Podcasts about CDO

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 Businesses today are frustrated with outdated providers, rigid pricing, and fragmented tools.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.

​​Het gebruik en delen van gegevens (data) groeit sterk, ook binnen de overheid. In drie podcastafleveringen onderzoekt Walter van Wijk (CIP) hoe organisaties privacy en data-ethiek borgen bij datagedreven werken. Hij spreekt met drie experts Ronald Damhof, Bart Schellekens en Christian Verhagen over onder meer de rollen van CPO's en CDO's, de privacyaspecten van dataprojecten, en vraagstukken rond bescherming, preventie, opsporing en ethiek.In aflevering 1 wordt met Ronald Damhof (Chief Data Officer bij het ministerie van JenV en voorzitter van de Taskforce Gegevensdeling) gesproken over de kansen én de risico's bij data-projecten, ook als het gaat over opsporing. En over de relatie tussen de CDO en de CPO en de mens-kant van dit werk. 

​​Het gebruik en delen van gegevens (data) groeit sterk, ook binnen de overheid. In drie podcastafleveringen onderzoekt Walter van Wijk (CIP) hoe organisaties privacy en data-ethiek borgen bij datagedreven werken. Hij spreekt met drie experts Ronald Damhof, Bart Schellekens en Christian Verhagen over onder meer de rollen van CPO's en CDO's, de privacyaspecten van dataprojecten, en vraagstukken rond bescherming, preventie, opsporing en ethiek.​In aflevering 2 wordt gepleit voor een ‘belangen-officer' in plaats van een privacy officer, wordt de noodzaak van begrip van data bij bestuurders uitgelegd. Daarnaast komt de mogelijke spraakverwarring tussen juristen en niet-juristen aan de orde. Te gast is Bart Schellekens, vanuit het ministerie van BZK onder meer betrokken als adviseur bij de dienst Ontwikkeling, Digitalisering en Innovatie en bij het programma IBDS. 

​​Het gebruik en delen van gegevens (data) groeit sterk, ook binnen de overheid. In drie podcastafleveringen onderzoekt Walter van Wijk (CIP) hoe organisaties privacy en data-ethiek borgen bij datagedreven werken. Hij spreekt met drie experts Ronald Damhof, Bart Schellekens en Christian Verhagen over onder meer de rollen van CPO's en CDO's, de privacyaspecten van dataprojecten, en vraagstukken rond bescherming, preventie, opsporing en ethiek. ​In aflevering 3 heeft Christian Verhagen (partner en adviseur bij Highberg op terreinen als AI, data governance en data-ethiek) het over de ethische kant van data-projecten. Hoe volwassen zijn we, wanneer is het goed genoeg en hoe komen we daar? Wat is de rol van leiderschap daarbij? En hoever kan de overheid gaan in proactieve dienstverlening? 

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.

Tekoäly ei muuta vain tapaa kirjoittaa koodia. Se muuttaa sitä, mitä ohjelmistot voivat tehdä, miten niitä hinnoitellaan, millaisia tiimejä tarvitaan ja mitä ylipäätään kannattaa rakentaa. Muutos ei tapahdu tulevaisuudessa, vaan se on jo käynnissä.Vieraana on Juhana Harmanen, Goforen digipalveluiden johtaja. Harmanen on kulkenut freelancerista startup-yrittäjäksi, konsultiksi, CDO:ksi ja takaisin konsulttipuolelle. Gofore on nyt yli 1 900 asiantuntijan yritys, joka toimii 26 kaupungissa ja tekee yli 200 miljoonan euron liikevaihtoa. Jaksossa käydään läpi, mitä tässä murroksessa tapahtuu ohjelmistotuotannolle, hinnoittelulle, roolien muutokselle ja suomalaisen yrityksen kansainvälisille mahdollisuuksille.Jaksossa pureudumme:- Miksi Sam Altmanin ennustus yhden hengen miljardiyrityksestä meni jo ohi, ja mitä se kertoo AI-natiivista rakentamisesta- Miten agenttinen kehitysympäristö muuttaa ohjelmistotuotannon arjen ja mitä GitHub Copilot tai Claude Code tekevät jo nyt- Miksi speksauksen ja laadunvarmistuksen merkitys kasvaa samalla kun varsinainen koodaustyö automatisoituu- Miten kuuden vuoden tekninen velka saatiin purettua kolmessa kuukaudessa tekoälyavusteisesti- Miksi konsulttibisneksessä kysyntä on kasvanut eikä supistunut, vaikka koodareiden työ tehostuu- Mitä "service as software" tarkoittaa käytännössä ja miksi pääomasijoittajat arvioivat sen seitsemän kertaa suuremmaksi markkinaksi kuin perinteinen työkalumarkkina- Miksi token-pohjainen hinnoittelu murtaa kiinteän kuukausimaksun logiikan SaaS-tuotteissa- Miten Ukraina on rakentanut agenttisen kerroksen kansallisen digitaalisen infrastruktuurin päälle sodan keskellä- Mitä nuorelle koodarille tai alaa vaihtavalle kannattaa tässä tilanteessa tehdä- Miksi arvopohjaiseen hinnoitteluun siirtyminen vaatii myös johtamisajattelun uusimisenSisällysluettelo:00:00 Johdanto ja aihe01:57 Sam Altmanin ennustus ja 1,8 miljardin yhden hengen yritys04:00 Suomen talouskasvun haaste ja tuottavuusloikka05:34 Juhana Harmasen tausta: freelancerista startup-yrittäjäksi ja Goforelle19:55 Konsulttibisneksen kysyntä AI-murroksessa20:58 Ohjelmistotuotannon muutos: mistä pullonkaula siirtyy25:53 Agenttinen kehitysprosessi käytännössä32:37 Live-demo: verkkokauppa pyörimään kahdessa tunnissa ilman ohjelmointitaustaa36:24 Speksauksen ja laadunvarmistuksen uusi rooli38:47 Case: kuuden vuoden järjestelmä uudistettiin kolmessa kuukaudessa39:48 Onko koodarilla enää töitä?45:25 Miksi kiireisillä seniorikehittäjillä ei ole aikaa uudistua, ja mikä siinä on mahdollisuus47:29 Token-pohjainen hinnoittelu ja SaaS-liiketoimintamallin murros54:03 Agenttipohjainen kehitysorganisaatio: roolit ja orkestrointi59:09 Lokien analysointi, tietoturva ja jatkuva laadunvarmistus01:05:57 Service as software ja mitä se tarkoittaa eri toimialoille01:12:24 Ukrainan esimerkki: agenttinen kerros kansallisessa digitaalisessa infrastruktuurissa01:19:06 Arvopohjaiseen hinnoitteluun siirtyminen ja johtamisen muutos01:23:18 LoppuajatuksetMenestystä Etsimässä on podcast suomalaisesta ohjelmisto- ja SaaS-liiketoiminnasta. Keskustelut käsittelevät kasvua, kansainvälistymistä, liiketoimintamalleja ja päätöksiä, joita harvoin avataan julkisesti. Jakso on katsottavissa YouTubessa ja kuunneltavissa kaikissa podcast-palveluissa.Tutustu myös:Juhana Harmanen (LinkedIn): https://www.linkedin.com/in/harmanen/Gofore: https://gofore.com/Antti Pietilä (LinkedIn): https://www.linkedin.com/in/anttipietila/Kasvuvalmennus SaaS-yrityksille: https://calendly.com/antti-pietila/kasvuvalmennus-sparrausLoyalistic: https://loyalistic.com/fi/Loyalistic Studio: https://loyalistic.com/fi/studio/SaaS Finland: https://www.saasfinland.com/Podcast tehdään yhteistyössä Loyalisticin ja SaaS Finlandin kanssa.

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.

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.

Hur förändras arbetslivet när AI börjar påverka allt från arbetssätt till hur företag bygger kultur och skapar värde? I veckans avsnitt gästar Andreas Hölne, CIO och CDO på Fabege. Samtalet kretsar kring varför digitalisering inte längre kan drivas vid sidan av affären, hur AI-native bolag förändrar spelplanen och varför många företag fortfarande fastnar i hype istället för verklig transformation. Andreas delar också sina perspektiv på ledarskap, innovation och varför Sveriges tech-ekosystem fortfarande sticker ut internationellt.

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.

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.

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

Markus Unger ist Inhaber von vit:bikes – dem Münchner Fahrradgeschäfts-Verbund mit über 30 Shops und 180.000 YouTube-Abonnenten. Wir sprechen darüber, wie er den Sprung vom Aushilfe-Job zu einer AI-nativen Organisation geschafft hat – und warum der Mittelstand schneller AI-nativ werden kann als jeder Konzern. Eine Folge über Speichengregale, Säge schärfen, Cost per Lead, das vit:bikes-Cockpit und die These, dass der CEO Data und AI selbst treiben muss. Key Takeaways: → Warum 50 Prozent der Fahrradhändler in 3 Jahren nicht mehr existieren werden → Wie ein Speichengregal der Wendepunkt für 30 Shops wurde → Warum vit:bikes mit YouTube 10-12 Millionen Nutzer pro Jahr erreicht → Wieso eine selbstgebaute App 100.000-Euro-Angebote von Software-Firmen ersetzt → Warum der CEO Data und AI selbst treiben muss – und nicht die IT Über den Gast: Markus Unger ist Inhaber und Geschäftsführer von vit:bikes – einem der größten Fahrradgeschäfts-Verbünde im deutschsprachigen Raum mit über 30 Shops und 180.000 YouTube-Abonnenten. Bis 2030 will er auf 200 Shops skalieren – befeuert durch eine selbstentwickelte Software, das vit:bikes-Cockpit.

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

„Wir machen das Leben von Milliarden von Menschen sicherer.“ In dieser Folge spricht Franz Kubbillum mit Gabriel Mitschke‑Collande, Chief Digital Officer bei Giesecke+Devrient. Das traditionsreiche Unternehmen hat sich vom historischen „Gelddrucker“ zum globalen Security‑Tech‑Player mit rund 14.500 Mitarbeitenden und drei Geschäftsbereichen entwickelt: Digital Security (Verschlüsselung, Identitäten, Konnektivität), Financial Platforms (Zahlungskarten, 3DS, Bezahlinfrastrukturen) und Currency Technology (Banknoten, Sicherheitsmerkmale, Maschinen sowie digitale Zentralbankwährungen). Gabriel beschreibt, wie er vom TUM‑BWL‑Studium über Beratung und BayWa‑Stationen zum CDO eines Familienunternehmens mit 3,1 Milliarden Euro Umsatz wurde – und warum ihn gerade die Kombination aus Technologie, Sicherheit, Nachhaltigkeit, Venture Investments und langem Zeithorizont (bis hin zur „250‑Jahre‑Perspektive“) reizt. Er spricht über Priorisierungskompetenz und Energiemanagement im C‑Level, über Verantwortung als Quelle von Motivation und darüber, wie man sich in einer Welt rasanter Technologiezyklen (Internet, Smartphone, KI, Quantencomputer) fachlich „up to speed“ hält, ohne dabei den Blick für Menschen und Kultur zu verlieren. Weitere Fragen, die Gabriel in dieser Episode beantwortet, sind: - Wie ist Giesecke+Devrient konkret aufgestellt – von Banknoten und Pässen über SIM‑Karten und eSIMs bis hin zu digitalen Zahlungs- und Identitätssystemen? - Was bedeutet seine Rolle als CDO in der Praxis, und wie greifen IT, Technologiebüro, Cyber Security, Nachhaltigkeit und Venture‑Portfolio ineinander? - Wie denkt er über Verantwortung, Karriere und Führung – von „Passion und Patience“ über den berühmten „übernächsten Schritt“ bis zu ganz praktischen Routinen für Fokus und Energie? - Welche Rolle spielen Trust, Post‑Quantum‑Kryptografie und KI für die Zukunft von Sicherheit, digitalen Werten und unserem Vertrauen in digitale Infrastruktur? Themen: - C-level - Digital Security - Energiemanagement ----- Über Atreus – A Heidrick & Struggles Company Atreus garantiert die perfekte Interim-Ressource (m/w/d) für Missionen, die nur eine einzige Option erlauben: nachhaltigen Erfolg! Unser globales Netzwerk aus erfahrenen Managern auf Zeit zählt weltweit zu den besten. In engem Schulterschluss mit den Atreus Direktoren setzen unsere Interim Manager vor Ort Kräfte frei, die Ihr Unternehmen zukunftssicher auf das nächste Level katapultieren. ▶️ Besuchen Sie unsere Website: https://www.atreus.de/ ▶️ Interim Management: https://www.atreus.de/kompetenzen/service/interim-management/ ▶️ Für Interim Manager: https://www.atreus.de/interim-manager/ ▶️ LinkedIn-Profil von Gabriel von Mitschke-Collande: https://www.linkedin.com/in/gabriel-von-mitschke-collande-a421a0156/ ▶️ Profil von Franz Kubbillum: https://www.atreus.de/team/franz-kubbillum/

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

In diesem Webinar zeigt Kay Lehmann, wie der etablierte Value Stream von converlytics – von Webanalyse über Conversion-Optimierung bis zur Datenintegration und Aktivierung – zur Grundlage für eine zukunftsfähige, KI-getriebene Organisations- und Tool-Landschaft wird. Anhand konkreter Use Cases erfährst Du, wie Du Tracking-Setups, Tests und Datenflüsse so ausrichtest, dass Machine-Learning-Modelle, Personalisierung und Automatisierung wirklich businessrelevante Entscheidungen unterstützen – statt nur „noch mehr Daten“ zu erzeugen. Folgendes hast Du nach dem Webinar gelernt: - Verstehen, wie ein Ende-zu-Ende-Value-Stream (von Webanalyse bis Aktivierung) die Grundlage für sinnvolle KI-Anwendungen im Marketing legt. - Lernen, wie Tracking-Setups, Tests und Datenflüsse gestaltet werden müssen, damit Machine Learning, Personalisierung und Automatisierung messbaren Business-Impact erzeugen. - Erkennen, wie sich der Fokus von „mehr Daten sammeln“ hin zu „besser entscheiden und automatisieren“ verschieben muss. - Du verstehst nach dem Webinar, wie ein durchgängiger Data Value Stream – von der Datenerhebung über Conversion-Optimierung bis zur Integration und Aktivierung – die technische und organisatorische Basis für zukunftsfähige, KI-getriebene Marketing- und Produkt-Setups bildet. Du lernst, welche Anforderungen Tracking-Setups, Testdesigns und Datenflüsse erfüllen müssen, damit Machine-Learning-Modelle, Personalisierungslogiken und Automatisierung nicht im Blindflug laufen, sondern nachweislich bessere Entscheidungen und höhere Conversion-Raten ermöglichen. Zudem nimmst Du mit, wie Du den Fokus in deiner Organisationen weg von reiner Datensammlung und Dashboard-Produktion hin zu wirklich entscheidungs- und wirkungsorientierten Datenprozessen verschieben kannst. Zielgruppe: Das Webinar richtet sich an Entscheider:innen und Führungsverantwortliche, die daten- und KI-getriebene Wertschöpfung wirklich nutzen wollen - C‑Level und Bereichsleitungen, die ihre Organisation daten- und KI-ready ausrichten möchten (z.B. CEO, CMO, CDO, CPO). - Data-, Analytics- und Marketing-Leads, die für Tracking, Tests, Attribution, Personalisierung und Reporting verantwortlich sind. - Tech-Leads und Architekt:innen, die Marketing-/Product-Stacks, Data Pipelines und Integrationen konzipieren oder betreiben. Shownotes:

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.

Carlos Riebeling war Geschäftsführer bei Bitburger, vorher CDO und Geschäftsführer bei MyDays und Jochen Schweizer, davor Praktikant bei Westwing in der Rocket-Zeit. 15 Jahre Datenhandwerk. Jetzt gründet er eine D2C men's skincare performance brand – alleine. Wir sprechen über den 7-Tage-Weihnachtsflip im E-Commerce, über Print-Mailings mit zweistelligen Millionen-Umsätzen und über die Frage, warum AI nicht das Tool ist, sondern das Multiplikator-Sparring für alles, was du schon kannst. Key Takeaways: → Warum 15 Jahre Daten-Methodik der echte AI-Hebel sind – und nicht das Tool → Der 7-Tage-Weihnachtsflip: wie MyDays jedes Jahr exakt am selben Tag von 70% Frauen auf 70% Männer kippte → Print-Mailings mit Geo-Personalisierung – zweistelliger Millionen-Umsatz zu Weihnachten → Mittelstand-Assessment: Wie du in zwei Gesprächen erkennst, ob echte Transformationsbereitschaft da ist → Warum IT/Data/AI ein Business-Enabler ist – nicht ein Infrastruktur-Thema → Typing Mind, Claude & Nanobanana: Carlos' konkreter AI-Werkzeugkasten fuer den Marken-Aufbau → Die Rechnung, die alles ändert: Ein Single-Founder mit dem Wissensstand von 20 bis 100 Kollegen Über den Gast: Carlos Riebeling ist Gründer von 60kollektiv, einer D2C men's skincare performance brand. Davor war er Geschäftsführer bei der Bitburger-Gruppe, CDO und Geschäftsführer bei MyDays und Jochen Schweizer und Praktikant bei Westwing in der Rocket-Internet-Ära. Bachelor in Münster, Master in England, Abschlussarbeit mit der NASA. Heute 39, Vater einer Tochter, lebt in Düsseldorf. MY DATA IS BETTER THAN YOURS ist ein Projekt von BETTER THAN YOURS, der Marke für richtig gute Podcasts.

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

Welcome to another episode of Data Debrief, the companion show to Driven By Data: The Podcast, where hosts Catherine Dowden-King and Kyle Winterbottom sit down to unpack Tuesday's conversation, share what's been on their minds, and explore what's really happening across the data and AI landscape.With Kyle away this week, Catherine is joined by Kinnari Ladha, CDO, Travelodge, to reflect on Tuesday's episode with Barry Panayi, diving deeper into the themes that sparked the most discussion, and a few that deserve even more attention.They cover:Why the path from CDO to NED is becoming increasingly attractive, but remains unclear, relationship-driven, and difficult to break intoWhat boards actually expect from data leaders, and why being “the data expert” is only a small part of the valueThe growing lack of standardisation in the CDO role, and how differing mandates, ownership, and expectations are shaping success or failureThe emerging split between data foundations and value delivery, and whether the CDO role is becoming too big for one personThe ongoing tension between CDO, CIO, and CTO over AI ownership, and why no single function can truly own itThe challenge of balancing short-term ROI with long-term foundational investment, and why many organisations are still getting this wrongWhy strong collaboration between data and technology leaders is critical, and how misalignment can derail even the best strategiesThe widening maturity gap across organisations, from those deploying AI at scale to those still grappling with basic data challengesWhy data and AI literacy remains one of the most overlooked barriers to success, despite heavy investment in tools and platformsThe growing pressure on organisations to drive adoption, and the reality that without behaviour change, even the best technology failsPlus, in this week's Thoughts of the Week, Catherine shares her perspective on the growing divide in organisational maturity and how the AI boom is widening the gap between leaders and laggards. Kinnari reflects on the often-overlooked challenge of data and AI literacy, and why organisations must prioritise workforce capability if they want to unlock real value from their investments.Like and subscribe wherever you listen, and if you've got a question or topic you'd like the team to cover, email community@orbitiongroup.com

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.

Qualytics is redefining enterprise data quality by positioning it as a collaborative business function rather than an isolated data engineering problem. Founded at the start of the pandemic by Gorkem Sevinc - a former CTO and CDO who spent years managing reactive data quality firefights - Qualytics emerged from a clear practitioner pain point: writing endless custom rules to catch data issues after they'd already broken dashboards and KPIs. The company raised pre-seed and seed rounds while building with beta customers, then closed a Series A as repeatability patterns emerged in their POC process. Now, as enterprises scramble to operationalize AI initiatives, Qualytics is experiencing explosive inbound demand from organizations realizing their data foundations aren't ready for democratized data access. Topics Discussed The practitioner insight that sparked Qualytics: reactive rule-writing doesn't scale Leveraging existing CTO/CDO networks and PE portfolio connections for beta customers The evolution from free POCs to paid POCs as a mutual commitment mechanism Identifying repeatability through week-by-week POC conversion patterns Building practitioner credibility into the sales motion while hiring for enterprise sales grit The decision to hire sales and marketing leadership simultaneously post-Series A Tracking in-product engagement metrics (DQ operations frequency, anomaly detection, rule editing) as churn prevention Positioning data quality as vertical-specific business problems (premium leakage, regulatory compliance) The timing advantage: AI adoption forcing enterprises to treat data governance as mandatory infrastructure GTM Lessons For B2B Founders Talk to 100 prospects before writing code—even with deep domain expertise: After burning 18 months building a radiology second opinion product that patients didn't want (they didn't even know radiologists were doctors), Gorkem adopted a hard rule: validate with 100 conversations before building. His advantage as a former CTO who lived the data quality problem created false confidence. Practitioners often assume their pain is universal, but buyer awareness and willingness to pay are separate questions. Start with NSF I-Corps-style problem validation: show rough sketches, probe what happened when they hit the pain point, understand how it hurt them financially or operationally. Repeatability appears in micro-conversions during trials, not just closed-won rates: Gorkem didn't declare product-market fit when deals closed—he declared it when he could predict POC behavior by week. "Week two, I'm expecting this. Week three, I'm expecting this." That predictability enabled ROI calculators and internal champion enablement materials. For technical founders, this means instrumenting your trial or POC to track leading indicators: specific features activated, data volumes processed, number of team members engaged, frequency of logins. When those patterns stabilize across prospects, you have a repeatable motion. Use paid POCs as a procurement front-loading mechanism, not a revenue play: Qualytics charges nominal amounts for some POCs—not for the revenue, but to get the MSA signed and force both parties through legal/security review upfront. This eliminates the pattern where free POCs succeed technically but die in procurement. Large enterprises often refuse to pay for POCs, which Gorkem accepts—but only if they commit equivalent effort (executive time, cross-functional teams). The paid POC is a qualification tool: if they won't commit anything, they're not a real opportunity. Hire sales and marketing leadership in parallel and hold them to unified GTM metrics: Gorkem regrets hiring early sales reps before leadership and delaying marketing investment. Post-Series A, he hired both leaders simultaneously and holds them jointly accountable to pipeline generation and velocity—not siloed MQL counts or quota attainment. This structural decision forces collaboration on messaging, ICP definition, and campaign strategy from day one. For technical founders who "figured out" founder-led sales, resist the urge to replicate your motion with more SDRs. Bring in strategic leadership that can build a scalable system. Instrument product engagement as your earliest churn signal—then intervene immediately: Beyond quarterly NPS and executive QBRs, Gorkem tracks granular product usage: how many data quality operations users run, how many anomalies they discover, how actively they're editing rules. When engagement drops, he doesn't wait—he jumps into the customer's existing weekly meetings to diagnose and course-correct. For B2B founders building complex products with long time-to-value, passive health scores aren't enough. You need active usage telemetry and a low-latency intervention process. Translate technical capabilities into vertical-specific business outcomes: Gorkem doesn't pitch "data quality for data engineers." He talks about premium leakage with insurance companies and OCC/SEC data controls with banks. This reframing works because buyers recognize their problem, not a vendor category. The shift requires research: understand each vertical's regulatory environment, operational pain points, and the business metrics executives care about. When you walk in speaking their language about their P&L impact, you're not another vendor—you're someone who gets it. Time your market entry to when "nice-to-have" becomes "must-have": When Qualytics launched, some enterprises called data quality a "nice-to-have." AI adoption changed that calculus overnight. Organizations planning to let 20,000 employees interrogate data through AI interfaces suddenly realized they need robust data governance, quality controls, and cataloging first. Gorkem's timing wasn't luck—he built during the "nice-to-have" phase so he'd be ready when AI budgets made it mandatory. Technical founders should identify the external forcing function (regulation, technology shift, economic change) that will transform their solution from vitamin to painkiller. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

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.   

The General Services Administration has leaned into its role as a central, shared services provider for the rest of the federal government during the second Trump administration. In particular, it has taken a leadership position centralizing most federal procurement under one roof and serving as a sort of clearinghouse for federal AI efforts. With so much transformation underway, the GSA during Trump 2.0 has taken on an even brighter spotlight, fueling federal operations. Miranda Nazzaro is the FedScoop reporter covering GSA during this pivotal time, and she joins the podcast to discuss some of the agency's top priorities, from OneGov and the TMF to eliminating woke AI, among others. The Treasury Department said Monday that it would cancel all of its contracts with Booz Allen Hamilton, linking the decision to a former employee now serving prison time for leaking tax returns. Treasury Secretary Scott Bessent said in a three-paragraph press release that the agency's 31 contracts with Booz Allen Hamilton — worth $21 million in total obligations and $4.8 million in annual spending — would be scrapped as part of President Donald Trump's push to “root out waste, fraud and abuse.” “Canceling these contracts is an essential step to increasing Americans' trust in government,” Bessent said. “Booz Allen failed to implement adequate safeguards to protect sensitive data, including the confidential taxpayer information it had access to through its contracts with the Internal Revenue Service.” A Booz spokesperson said in an email to FedScoop that the firm was “surprised by this announcement” — especially given Treasury's reasoning regarding Charles Edward Littlejohn, who between 2018 and 2020 leaked the confidential tax returns and information of hundreds of thousands of taxpayers. “Booz Allen fully supported the U.S. government in its investigation, and the government expressed gratitude for our assistance, which led to Littlejohn's prosecution,” the Booz spokesperson said. “We were surprised by this announcement and look forward to discussing this matter with Treasury.” Per the Treasury release, the IRS determined that the data breach affected roughly 406,000 taxpayers. Littlejohn, who was sentenced to five years in prison last January after pleading guilty to one count of disclosing tax return information without authorization, leaked the returns of Trump, Elon Musk and other wealthy individuals to a pair of news organizations. NASA has a new top official for artificial intelligence and data. Kevin Murphy began serving in an acting capacity in both roles Nov. 30, 2025, NASA spokesperson Jennifer Dooren confirmed to FedScoop in an email. He replaces David Salvagnini, who was the agency's CDO for roughly two-and-a-half years, and CAIO for just over a year-and-a-half. Salvagnini was the agency's first-ever CAIO. According to Murphy's LinkedIn, he has been at NASA for over 17 years. He first served as a system architect at NASA's Goddard Space Flight Center and has held a series of data-related roles, including chief science data officer. As the agency's lead for data science, Murphy has already worked to advance technologies — such as cloud computing, machine learning, and data platforms — for use with NASA's scientific data, per an agency bio. He also oversees the agency's high-end computing capability (HECC) portfolio, which deploys computing technologies to support large-scale modeling, simulation and analysis at the agency. Murphy's designation as acting CAIO and CDO comes after Salvagnini announced his plans to leave the agency in a LinkedIn post roughly two months ago. In that post, Salvagnini said he opted into the Trump administration's deferred resignation program. He said he began his transition Oct. 31 and would retire from federal service in the spring of 2026.

Join Adam Good, Founder and CEO of Auric Essentials, for a deep dive into the next frontier of health-tech and environmental design. While our modern spaces are "smart," they are often emotionally hollow. Adam explains how he is bridging the gap between digital transformation and human biology by turning scent our most powerful but neglected sense into a programmable, data-driven layer of ambient computing that optimizes for stress, focus, and sleep.

In this episode, Verena Gründel talks to Andrea Lederer, Chief Digital Officer at Weleda, the iconic natural cosmetics brand loved by Bella Hadid. Andrea shares her journey of digitally transforming a 105-year-old heritage company, building e-commerce structures from the ground up, and aligning digital goals with brand values like premiumization and sustainability. They discuss how Andrea restructured teams, harmonized global data, launched new digital campaigns, and what it takes to move fast without losing sight of brand identity. 5 Data-Driven Takeaways from the Podcast: When Andrea joined, the digital team was "somewhere in marketing" — not linked to sales or customers. Reporting systems were disconnected. “As global CDO, I couldn't even tell you how much we were selling digitally,” she says. That changed fast. She aligned her strategy with Weleda's four corporate goals: internationalization, digitization, premiumization, and product innovation. “We asked: what role does digital play in each? Then we translated it into team and individual goals,” Andrea explains. She also brought in structure. “We clarified roles, cleaned up CRM, launched paid media, and set pricing rules.” Quick wins like Prime Day campaigns and social media boosts delivered instant ROI without compromising brand positioning. Weleda's first D2C-webshop launched in 2024.Today, 40% of webshop traffic comes from paid media. Global sales achieved double-digit growth in 2025. Digital were outpacing traditional channels. Andrea restructured and re-hired nearly the entire digital team. In autumn 2025 Weleda launched it's first social-first campaign ever: for the new product Booster Drops- When Andrea joined, the digital team was "somewhere in marketing" — not linked to sales or customers. Reporting systems were disconnected. “As global CDO, I couldn't even tell you how much we were selling digitally,” she says. That changed fast. She aligned her strategy with Weleda's four corporate goals: internationalization, digitization, premiumization, and product innovation. “We asked: what role does digital play in each? Then we translated it into team and individual goals,” Andrea explains. She also brought in structure. “We clarified roles, cleaned up CRM, launched paid media, and set pricing rules.” Quick wins like Prime Day campaigns and social media boosts delivered instant ROI without compromising brand positioning. “We didn't go below the market. We just adjusted our pricing to become competitive,” she clarifies. +++ Stay up to date until then with Verena's newsletter, DMEXCO Digital Digest – the most important news from the world of digital marketing in Germany and internationally, delivered straight to your inbox.https://go.dmexco.com/digital-digest-current-edition

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:

Episode Overview In this episode of the CDO Matters Podcast, Malcolm Hawker lays out his most candid and contrarian predictions for where data, analytics, and AI are really headed in 2026—cutting through the hype to focus on what will actually matter for data leaders. From the evolution (and fragmentation) of the CDO role to hard truths about AI readiness, governance, and operating models, this episode challenges conventional wisdom and calls out uncomfortable realities many organizations are still ignoring. If you're responsible for turning data into outcomes, and not just headlines, this is a must-listen.Episode Links and ResourcesFollow Malcolm Hawker on LinkedIn

Bill Saltmarsh joins me to discuss where a modern CDO gets the inspiration to “operate in the producty way” in his domain, which is healthcare. Now Vice President of Enterprise Data and Transformation and the Chief Data Officer at Children's Mercy Kansas City, his early days as an analyst revealed a gap between what stakeholders asked for vs. the outcomes they sought. This convinced him that data teams need to pause, ask better questions, and prioritize meaningful outcomes over quickly churning out dashboards and reports. Bill and I discuss how a producty mindset can be embedded across an organization. He also talks about why data leaders must set firm expectations. We explore the personal and cultural shifts needed for analysts and data scientists to embrace design, facilitation, and deeper discovery, even when it initially seems to slow things down. We also examine how to define value and ROI in healthcare, where a data team's impact is often indirect.  By tying data efforts to organizational OKRs and investing in governance, strong data foundations, and data literacy, he argues that analytics, data, and AI can drive better decisions, enhance patient care, and create durable organizational value. Highlights/ Skip to: What led Bill Saltmarsh to run his team at Children's Mercy “the producty way” (1:42)  The kinds of environments Bill worked in prior that influenced his current management philosophy (4:36) Why data teams shouldn't be report factories (6:37)  Setting the standard at the leadership level vs the everyday work (10:53) How Bill is skilling and hiring for non-technical skills (i.e. product, design, etc) (13:51)  Patterns that data professionals go through to know if they're guiding stakeholders correctly (20:54)  The point when Bill has to think about the financial side of the hospital (26:30) How Bill thinks about measuring the data team's  contributions to the hospital's success (30:28) Bill's philosophy on generative AI (36:00) Links Bill Saltmarsh on LinkedIn

In this last episode of the special AI mini-series, we now explore the human side of transformation, where technology meets purpose and people remain at the center. From future jobs and critical thinking to working with C-level leaders, how human intervention and high-quality data drive success in an AI-powered world.This week Dave, Esmee , Rob sit down with Johanna Hutchinson, CDO at BAE systems about why data matters, the rise of Sovereign AI, and the skills shaping the intelligence age. TLDR00:55 Introduction of Johanna Hutchinson02:09 Explaining the State of AI mini-series with Craig06:01 Conversation with Johanna34:20 Weaving today's data tapestries with AI40:20 Going to a rave GuestJohanna Hutchinson: https://www.linkedin.com/in/johanna-hutchinson-95b95568/ HostsDave Chapman: https://www.linkedin.com/in/chapmandr/Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/with co-host Craig Suckling: https://www.linkedin.com/in/craigsuckling/ProductionMarcel van der Burg: https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman: https://www.linkedin.com/in/chapmandr/ SoundBen Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:  https://www.linkedin.com/in/louis-corbett-087250264/ 'Cloud Realities' is an original podcast from Capgemini

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

This week on the GovNavigators Show, Robert and Adam welcome Ted Kaouk, former Federal Chief Data Officer and (as Adam puts it) the godfather of the federal CDO community. Now founder of Generative Work AI, Ted joins the hosts to discuss how generative AI is transforming the way organizations think, learn, and lead.From his “30 AI apps in 30 days” challenge to lessons from building the CDO Council after the Evidence Act, Ted shares insights on how agencies can move from compliance to true data-driven value, the emerging art of “vibe coding,” and why behavioral feedback may be the new superpower in managing AI systems.Show Notes:OPM: Hiring Guidance MemoVeterans Day: VA Resources for Honoring the DayEvents on the GovNavigators' Radar:Nov 10: ACT-IAC GovTechNext Emerging Tech Demo DayNov 12: ACT-IAC CX SummitNov 18: DefenseOne's State of Defense Business Acquisition Summit Dec 4-5: ACT-IAC's ELC in DC!  

On today's Promoted Episode of Experiencing Data, I'm talking with Lucas Thelosen, CEO of Gravity and creator of Orion, an AI analyst transforming how data teams work. Lucas was head of PS for Looker, and eventually became Head of Product for Google's Data and AI Cloud prior to starting his own data product company. We dig into how his team built Orion, the challenge of keeping AI accurate and trustworthy when doing analytical work, and how they're thinking about the balance of human control with automation when their product acts as a force multiplier for human analysts.   In addition to talking about the product, we also talk about how Gravity arrived at specific enough use cases for this technology that a market would be willing to pay for, and how they're thinking about pricing in today's more “outcomes-based” environment.  Incidentally, one thing I didn't know when I first agreed to consider having Gravity and Lucas on my show was that Lucas has been a long-time proponent of data product management and operating with a product mindset. In this episode, he shares the “ah-hah” moment where things clicked for him around building data products in this manner. Lucas shares how pivotal this moment was for him, and how it helped accelerate his career from Looker to Google and now Gravity. If you're leading a data team, you're a forward-thinking CDO, or you're interested in commercializing your own analytics/AI product, my chat with Lucas should inspire you!     Highlights/ Skip to: Lucas's breakthrough came when he embraced a data product management mindset (02:43) How Lucas thinks about Gravity as being the instrumentalists in an orchestra, conducted by the user (4:31) Finding product-market fit by solving for a common analytics pain point (8:11) Analytics product and dashboard adoption challenges: why dashboards die and thinking of analytics as changing the business gradually (22:25) What outcome-based pricing means for AI and analytics (32:08) The challenge of defining guardrails and ethics for AI-based analytics products [just in case somebody wants to “fudge the numbers”] (46:03) Lucas' closing thoughts about what AI is unlocking for analysts and how to position your career for the future  (48:35) Special Bonus for DPLC Community Members Are you a member of the Data Product Leadership Community? After our chat, I invited Lucas to come give a talk about his journey of moving from “data” to “product” and adopting a producty mindset for analytics and AI work. He was more than happy to oblige. Watch for this in late 2025/early 2026 on our monthly webinar and group discussion calendar.   Note: today's episode is one of my rare Promoted Episodes. Please help support the show by visiting Gravity's links below: Quotes from Today's Episode “The whole point of data and analytics is to help the business evolve. When your reports make people ask new questions, that's a win. If the conversations today sound different than they did three months ago, it means you've done your job, you've helped move the business forward.” — Lucas  “Accuracy is everything. The moment you lose trust, the business, the use case, it's all over. Earning that trust back takes a long time, so we made accuracy our number one design pillar from day one.” — Lucas  “Language models have changed the game in terms of scale. Suddenly, we're facing all these new kinds of problems, not just in AI, but in the old-school software sense too. Things like privacy, scalability, and figuring out who's responsible.” — Brian “Most people building analytics products have never been analysts, and that's a huge disadvantage. If data doesn't drive action, you've missed the mark. That's why so many dashboards die quickly.” — Lucas “Re: collecting feedback so you know if your UX is good: I generally agree that qualitative feedback is the best place to start, not analytics [on your analytics!] Especially in UX, analytics measure usage aspects of the product, not the subject human experience. Experience is a collection of feelings and perceptions about how something went.” — Brian Links Gravity: https://www.bygravity.com LinkedIn: https://www.linkedin.com/in/thelosen/ Email Lucas and team: hello@bygravity.com

The distribution of asset price returns is a subject of much study in the literature of empirical finance. We know, of course, that equity returns are left-tailed, subject to the occasional violent plunge. But other asset classes are different, and in this context it was a pleasure to welcome Ben Hoff, Global Head of Commodity Strategy at Société Générale, to the Alpha Exchange. Ben describes commodities as a dual system — one that exists both physically and financially. This duality means real-world frictions such as storage, transport, and substitution shape risk and return in ways financial models often miss. Unlike equities, where the volatility risk premium (VRP) is structural and macro-driven — investors chronically overpay for protection against crashes — the commodity VRP is episodic and micro-driven, emerging only when the physical system's natural buffers are overwhelmed. Ben likens the commodity ecosystem to a CDO structure of risk absorption. The first-loss tranche is “optionality in time,” where storage smooths shocks by shifting supply forward. The mezzanine tranche cures through space and form, rerouting flows across geographies or substituting between products. Only when those defenses are depleted does the equity tranche — financial volatility — take over. This hierarchy explains why volatility in commodities is less persistent but often more explosive when it surfaces. We also explore how the financialization of commodities — benchmark indices, systematic flows, and vol strategies — has created visible “signatures” in pricing, yet the underlying markets remain driven by physical constraints and optionality. Ben's takeaway: commodities are inherently antifragile, making their risk premia complex, localized, and highly path dependent. I hope you enjoy this episode of the Alpha Exchange, my conversation with Ben Hoff.