • Why AI represents retail's “third major technology wave” • Lessons retailers can learn from ecommerce and mobile transformation • How AI can create more human customer experiences • Why frontline employees become even more important in an AI-driven world • The role of AI in understanding customer intent and personalization • Why retailers need faster planning and decision-making cycles • How adaptive operating models will shape future retail organizations • Why testing, learning, and iteration matter more than ever • The importance of combining digital, physical, and human retail experiences • How AI can help retailers solve long-standing operational challenges Thank you to Vusion for supporting Omni Talk Retail's live coverage from Berlin. #WorldRetailCongress #WRC2026 #OmniTalkRetail #AIinRetail #RetailInnovation #RetailTechnology #CustomerExperience #FutureOfRetail #DigitalTransformation #RetailLeadership

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

Join this episode of DM Radio, as host Eric Kavanagh speaks with Mark Brady, Chief Data Officer of TRMC/KBR, ex CDO for US Space Force, about the rapidly evolving landscape of artificial intelligence and the urgent need for clearer safety principles.  Hi will share his perspective on how intelligence works across humans and machines, from perception and memory to language and reasoning, and why today's AI systems challenge traditional definitions.  Learn more about the risks of opaque "black box" models and why understanding their behavior is critical as they become more powerful and widely deployed.

AI agents are appearing across every enterprise platform, but most still struggle to move beyond scripted automation into systems that can reason, adapt, and operate within real workflows.On this episode of Ctrl + Alt + AI, Dimitri Sirota,  speaks with Justin Heller, former Chief Data Officer at Synchrony Financial and Chief Data & AI Officer of Quantify Data Advisors, about how organizations can leverage their existing data to reduce cyber risks, manage unstructured data, and integrate AI effectively. Justin, formerly the Chief Data Officer at Synchrony Financial, shares insights on the evolving role of data governance in an AI-driven world and the importance of shifting from a "pilot" mentality to creating sustainable AI-driven business value. Tune in as they unpack the complexities of managing both structured and unstructured data, ensuring relevance, and achieving true data governance alignment with emerging AI technologies.What to expect:How organizations can use existing data assets to reduce cyber risks and enhance AI initiativesWhy relevance, not just accuracy, is the key to effective AI and data managementThe importance of connecting unstructured data, metadata, and AI systems for better decision-makingThings to listen for: (00:00) Meet Justin Heller(01:25) Justin's transition from CDO to data advisor(02:35) From structured to unstructured data in AI environments(04:24) Why context engineering is critical for AI-driven business decisions(06:00) Moving beyond AI pilot projects to sustainable value(08:30) How data stewards can work with AI tools(09:00) Integrating AI across existing business processes(10:03) Building governance models for unstructured data(13:00) AI in unstructured data repositories: Best practices(15:00) Measuring ROI from generative AI in enterprises(18:00) Cross-functional collaboration for effective AI implementation(20:00) The role of CDAOs in driving AI-related outcomes(21:30) Shifting from pilot programs to ongoing AI-driven business value

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

Craig Hepburn sits at the intersection of enterprise technology and cultural institutions. He spent years as UEFA's Chief Digital Transformation Officer, overseeing its digital ecosystem, OTT platform build, and Innovation Hub. He moved to Art Basel as CDO in 2023. He is now an independent AI strategist, Perplexity Fellow, and prolific writer on the structural implications of AI for organisations and industries. His Substack has become essential reading on the gap between AI hype and implementation reality.Hepburn's central thesis is that most people and organisations are “tourists in someone else's architecture.” He draws a sharp distinction between using AI (prompting chatbots, generating content) and building with AI (constructing proprietary systems, workflows and tools). He argues the latter is what will separate winners from losers — and that the window for making that shift is narrowing fast.Crucially, Hepburn's argument extends beyond sport. His recent writing on “The Builder and the Billion Dollar Lie” contends that entire industries — consulting, systems integration, transformation programmes — were built inside the gap between the person who understood a problem and the person who could build the solution. Agentic AI, he argues, is starting to close that gap. That has profound implications for the agency model in sport.Unofficial Partner is the leading podcast for the business of sport. A mix of entertaining and thought provoking conversations with a who's who of the global industry. To join our community of listeners, sign up to the weekly UP Newsletter and follow us on Twitter and TikTok at @UnofficialPartnerWe publish two podcasts each week, on Tuesday and Friday. These are deep conversations with smart people from inside and outside sport. Our entire back catalogue of 500 sports business conversations are available free of charge here. Each pod is available by searching for ‘Unofficial Partner' on Apple, Spotify and every podcast app. If you're interested in collaborating with Unofficial Partner to create one-off podcasts or series and live events, you can reach us via the website.

Picking a use case, proving value, and expanding has been the standard starting point for enterprise AI. For organizations early in their AI journey, that advice still holds. But for large enterprises that are past the pilot stage and trying to scale across business units, geographies, and brands, it isn't enough.At NVIDIA GTC, Cameron Davies, Chief Data Officer of Yum Brands, shared how his team is thinking about AI differently — and why they had to. With 63,000 restaurant locations, 100 million daily transactions, and 1,500 franchisees across 155 countries, Yum operates at a scale where a single bad AI decision can fail loudly, repeatedly, and fast.In this episode, Maribel breaks down Davies' framework and what it means for how enterprise leaders should be thinking about AI in 2026 and beyond.---**What you'll learn**- Why the use case as a unit of AI planning has a structural limitation at enterprise scale- What "scalable AI skills" means and why it's different from building agents for specific use cases- Why governance has to come before deployment, not after — and what happens when it doesn't- How measurement functions as operational discipline, not just a reporting obligation- What Yum's AI flywheel looks like and why it only works if measurement is continuous- What this framework means for organizations that aren't Yum-sizedAbout Cameron DaviesCameron Davies is the Chief Data Officer at Yum Brands, the parent company of KFC, Taco Bell, Pizza Hut, and The Habit Burger Grill. He leads the company's corporate data and analytics strategy and oversees the development and adoption of advanced data capabilities. He previously spent seven years as SVP at NBCUniversal and over 18 years at The Walt Disney Company, where he led the Corporate Center of Excellence for AI and machine learning.---**Resources and references mentioned**-NVIDIA GTC session: "Scaling AI Agents Globally Across Brands, Use Cases, and Restaurants" (S81755) — Cameron Davies, Yum Brands- Responsible AI Institute — chaired by Manoj Saxena- Trustwise — AI trust startup founded by Manoj Saxena- Byte — Yum Brands' proprietary e-commerce, point-of-sale, and menu platform- Lopez Research blog: The Rules for Scaling AI Have Changed. Yum Brands Proved It. — [LINK]---

AI is forcing enterprises to rethink everything from hardware to governance, and most organizations are attacking it in silos. Mano Bhattacharyya (CTO, Nutanix) breaks down why AI isn't just an application layer problem, but an end-to-end transformation that spans compute (GPUs, ARM, DPUs), data management (unclean enterprise data, knowledge graphs), security (agent gateways, MCP server risks), and economics (token costs vs. usage explosion). He explains why CIOs need cross-functional AI committees, not isolated strategies, and why use case driven AI beats exploratory projects that burn budgets in months. The solution is to form AI committees where CIO, CTO, and CDO work together, not in silos. Focus on use case driven AI by learning from peers, rather than exploratory AI that becomes a budget trap. Start with small prototypes with dedicated use cases, not a free-for-all where every team tries something.   Chapters: 0:00 AI as an End-to-End Infrastructure Challenge 2:16 Networking, Storage, and Bare Metal VM Performance 4:43 Agent Security, Gateways, and Enterprise Governance 6:33 Use Case Driven AI and Learning from Peers -- This episode of IT Visionaries is brought to you by Meter - the company building better networks. Businesses today are frustrated with outdated providers, rigid pricing, and fragmented tools. Meter changes that with a single integrated solution that covers everything wired, wireless, and even cellular networking. They design the hardware, write the firmware, build the software, and manage it all so your team doesn't have to.That means you get fast, secure, and scalable connectivity without the complexity of juggling multiple providers. Thanks to meter for sponsoring. Go to meter.com/itv to book a demo.---IT Visionaries is made by the team at Mission.org. Learn more about our media studio and network of podcasts at mission.org. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The market for data leaders is growing - but the CDO role itself may be under pressure. In this episode, Malcolm Hawker and Kyle Winterbottom explore why organizations are questioning data leadership value, how AI is reshaping career paths, and what separates those who advance from those who stall. If you're navigating your next move in data, this is a conversation you can't afford to ignore.

This episode explores how boards and leaders can make smart, bold decisions in uncertain times. Thomas Zimmerer shares practical advice on embracing uncertainty, leveraging data and AI, and ensuring strong ownership. Real-world examples and actionable tips make it valuable for anyone facing fast-changing business environments. About the guest Thomas Zimmerer is a board member and AI & digital board advisor, working with Boards of Directors and supervisory boards on decision-making, governance, and long-term value creation under uncertainty. He brings global experience as CEO, CIO, COO and CDO across industrial, technology, and regulated environments in Europe and the Middle East, and serves on supervisory and advisory boards. His work focuses on how boards govern business models, risk, and strategic direction as AI reshapes decision environments. Thomas is a published author and regular contributor to German governance and risk-management journals, including Der Aufsichtsrat and the Zeitschrift für Risikomanagement. Relevant links https://www.aiizationboard.com https://www.linkedin.com/in/thomaszimmerer/ Key Take-Aways Make uncertainty explicit: Put unknowns and assumptions on the table to improve clarity and decision quality. Leverage existing data and AI: Use current tools and data to run scenarios and support faster, smarter decisions. Ensure strong ownership: Assign clear responsibility for decisions, including both success and failure. Chapters 0:00 - Intro 0:35 - Navigating Decision-Making in Uncertainty 1:34 - Vision and Focus Amid VUCA Challenges 3:20 - Understanding Decision-Making Under Uncertainty 3:55 - Explicitly Addressing Unknowns in Decision-Making 5:58 - Leveraging Technology and Data for Decisions 9:32 - Utilizing Existing Tools for Effective Decision-Making 11:36 - Ownership and Accountability in Board Decisions 17:39 - Embracing Risk and Learning from Decisions 20:34 - Case Study: Bold Investments in AI 24:58 - Learning from Unsuccessful Decisions 28:19 - Final Insights on Uncertainty and Leadership 30:40 - Conclusion and Audience Engagement Please, hit the follow button and leave your feedback: Apple Podcast: https://www.cxgoalkeeper.com/apple Spotify: https://www.cxgoalkeeper.com/spotify About the host: Gregorio Uglioni is a seasoned transformation leader with over 15 years of experience shaping business and digital change, consistently delivering service excellence and measurable impact. As an Associate Partner at Forward, he is recognized for his strategic vision, operational expertise, and ability to drive sustainable growth. A respected keynote speaker and host of the well-known global podcast Business Transformation Pitch with the CX Goalkeeper, Gregorio energizes and inspires organizations worldwide with his customer-centric approach to innovation. Follow Gregorio Uglioni on Linkedin: https://www.linkedin.com/in/gregorio-uglioni/

In this episode, we speak with Michael Henriques, Partner and Senior Portfolio Manager at Magnetar, a multi-strategy alternative investment manager with more than $22 billion in AUM. Founded in 2005, the firm invests across public and private markets in the U.S. and Europe, with a focus on alternative credit, fixed income, and venture strategies. Its Alternative Credit & Fixed Income business targets Specialty Finance, Structured Solutions, and Opportunistic Markets, seeking to generate attractive risk-adjusted returns, particularly during periods of market dislocation. Michael brings three decades of experience in fixed income, structured securities, and real estate. He joined the firm in 2007 and previously served as a Managing Director at Deutsche Bank. Before that, he spent more than ten years at Goldman Sachs, where he began as a structured finance analyst and ultimately co-headed the CDO and Synthetic ABS group. Michael received his MBA from Wharton and his BA from Princeton. Magnetar was recently recognized as a Top Private Credit Firm of 2025 by GrowthCap. Michael supports HE3AT. To learn more about this organization click here. I am your host, RJ Lumba. We hope you enjoy the show. If you like the episode, click to follow.

The Zscaler Public Sector Summit, Robert Roser, CISO, CDO and director of cybersecurity at Idaho National Laboratory, discussed the rapidly evolving cyber threat landscape facing government and critical infrastructure. He joined GovCIO Media & Research at the event to explain how malicious actors are increasingly using AI to launch more sophisticated phishing campaigns and ransomware attacks while also lowering the barrier to entry for less-skilled hackers. He also shared how his team is strengthening defenses through zero trust principles, stronger identity protections and new guardrails for AI use. As cyber threats grow more complex, Roser emphasized that cybersecurity is not just an IT issue — it is a responsibility shared across the entire organization.

Taka Ariga hits all the right notes for AI at scale: clarity of purpose, strong foundations, sustainable innovation, engaged ownership, and a confident workforce.    Taka and Kimberly discuss going beyond novel AI prototypes; the limits of automation; context building; data sovereignty and integrity; the unstructured data deluge; the unique sensitivities and needs of public agencies; valuing ownership and viable ways to scale; plagiarizing for good; foundations for AI success; wanting innovation without change; rethinking governance; enabling confident AI use; making space for reinvention; and being a skeptical AI advocate.Taka Ariga is a heretical technologist and the founder of Sol Imagination. He focuses on AI strategy design, implementation, and value capture. Taka served the US Office of Personnel Management (OPM) as CDO and CAIO and the US Government Accountability Office (GAO) as Chief Data Scientist and Director of the Innovation Lab. Related Resources:Sol Imagination (company)                  https://sol-imagination.ai/  A transcript of this episode is here.   

Building franchise relationships isn't about brand standards — it's about listening, flexibility, and time in the field. That idea sits at the center of this episode of #NoVacancyNews. I'm in Massachusetts at the global headquarters of Sonesta International Hotels, talking with Phil Hugh, Chief Development Officer. Phil walks through how stepping into the CDO role pushed him out of the office and onto the road for roughly 60 days, visiting properties, meeting with owners, and seeing firsthand what it actually takes to operate hotels across the economy, midscale, and upscale segments. What comes through clearly is how Sonesta has changed how it shows up for owners — from flattening its organization to putting real focus behind Americas Best Value Inn, refining its portfolio, and structuring franchise agreements around long-term success instead of short-term wins. We cover: