Podcasts about chief data officers

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

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.

What does it really take to grow into leadership in data — especially if you didn't come from a traditional technical background? In this episode, Cecilia speaks with Lauren Dixon, Chief Data Officer at the Financial Conduct Authority, about building confidence, stepping into opportunities before feeling fully ready, and redefining what success in data leadership actually looks like. From studying history to leading large technology and data teams, Lauren shares how curiosity, communication, collaboration, and self-awareness have shaped her career far more than having the “perfect” background. Together, they explore imposter syndrome, leadership growth, mentorship, hiring beyond degrees, and the practical skills that truly help people stand out in data today. This is an honest and reassuring conversation for anyone who has ever questioned whether they are technical, experienced, or qualified enough to take the next step in their career.

Mobility is becoming a digital service challenge, not just a transport one. As expectations rise, agencies need connected, secure, user-friendly experiences that make it easier for people and industry to interact with government. In this week's episode, Erika-Kirsten Easton, Chief Data Officer and Director General, Enterprise Service, Data and Architecture, Transport Canada shares how they are modernising service delivery, using data and AI to improve decisions, and connecting systems across teams and jurisdictions. The session offers practical ideas for safer, more accessible services that keep pace with changing needs. Erika-Kirsten Easton, Chief Data Officer and Director General, Enterprise Service, Data and Architecture, Transport Canada   For more great insights head to www.PublicSectorNetwork.co  

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.

Join Steven Walchek, Co-Founder and CEO of Liminal, for a deep dive into the "adoption paradox" facing the modern enterprise. Despite billions in AI investment, most organizations remain trapped in perpetual pilots. A serial entrepreneur with over $1.1B in exit value and a former CINO at FIS, Steven argues that the failure isn't technical—it's strategic. In this episode, we explore why forcing standardization kills impact and how the industry is shifting toward "Secure AI Enablement" that learns from actual user behavior to autonomously deploy capabilities where they matter most.

The last time Ott Velsberg appeared on this podcast was early 2022. Popular LLMs had not yet been released. The conversation was about how AI could support data-driven decision-making in government – a topic that felt, at the time, more prospective than operational.Four years on, with Estonia counting over 220 public sector AI use cases and close to 60 million euros in estimated annual impact, the frame has shifted. So, it is now less about what AI in government could look like and more about what it has taken to get there.In this episode of the Digital Government Podcast, Ott Velsberg, outgoing Chief Data Officer of the Estonian government and responsible for Estonia's AI policy, tells us about the path from strategy to working infrastructure. And the long institutional work that makes it possible to coexist with both consent-based data sharing and AI adoption at scale.

 Tell us about your journey—from finance and strategic planning at AXA to leading data strategy for some of the world's most iconic beauty and luxury brands. What drew you into this world, and how has your background shaped your approach?You work at the intersection of data and creativity—especially in industries that have traditionally been emotionally driven. How do you balance the power of analytics with the intuition that drives beauty and luxury?You've worked with some of the biggest names—Chanel, Dior, L'Oréal, and more. Can you share an example where data-driven insights led to a surprising or game-changing commercial outcome for a client?What are some of the biggest opportunities or shifts you're seeing in the beauty and luxury industry over the next few years? Where should brands be leaning in—or rethinking old habits?What are you most excited about for the future of beauty, luxury, and data? Whether it's technology, consumer behavior, or new ways of working—what's lighting you up right now?  

K-Pop is in a unique situation. The genre “feels” like it's everywhere. BTS, Blackpink, Stray Kids, and KPop **Demon Hunters have topped Billboard and Netflix charts. But recent data shows that roughly 2% of global streams are from K-Pop, and the genre is and trending down. We are joined by Will Page, former Chief Economist at Spotify and author of Pivot. He released a new report on Music Business Worldwide in collaboration with Jeongbeom ‘JB' Kim, Chief Data Officer at the Korean-based KreatorsNetwork. We discussed how K-Pop's demand is centralized at the top, why even a phenomenon like KPop Demon Hunters didn't lift the rest of the genre, and what Western labels keep getting wrong when they try to copy the model. We dive deep into Korea's "export or die" culture, and what other sports may teach music about reaching new audiences. CHAPTERS 04:47 The Status of K-Pop 08:28 The Impact of BTS' Hiatus 17:02 The Limitations of Superfan Monetization 24:38 "Export or Die" Model 29:44 Inflation's Impact on Music 31:28 Lessons from Formula 1 SPONSORS Chartmetric: Listen in for our Stat of the Week Symphonic: Distribute your music to one of the largest networks in the industry. Symphonic delivers your music to over 200 digital service providers ensuring that you're monetizing every stream and use of your music on Spotify, TikTok, YouTube, and more TRAPITAL Where technology shapes culture. New episodes and memos every week. Sign up here for free.

The data and AI landscape shifted more in the last six months than in the three years prior and most data teams are still operating off roadmaps that predate it. Malcolm Hawker goes solo to give data leaders an honest read on where things stand and what to prioritize for the rest of the year.

Learn what happens when the executive accountable for data strategy is also the executive accountable for the business results that depend on it. Saugata Saha, President of S&P Global Market Intelligence and Chief Enterprise Data Officer at S&P Global, shares how he manages one of the world's largest financial data estates while driving business outcomes across public and private markets. He breaks down the four pillars of S&P Global's data strategy, the federated organizational model that connects data teams to business value, and why capturing ROI from AI requires deliberate workflow transformation. Key Moments Why Data Strategy Must Follow Business Strategy (04:57): Saugata challenges the idea that data and business strategy can run in parallel. Market trends, customer pain points, and existing capabilities must come first. Building an AI-Ready Financial Data Estate (15:10): Scale alone does not create intelligence. Saugata explains why semantic layers and graph databases are the hard work behind connected financial data. How AI Compresses Post-Acquisition Data Integration (18:29): Manual reconciliation of millions of records is no longer the only path. Discover how AI entity matching accelerated post-acquisition integration. The Federated Model That Connects Data to Value (22:49): Most large organizations either over-centralize data teams or leave them too embedded to scale. Saugata outlines the federated model that actually bridges both. Rethinking AI Productivity: From Marginal to Transformative (28:29): Most AI programs stop at training and tooling. Saugata explains why deliberately redesigning workflows is the missing step between AI investment and real ROI. Key Quotes “Data strategy and business strategy have to be very tightly connected. And if they're not, that's when value capture does not happen. In fact, I would go so far as to say data strategy actually follows from business strategy.” - Saugata Saha “Stop treating data as an afterthought or byproduct, but start thinking about data as a key ingredient for value creation and competitive advantage.” - Saugata Saha “We don't want everybody to become 10% more productive, because that's a little squishy. We want 10% of the people to become a hundred percent more productive so they can do other things.” - Saugata Saha “If a company can really use data at scale for better decision making, better client service, [and] better outcomes, that creates a lasting edge over the competition.” - Saugata Saha Mentions S&P Global Agrees to Acquire With Intelligence from Motive Partners for $1.8 Billion, Establishing Its Leadership in Private Markets Intelligence The Data & AI Chief: Why a Federated Data Team is Crucial for Business Value, with Dow Private Companies Wait Too Long to Go Public The Lex Fridman Podcast Guest Bios  Saugata serves as President of S&P Global Market Intelligence, leading the division's efforts to deliver essential insights and intelligence to clients worldwide. He is also S&P Global's Chief Enterprise Data Officer, responsible for driving innovation and excellence in the company's enterprise data strategy. Saugata is a member of S&P Global's Executive Leadership Team, contributing to the strategic direction and growth of the organization. Before joining S&P Global, Saugata was a consultant at McKinsey & Company's New York office, where he advised clients on strategy, mergers and acquisitions, corporate finance, and operational improvements across various industries, with a strong focus on financial services. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

The rise in use and function of ambient AI scribes is arguably one of the fastest technologic changes ever seen in health care. In this episode of Healthy Dialogue, host Derek Angus, MD, MPH, is joined by Vincent Liu, MD, MS, Chief Data Officer of The Permanente Medical Group in Kaiser Permanente, to discuss the rapidly changing world of ambient AI. Related Content: Ambient AI Scribes and the Quintuple Aim Changes in Clinician Time Expenditure and Visit Quantity With Adoption of Artificial Intelligence–Powered Scribes AI Scribes Are Here, but Is Health Care Ready?

In this episode, I follow up with Ross Koenig, Chief Data Officer at Shore Capital Partners and one of our earliest podcast guests. Ross reflects on how his role has evolved over the past three years, from leading the Data Center of Excellence supporting portfolio companies to focusing on Shore's own investment process data and cross-portfolio insights. He shares why Shore's longstanding commitment to process and documentation has become a powerful foundation in the age of AI, and how nearly 1,000 codified executive hires now enable pattern recognition that few firms can match. Ross also discusses why "garbage in, garbage out" matters more than ever, how AI is breaking down walls between executives and data, and why business owners who ignore AI risk being left behind.Key Takeaways:Build the data foundation first because AI only works as well as the process, documentation, and discipline behind itCodify what you know across people, processes, and decisions so you can spot patterns, learn from past outcomes, and make smarter choices going forwardRecognize that AI's biggest unlock is accessibility, putting information and analysis directly in the hands of executives who used to be blocked by technical wallsEngage with AI now regardless of size or industry because the cost barriers have fallen and the businesses that ignore it will quickly fall behindChapters:00:00 – Introduction01:36 – Building the Data Foundation06:36 – Codifying Knowledge at Scale12:15 – Accessibility and the Power of AI15:31 – Why Every Business Owner Should Engage with AI NowListen to our podcasts at:https://www.shorecp.university/podcastsYou'll also find other Bigger. Stronger. Faster. episodes, alongside our Microcap Moments and Everyday Heroes series—highlighting the people and stories that make the microcap space unique.Other ways to connect:Blog: https://www.shorecp.university/blogShore University: https://www.shorecp.university/Shore Capital Partners: https://www.shorecp.com/LinkedIn: https://www.linkedin.com/company/shore-universityThis podcast is the property of Shore Capital Partners LLC. None of the content herein is investment advice, an offer of investment advisory services, or a recommendation or offer relating to any security. See the “Terms of Use” page on the Shore Capital website for other important information.

In this episode, I follow up with Ross Koenig, Chief Data Officer at Shore Capital Partners and one of our earliest podcast guests. Ross reflects on how his role has evolved over the past three years, from leading the Data Center of Excellence supporting portfolio companies to focusing on Shore's own investment process data and cross-portfolio insights. He shares why Shore's longstanding commitment to process and documentation has become a powerful foundation in the age of AI, and how nearly 1,000 codified executive hires now enable pattern recognition that few firms can match. Ross also discusses why "garbage in, garbage out" matters more than ever, how AI is breaking down walls between executives and data, and why business owners who ignore AI risk being left behind.Key Takeaways:Build the data foundation first because AI only works as well as the process, documentation, and discipline behind itCodify what you know across people, processes, and decisions so you can spot patterns, learn from past outcomes, and make smarter choices going forwardRecognize that AI's biggest unlock is accessibility, putting information and analysis directly in the hands of executives who used to be blocked by technical wallsEngage with AI now regardless of size or industry because the cost barriers have fallen and the businesses that ignore it will quickly fall behindChapters:00:00 – Introduction01:36 – Building the Data Foundation06:36 – Codifying Knowledge at Scale12:15 – Accessibility and the Power of AI15:31 – Why Every Business Owner Should Engage with AI NowListen to our podcasts at:https://www.shorecp.university/podcastsYou'll also find other Bigger. Stronger. Faster. episodes, alongside our Microcap Moments and Everyday Heroes series—highlighting the people and stories that make the microcap space unique.Other ways to connect:Blog: https://www.shorecp.university/blogShore University: https://www.shorecp.university/Shore Capital Partners: https://www.shorecp.com/LinkedIn: https://www.linkedin.com/company/shore-universityThis podcast is the property of Shore Capital Partners LLC. None of the content herein is investment advice, an offer of investment advisory services, or a recommendation or offer relating to any security. See the “Terms of Use” page on the Shore Capital website for other important information.

This week on the GovNavigators Show, Adam and Robert sit down with the GovNavigator Network's Josh Martin, former Chief Data Officer for the State of Indiana turned founder and AI practitioner, for a candid, highly practical conversation about what AI can (and can't!) do in government today.Josh shares how he's building AI-powered tools to solve real-world problems, like instantly preparing for vendor meetings or streamlining procurement reviews, and explains why AI's biggest value isn't replacing people, but accelerating decision-making. He also offers a clear-eyed warning: agencies that over-automate without human oversight risk serious errors, hallucinations, and reputational damage.We dive into how governments can responsibly adopt AI, the importance of understanding data and prompting, and why “personal responsibility” is the missing ingredient in most AI strategies. Josh also reflects on his transition out of government, what he misses, and where he sees the biggest opportunities for innovation across the public sector.Show Notes:Partial DHS funding measure passedHouse Oversight advances nine anti-fraud billsDoW strikes new AI deal with major companiesNew EO pushing fixed-price contractingWhat's on the GovNavigators' Radar?May 2-4: Professional Services Council Annual ConferenceMay 5: AGA's Performance Counts SummitMay 6: Service to America MedalsMay 14-15: ACT-IAC's Emerging Technology & Innovation Summit

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.

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

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

In Episode 3, of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom was joined by Daragh Kelly, Chief Data Officer at The Economist, where they discuss why most AI initiatives are still failing, why there's not much measurable progress and how insight functions have become toolmakers and decision intelligence partners, which includes;Why most AI initiatives fail because they as a solution looking for problems.The importance of aligning AI use cases to strategic goals, KPIs and measurable outcomes.Why speed rather than velocity leads to very little measurable progress.Why compelling POCs create false confidence before the real production challenges begin.The deployment gap: why robust, scalable and commercially viable AI is still hard.Why disconnected tools and poor workflow integration stall AI value realisation.The simple test for prioritisation: is this problem big enough to matter?Why the best AI use cases act as building blocks for future capability.How AI and UX together are driving true self-service insight generation.Why insight teams are evolving from answer providers to toolmakers.The growing importance of data governance, quality and observability in an AI-first world.How distributed insight creation can weaken corporate memory and knowledge curation.The skills shift toward UX, enablement, storytelling and decision intelligence.Practical build vs buy criteria in fast-moving and rapidly commoditising AI markets.Why operating models matters less than discipline, purpose and capability building.

We explore the artificial intelligence trends and innovations that could define the insurance industry in the years ahead, and what they mean in practice for businesses and brokers. Our new host, financial broadcaster and journalist Georgie Frost, is joined by Akhil Lalwani, Chief Data Officer at Allianz UK and Mary Bowie, Managing Regional Counsel UK at Verisk, a global insurance, data analytics and software solutions provider. In this episode, we discuss: How AI is changing insurance today and what that means for brokers New exposures, accountability and the importance of data stewardship and governance Evolving customer behaviour, regulation and how to ensure AI-driven decisions are fair, explainable and accountable What the future of insurance could look like as AI matures, and the role brokers and human judgement will continue to play Follow the series for notifications about future episodes. You can watch the podcast on Spotify and YouTube | https://www.youtube.com/playlist?list=PLoLD_-KfE3a3mR38xel2jA58gVo3J6-Gs Want to help shape our future episodes? Share your ideas, thoughts and comments here | https://info.allianz.co.uk/insurance-tomorrow-podcast-survey.html Connect with us on LinkedIn [https://www.linkedin.com/showcase/allianzukbroker] for broker insights and explore the Allianz Knowledge Centre [https://www.allianz.co.uk/broker/knowledge-centre.html] for webinars, articles and resources on business risks and other key topics. A podcast series by Allianz Insurance, produced by Fresh Air Production. See omnystudio.com/listener for privacy information.

Cash flow underwriting, explainable AI, and credit risk analytics are changing how lenders approve borrowers and set loan terms. Tedd Huff, CEO of fintech advisory firm Voalyre and founder of Fintech Confidential, sits down with Jamie Twiss, CEO of Carrington Labs, and Kasey Kaplan, Chief Product and Commercial Officer, to break down how behavioral signals in bank transaction data outperform traditional credit scores.Over 50 percent of loan applicants cannot produce a reliable credit score, leaving self-employed workers, gig earners, and younger borrowers locked out of the system. Carrington Labs uses billions of lines of transaction data to build personalized, explainable machine learning models per lender, per product, and per customer segment. The conversation covers their "control point" approach to AI, lifecycle underwriting beyond origination, open banking friction in the US, and a five-year outlook on embedded, agent-driven lending.FIND OUT MORE1️⃣ Map analytics to every step of your lending funnel to find exactly where borrowers drop off and why.2️⃣ Buy best-of-breed origination and servicing tools instead of building proprietary underwriting tech in-house.3️⃣ Start with off-the-shelf models, lend small, collect performance signal, then shift to custom models fast.4️⃣ Offer higher loan limits to borrowers who sync more accounts through open banking.5️⃣ Track how borrowers respond to financial scarcity and build those behavioral patterns into your credit criteria.GuestJamie Twiss LinkedIn: https://www.linkedin.com/in/james-twiss/Kasey Kaplan LinkedIn: https://www.linkedin.com/in/kaseykaplan/CompanyCarrington Labs: https://www.carringtonlabs.com/Carrington Labs LinkedIn: https://www.linkedin.com/company/carringtonlabs/Beforepay Group: https://www.beforepaygroup.comFintech ConfidentialPodcast: https://fintechconfidential.com/listenNotifications: https://fintechconfidential.com/accessLinkedIn: https://www.linkedin.com/company/fintechconfidentialX: https://x.com/FTconfidentialInstagram: https://www.instagram.com/fintechconfidentialFacebook: https://www.facebook.com/fintechconfidentialAbout the GuestsJamie Twiss is CEO of Carrington Labs and Beforepay Group. He began his career at McKinsey & Company, held senior banking roles including Chief Data Officer at a major Australian bank, and now leads the development of explainable AI credit risk models for lenders globally.Kasey Kaplan is Chief Product and Commercial Officer at Carrington Labs. With over 15 years across payments, program management, and fintech lending, he leads commercial execution across credit risk scoring, cash flow underwriting, and loan limit solutions.About the CompanyCarrington Labs is the AI and enterprise software division of ASX-listed Beforepay Group. The company builds explainable AI credit risk scoring, cash flow underwriting, and loan limit solutions for banks and non-bank lenders worldwide, having powered over 4 million loans through its sister business.About the HostTedd Huff, CEO of fintech advisory firm Voalyre and founder of Fintech Confidential. With 25+ years in fintech and payments, he brings entertaining and informative conversations focused on the people, tech, and companies that change how you pay and get paid.DD3 MediaFintech Confidential is a production of DD3 Media, a media creation, management, and production company delivering engaging fintech content globally.Chapters00:00 Episode Highlights01:04 Welcome to Fintech Confidential01:13 DFNS: Wallets as a Service (sponsor)02:34 Meet Carrington Labs04:48 Casey FinTech Origin06:05 Jamie Credit Risk Path07:59 Mission Beyond Scores10:16 Cashflow Underwriting13:35 Alternative Data Behaviors17:37 Built Inside Beforepay21:01 AI Control Points24:07 Deterministic Vs Inference28:35 Keeping Bias Out34:48 Real Client Turnaround36:44 Funnel Friction Signals38:25 Optimizing Drop Off39:21 Sky Flow: Building Fast and Secure (sponsor)40:21 Product Specific Risk Models42:32 From Shelf To Custom43:34 Model Retraining Workflow47:05 Siloed Versus Consortium48:59 Cashflow Behavior Insights50:25 Feature Engineering Matters51:41 Macro Shocks In Data56:07 Lifecycle Servicing Signals57:36 Limit Management Uplift58:55 Open Banking Pushback01:03:53 Crystal Ball AI Lending01:09:11 Advice And Wrap Up01:13:24 Hawk AI: Realtime Fraud Monitoring (sponsor)01:14:10 Disclaimer

In Episode 2, of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom was re-joined (over 2.5 years on) by Barry Panayi, Group Chief Data Officer at Howden, where they discuss, how the CDAO role continues to evolve and whether accountability is being diluted, which includes;Why the CDAO role is splitting between commercial outcomes and BAU leadershipWhy the biggest trade-off today is speed vs sustainable valueA pragmatic view on AI ownership: enterprise enablement vs bespoke buildThe risk of over-indexing on ROI and neglecting foundational data capabilityThe danger of the CDAO role being watered downWhy many organisations still hire CDAOs for the wrong mandateHow culture and incentives shape whether data leaders can succeedHow AI is making value measurement easier than traditional data workWhy proving quick wins can sometimes slow long-term progressWhy CDAO–CTO chemistry now matters more than job titlesThe leadership lesson: make the least bad decision with convictionWhy Barry wrote The AI of the Beholder as a leadership decision simulatorThe reality that there is rarely one “right” leadership choiceWhy future board opportunities for CDAOs require broader leadership breadthWhat boards actually value from ex-CDAO leaders in NED roles

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]---

In Episode 1, of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom was joined by Kevin Cassar, Chief Data Officer at TalkTalk, where they discuss how to accelerate the speed of value delivery, which includes;Why speed to value is becoming as important as ROI itselfThe macroeconomic and GenAI forces increasing pressure on delivery timelinesHow TalkTalk's data strategy is focused on profitability, trust, and customer satisfactionThe “triangle of value”: data, business action, and financial outcomesWhy federated data operating models can accelerate adoption and executionThe balance between experimentation and implementation at scaleHow governance should act as an enabler, not a blocker, to faster deliveryThe pyramid framework linking ExCo priorities to data products and modelsWhy culture, psychological safety, and upskilling are critical to speedThe five pillars of organisational readiness for faster value creationReal examples of reducing delivery from 12 months to under 8 weeksKevin's three practical leadership principles: vision, collaboration, and investing in peopleWhy ExCo maturity and sponsorship are the hidden drivers of speed to valueHow direct CEO access materially reduces time from idea to productionWhy poor data foundations still kill speed, regardless of AI ambitionWhy adoption velocity, not just build velocity, determines realised value

What does it take to rebuild a life around what you actually love — not what looks impressive from the outside? Laura Kurup spent years as a high-performing tech executive — Chief Strategy Officer at the SEC, Chief Data Officer at the New York Fed — and somewhere along the way, paddling took a backseat. Then came a rare and aggressive breast cancer diagnosis at 40. What followed was a two-year unraveling and rebuilding that led her back to the river, into whitewater instruction, and toward a life anchored in her own intuition. In this episode, Laura and Anna explore what it really means to face discomfort — not just the dramatic kind, but the low-level stuff we spend enormous energy ignoring. Laura shares how cancer cracked open the identity she'd been clinging to, why letting go of control on the Grand Canyon led to better lines, and what she's learned about expanding her window of tolerance — on the water and in life. In this episode, you'll explore: Why high performers often ignore discomfort until it becomes a five-alarm fire — and how to work with it at lower levels instead The mental shift that transformed Laura's lines on the Grand Canyon: from controlling the plan to reading and running How identity attachment (like being "the canoeist who doesn't flip") can quietly limit your growth What a cancer diagnosis revealed about the difference between performing a life and actually living one Laura's take on using AI intentionally — and why she compares it to ketchup The river metaphor she brought home from the Grand: following the bubble line If you've ever pushed discomfort down until it exploded, played it too safe and ended up stuck on the rocks, or wondered what life might look like if you gave yourself permission to follow your intuition — this episode is for you. The river gives you a put-in and a take-out. How you run it is up to you.

Today on the Driven by Data Dilemmas Show, host Catherine Dowden-King is joined by Alexandra Sidgreaves, Chief Data Officer for Zurich InsuranceToday's theme is all about 'Transformation the right way' - together, Alex & Catherine talk about the errors of forgetting people in great big transformations, how AI is shaking up the entire world, not just data teams and importantly, how to avoid getting someone fired on your first day!Listen wherever you get your podcasts!Remember, you can send your own dilemmas to community@orbitiongroup.com, and Catherine will gladly read them to our expert guests to answer and provide their own insight on.Driven by Data Dilemmas is the spin-off show from the Driven by Data Podcast, released every Thursday. Catherine Dowden-King is joined by some of our best-loved senior leaders in the data, analytics and AI space to ask them to share their experiences, advice and thoughts on data dilemmas.

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

In this episode, we explore how AI is fundamentally changing the way people find and buy products online. Cole Casperson, Partner and Chief Data Officer at Crank Tank, discusses the shift from three-word searches to long, intent-driven AI conversations. He explains how brands can move beyond traditional SEO to ensure they are the ones recommended by AI tools. You will learn how to use diagnostic tools to see what AI thinks of your brand and how to win against bigger competitors in this new era of discovery.Topics discussed in this episode:  How AI shifts search from keywords to intent.Why user queries are getting much longer.What makes AI discovery different from SEO.How to use diagnostics to see AI rankings.Why competing on ideas beats keyword stuffing.What Amazon can teach brands about AI commerce.Why ignoring AI creates a compounding risk.How semantic understanding boosts conversion rates.What the Universal Commerce Protocol means.How challenger brands can outrank giants.Links & Resources Website: https://cranktank.net/LinkedIn: https://www.linkedin.com/company/cranktank/X/Twitter: https://x.com/WeAreCrankTankInstagram: https://www.instagram.com/wearecranktank/Get access to more free resources by visiting the show notes at https://tinyurl.com/ccpvaneaI'd love your feedback. Tap the the link to send me a text. ______________________________________________________ LOVE THE SHOW? HERE ARE THE NEXT STEPS! Follow the podcast to get every bonus episode. Tap follow now and don't miss out! Rate & Review: Help others discover the show by rating the show on Apple Podcasts at https://tinyurl.com/ecb-apple-podcasts Join our Free Newsletter: https://newsletter.ecommercecoffeebreak.com/ Support The Show On Patreon: https://www.patreon.com/EcommerceCoffeeBreak Partner with us: https://ecommercecoffeebreak.com/partner-with-us/

This week on the podcast, Stuart Hatcher, IBA's Chief Economist and Chief Data Officer, and Jonathan McDonald, Manager – Classic & Cargo Aircraft, examine how the current Middle East conflict is impacting global aviation and asset values. They reflect on previous regional conflicts and their implications for airport security, and how today's developments, with rising oil prices, are affecting airline operations across the world.Our speakers also break down IBA's latest Engine and Lease Rate updates and explore the Trading Market Outlook for the year ahead, with a particular spotlight on P2F conversions.Tune in for clear analysis, market context, and expert insights you won't want to miss. To read the articles discussed today, please visit our website: https://www.iba.aero/resources/#articles Sign up for the newsletter - https://www.iba.aero/sign-up/LinkedIn - https://www.linkedin.com/company/iba-aviation-consultancy/YouTube - https://www.youtube.com/channel/UCSkPhTf-05htY99V79fklMAWebsite - www.iba.aero

Today's guest is Dave Shuman, Chief Data Officer at Precisely.Precisely is a global leader in data integrity, helping organisations trust, manage and maximise their data so they can power better decisions, analytics and AI at scale.Despite the explosion of AI investment, many organisations are still struggling to turn AI ambition into measurable business outcomes, and in this episode, we explore why.Dave shares insights from Precisely's 2026 State of Data Integrity and AI Readiness report, and explains why so many companies believe AI is aligned to business goals, yet only a small percentage actually link those initiatives to real KPIs.We discuss:• Why most AI initiatives fail to deliver measurable results• Where the disconnect between AI strategy and execution really begins• What the 2026 Data Integrity report reveals about enterprise AI readiness• How organisations can turn AI confidence into real operational capability• Whether the Chief Data Officer should be a data custodian or a business strategistIf you are building data platforms, scaling AI initiatives, or leading data strategy inside an organisation, this conversation is packed with insights.

In this episode of Power House, Dave Crosby, Chief Data Officer at Compass, joins the conversation to discuss how data and technology are reshaping the real estate industry. Crosby explains the role of a Chief Data Officer and how organizing data into actionable insights can help agents make better decisions in a rapidly changing market. The discussion explores the growing influence of AI, why its effectiveness depends entirely on data quality, and how companies must build cultures of accountability to successfully adopt new technologies. The episode also highlights Compass's entrepreneurial approach to supporting agents, the importance of market insights, and why professionals who combine strong networks with data-driven decision-making will have an advantage in the years ahead. Related to this episode: Dave Crosby's LinkedIn Compass The Power House podcast brings the biggest names in housing to answer hard-hitting questions about industry trends, operational and growth strategy, and leadership. Join HousingWire's Zeb Lowe every Thursday morning for candid conversations with industry leaders to learn how they're differentiating themselves from the competition. Hosted and produced by the HousingWire Content Studio.

Today on the Driven by Data Dilemmas Show, host Catherine Dowden-King is joined by Dan Kellett, Chief Data Officer for Carmoola! Together they talk about Leadership Legends vs Leadership Lows!Dan walks through his thoughts on what makes a leader a legend, vs a loser! They discuss stories about candidates crying during interviews and team members making rookie errors.Dan shares some candid experiences of when it all went wrong, and he needed to walk away from his desk.Listen wherever you get your podcasts!Remember, you can send your own dilemmas to community@orbitiongroup.com, and Catherine will gladly read them to our expert guests to answer and provide their own insight on.Driven by Data Dilemmas is the spin-off show from the Driven by Data Podcast, released every Thursday. Catherine Dowden-King is joined by some of our best-loved senior leaders in the data, analytics and AI space to ask them to share their experiences, advice and thoughts on data dilemmas.

In this episode, I talk with Nick Hart, President and CEO of the Data Foundation, about the rapidly changing landscape of federal data, statistical agencies, and evidence-based policymaking. We explore how the Evidence Act reshaped government data infrastructure, why privacy protections and data governance matter more than ever, and what's been happening behind the scenes over the last year as agencies faced staffing cuts, data removals, and unprecedented political pressure. Nick explains how government data systems actually work, why the U.S. model is both admired and strained, and what a “Data System 2.0” might look like in the future. We also discuss state and local data roles, the risks of politicizing data, and two public-facing initiatives from the Data Foundation: the Evidence Act Hub and the People's Data 100. This is a wide-ranging conversation about trust, transparency, and why government data quietly underpins far more of our lives than most people realize.Subscribe to the PolicyViz Podcast wherever you get your podcasts.Become a patron of the PolicyViz Podcast for as little as a buck a monthCheck out the Data Foundation and their People's Data 100 project! Follow me on Instagram, LinkedIn, Substack, Twitter, Website, YouTubeEmail: jon@policyviz.com

Data leaders are being asked to ship real AI outcomes while the foundations are still messy. In this conversation, Dave Shuman, Chief Data Officer at Precisely, breaks down what actually determines whether AI adoption sticks, from hiring “comb shaped” talent to building trusted data products that make AI outputs believable and usable.If you are building in data, AI, or analytics, this episode is a practical map for what needs to be true before AI can move from demos to dependable, repeatable impact.Key TakeawaysComb shaped talent beats narrow specialization, AI work rewards people who can span multiple skills and collaborate wellAdoption is a trust problem, and trust starts with data integrity, lineage, context, and a semantic layer that business users can understandOpen source drives the innovation, commercialization makes it safe and usable at enterprise scale, especially around security and supportData must be fit for purpose, start every AI project by asking what data it needs, who curates it, and what the known warts areHumans are still the last mile, small workflow choices can make adoption jump, even when the model is already accurateTimestamped Highlights00:56 The shift from T shaped to comb shaped talent, what modern AI teams actually need to look like05:36 Hiring for team fit over “world class” niche skills, and when to bring in trusted partners for depth07:37 How open source sparks the ideas, and why enterprises still need hardened, supported versions to scale11:31 Where AI adoption is today, why summarization is only the beginning, and what unlocks “AI 2.0”13:39 The trust stack for AI, clean integrated data, lineage, context, catalog, semantic layer, then agents19:26 A real adoption lesson from machine learning, and why the human experience decides if the system winsA line worth stealing“You do not just take generative AI and throw it at your chaos of data and expect it to make magic out of it.”Pro Tips for data and AI leadersHire and build teams like Tetris, fill skill voids across the group instead of chasing one perfect profileUse partners for the sharp edges, but require knowledge transfer so your team levels up every engagementMake adoption easier by designing for human behavior, sometimes the smallest workflow tweak beats more accuracyBuild governed data products in a catalog, then validate AI outputs side by side with dashboards to earn trust fastCall to ActionIf this helped you think more clearly about AI adoption, talent, and data foundations, follow the show and turn on notifications so you do not miss the next episode. Also, share it with one data or engineering leader who is trying to get AI out of pilots and into real workflows.

No novo episódio do Podcast Canaltech, Marcelo Fischer conversa com Daniel Avancini, cofundador e Chief Data Officer da Indicium, sobre o Moltbook e o OpenClaw, plataformas que permitem que agentes de inteligência artificial interajam entre si, como em uma espécie de “rede social dos robôs”. Durante a entrevista, Daniel explica como essas tecnologias funcionam na prática, por que elas chamaram tanta atenção no mercado e quais são os riscos e oportunidades dos agentes de IA no dia a dia das pessoas e das empresas. A conversa também passa por temas como segurança digital, produtividade, futuro do trabalho e os limites da inteligência artificial atual, sem cair em exageros ou ficção científica. Você também vai conferir: China dá mais um passo rumo aos carros do futuro e o volante pode ser o próximo a mudar, Samsung lança tela 3D que dispensa óculos e aposta no futuro das vitrines digitais e Embraer amplia sua presença global com acordo para carros voadores no Japão. Este podcast foi roteirizado e apresentado por Fernada Santos e contou com reportagens de Danielle Cassita e Renato Moura, sob coordenação de Anaísa Catucci. A trilha sonora é de Guilherme Zomer, a edição de Leandro Gomes e a arte da capa é de Erick Teixeira.See omnystudio.com/listener for privacy information.

Mark Wopata, Chief Data Officer and EVP of Finance at EquipmentShare (EQPT), which just went public on Nasdaq on Friday. The company is a vertically integrated construction company that is driving “productivity, safety, and efficiency.” It rents equipment for construction projects but also has a tech platform – Mark argues that the company should be viewed more as a tech IPO than a cyclical. “We serve the biggest and largest customers and jobsites in the world,” he says, with visibility into the trillions of dollars.======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

In this episode, Karen is joined by Kinnari Ladha, Chief Data Officer, to explore why great data work often fails to land — and how communication turns data leaders into trusted influencers, making it their superpower.    Drawing on over two decades in data—from hands-on analyst to executive leader—Kinnari shares candid stories of building “perfect” dashboards that didn't get used, and how those moments reshaped her approach to leadership.   She explains why career progression in data requires doing more work bringing people along, how tailoring messages to different audiences and shifting from reporting to storytelling changes impact, and why focusing on outcomes — not outputs — transforms how data is valued.   The conversation also dives into the role of trust in influence: how small, consistent actions build credibility, why listening comes before strategy, and how data teams can move from being seen as service providers to true business partners. With practical habits like weekly “highs and lows” and reframing data in business language, this episode is packed with insights you can apply straight away.

Ep 143- Encore Episode: AI in Population Health: Value vs Hype with Dr. David Nash As the year comes to a close, enjoy a throwback to one of their most popular episodes of 2025. On this episode Dan explores the evolving role of artificial intelligence (AI) in population health. As AI continues to dominate industry conversations and drive vendor offerings, healthcare leaders are faced with questions: What is real, what is hype, and where does the value lie? Dr. David Nash, Founding Dean Emeritus of Jefferson College of Population Health and a nationally recognized thought leader in value-based care and population health, joins the conversation. Additionally, Rick Howard, a seasoned Chief Data Officer and AI Strategist contributes to the conversation with his deep expertise in driving data-driven innovation across healthcare organizations. Together, they break down common misconceptions, highlight the most promising AI applications in care delivery, and offer practical insights into how health systems, providers, and payers can responsibly integrate AI to drive meaningful outcomes and return on investment (ROI). To stream our Station live 24/7 visit www.HealthcareNOWRadio.com or ask your Smart Device to “….Play Healthcare NOW Radio”. Find all of our network podcasts on your favorite podcast platforms and be sure to subscribe and like us. Learn more at www.healthcarenowradio.com/listen

As the year comes to a close, we thought you would enjoy a throwback to one of our most popular episodes of 2025. In this episode of Value-Based Care Insights, host Daniel Marino explores the evolving role of artificial intelligence (AI) in population health. As AI continues to dominate industry conversations and drive vendor offerings, healthcare leaders are faced with questions: What is real, what is hype, and where does the value lie? Dr. David Nash, Founding Dean Emeritus of Jefferson College of Population Health and a nationally recognized thought leader in value-based care and population health, join the conversation. Additionally, Rick Howard, a seasoned Chief Data Officer and AI Strategist contributes to the conversation with his deep expertise in driving data-driven innovation across healthcare organizations. Together, they break down common misconceptions, highlight the most promising AI applications in care delivery, and offer practical insights into how health systems, providers, and payers can responsibly integrate AI to drive meaningful outcomes and return on investment (ROI).

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

Douglas Matty is exiting his role as the Pentagon's chief digital and artificial intelligence officer and moving on to focus on the Trump administration's “Golden Dome for America” missile defense initiative, DefenseScoop has learned. Principal Deputy CDAO Andrew Mapes will lead the department's AI hub in an acting capacity until a new CDAO is hired. Ahead of reaching full operational capacity in 2022, the AI-accelerating office merged and integrated multiple technology-focused predecessor organizations at the Pentagon, including the Joint Artificial Intelligence Center (JAIC), Defense Digital Service (DDS), Office of the Chief Data Officer, and the Maven and Advana programs. The DOD's vision and priorities for the CDAO have been reconfigured several times since its inception. And while AI is a major priority for the U.S. government under President Donald Trump, the Pentagon's CDAO office has seen an exodus of senior leaders and other technical employees this year. Matty's departure also comes as the office is hustling to execute on a range of DOD-wide efforts to speed up the delivery and fielding of data analytics, automation, computer vision, machine learning and other next-generation AI capabilities for military and civilian personnel. Last week, Pentagon leaders unveiled a new purpose-built platform — GenAI.mil — to provide commercial options directly to most of its workforce on their desktops. The Centers for Medicare & Medicaid Services has tapped ID.me to verify the identities of beneficiaries on Medicare.gov, according to a Tuesday announcement from the identity-proofing company. ID.me will be available as an option for identity verification and sign-in on Medicare.gov starting in early 2026, per the release. The deal adds to the growing number of federal programs opting to use the digital identity service that leverages facial recognition technology and has been the subject of some controversy in the past. Already, ID.me is used at 21 federal agencies, including the Social Security Administration and Department of Veterans Affairs, per the release. Opting in means an ID.me user could sign in with the same credentials at any of the other federal, state or private-sector entities that use the service, the company said in a statement to FedScoop.

In this special episode, host Cindi Howson pulls together the most useful, and hard-won, lessons from a year of conversations with Data Chiefs leading the GenAI charge. With generative and agentic AI no longer a side experiment, this episode spotlights five practices early adopters can rely on to move from pilots to profit. Expect straight talk on what to prioritize, how to bring people with you, and how to scale AI with the trust, literacy, and guardrails that make impact stick.Key Moments:Tying AI to Real Dollars with Anand Iyer, Ecolab (02:10): Anand cuts through the GenAI FOMO and brings everything back to a simple survival test: if you can't draw a straight line from an AI initiative to top-line growth or bottom-line savings, it won't last. His lesson is a sharp reminder that “cool” doesn't scale, value does. Leading Through Ambiguity with Karen Stroup, WEX (06:01): Karen names what everyone's feeling: ambiguity is paralyzing. She explains how leaders earn trust by shrinking the unknown into learnable, bite-sized experiments and creating the psychological safety people need to engage instead of resist.Building Practical AI Literacy at Scale with Josh Cunningham, Lloyds Banking Group (12:42):  Josh shares how Lloyds Banking Group makes literacy impactful by meeting people where they are. Rather than one-size-fits-all training, they pair broad fundamentals with role-specific learning so every business unit can build confidence in ways that match their actual work. Scaling Responsible Agentic AI with Noelle Russell, AI Leadership Institute (25:09): Noelle steps in with a practical framework for building agentic systems that don't go rogue. She walks through the POET framework and stresses that responsible AI isn't a final checkpoint. It's something you embed from the first idea to production, with guardrails that protect people and outcomes.Embedding AI Where Work Happens with Ilan Twig, Navan (32:35): Ilan tells a classic early-adopter story: start with a business problem, move fast, and be ruthless about what needs building versus buying. His lesson is that AI wins when it's inside the workflow, supporting decisions at the point of impact rather than living in a separate tool. Don't Let Perfection Stall Progress with Ketan Karkhanis, ThoughtSpot (40:59): Ketan shares a culture gut-check: waiting for perfect metrics, perfect KPIs, or perfect clarity is how progress dies. He argues for visible, trust-building iteration, because in AI, speed to learning beats speed to certainty. Key Quotes:“One thing that people sometimes forget is that at the end of the day, it's all about are we either saving money or making money? And are you able to show that in the bottom line or the top line in a measurable way?” - Anand Iyer“I don't think there's any chief anything officer that should not be considering AI today. I think if you're not considering AI, you are at the risk of being disrupted because you're not going to be learning at the pace with the rest of the industry, and there's someone out there looking for a better way.” - Karen Stroup“It's trying your best to meet people where they are… Finding a way to anchor the [AI] learning to something that's relevant to their day-to-day role is always going to make it land better.” - Josh Cunningham“ When people lose 70% of their trust in you, they just don't buy from you, they don't work for you, they don't talk about you… and your business starts to die. I think that trust component is a human component… and it is underpinning all the other philosophies that I have.” - Noelle Russell“When you asked me about how to educate yourself on AI, I think that companies must make a decision, and quickly, this or that.” - Ilan Twig“ Don't let perfection be the enemy of progress.” - Ketan KarkhanisGuest Bios Anand IyerAnand Iyer is the SVP, Chief Data Officer at Ecolab, where he leads the company's global data and analytics strategy. Based in Mechanicsburg, Pennsylvania, he oversees enterprise data governance, business intelligence, engineering, and advanced analytics to accelerate Ecolab's digital transformation. Since joining in 2018, Anand has held several senior roles, including VP of Enterprise Architecture and VP of Architecture for Commercial Digital Solutions, helping to scale IoT and data-driven platforms across the organization.Karen StroupKaren joined WEX in 2022 as Chief Digital Officer, a newly created role. She brings more than 15 years of experience leading product management, digital, and innovation organizations focused on software as a service offerings, primarily in financial services.Josh CunninghamJosh Cunningham is the Group Head of Data and AI Culture at Lloyds Banking Group, where he leads the Data Culture Pillar—one of five strategic pillars in the Group's data strategy. He is focused on embedding data-driven mindsets across the organization and empowering teams to unlock the full value of data.Noelle RussellNoelle Russell is a multi-award-winning speaker, author, and AI Executive who specializes in transforming businesses through strategic AI adoption. She is a revenue growth + cost optimization expert, 4x Microsoft Responsible AI MVP, and named the #1 Agentic AI Leader in 2025. She has led teams at NPR, Microsoft, IBM, AWS and Amazon Alexa, and is a consistent champion for Data and AI literacy and is the founder of the "I ❤️ AI" Community teaching responsible AI for everyone.Ilan TwigIlan Twig is the co-founder and Chief Technology Officer (CTO) of Navan, the leading modern travel and expense management platform, globally. As CTO, Ilan drives Navan's product development and engineering efforts, leveraging cutting-edge technologies — including AI — to enhance user experience and operational efficiency. Ketan KarkhanisKetan Karkhanis is the CEO of ThoughtSpot, the Agentic Analytics Platform company. Prior to joining the company in September 2024, Ketan was the Executive Vice President and General Manager of Sales Cloud at Salesforce. He returned to Salesforce in March 2022 after his time as the COO of Turvo, an emerging supply-chain collaboration platform.  Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Learn how one of the world's biggest restaurant companies is turning data and AI into a recipe for global innovation. Cameron Davies, Chief Data Officer at Yum! Brands, shares how he's combining strategy, technology, and change management to drive gobal growth. He explains how Yum! is building AI literacy from the top down, reimagining operations with generative AI, and partnering with NVIDIA to scale innovation. Cameron reveals what true data leadership looks like, balancing bold ideas with business impact, and proving transformation starts with people, not technology.Key Moments:Start with the Business Problem, Not the Tech (04:27): Cameron recalls advice from a mentor, “start with the business problem down, not the technology up.” He emphasizes that innovation only matters when it solves real business challenges, reminding data leaders not to get enamored with the “cool” factor of technology at the expense of impact.Balancing Global Scale with Local Agility (07:45): Cameron unpacks the challenge of scaling analytics across 160 countries and four major brands, 98% of which are franchise-owned. He explains how Yum! balances centralization and autonomy, ensuring smaller markets have a voice while global teams leverage shared technology and insights.Building AI Literacy from the Top Down (13:44): Cameron describes Yum!'s investment in digital upskilling, from Harvard-led training for executives to hands-on AI workshops for employees. He outlines how the company is embedding AI tools, like Microsoft Copilot and ChatGPT, into daily workflows to build confidence and accelerate adoption.Digitizing the Restaurant: Byte By Yum! (17:18): Cameron introduces Byte By Yum!, a suite of proprietary software that simplifies restaurant operations. He explains how it unifies e-commerce, point-of-sale, voice AI, and kitchen systems to make running a restaurant easier and more efficient in an increasingly complex digital environment.Partnering with NVIDIA to Power the Future (25:12): Cameron shares how Yum!'s strategic partnership with NVIDIA is fueling next-generation restaurant innovation. He reveals how the collaboration gives Yum! early access to cutting-edge AI engineering and product strategy, extending his team's capabilities with some of the best minds in the field.Key Quotes:“Technology's actually a whole lot easier than people, and the more successful the people are, the harder it is to get them to change.” - Cameron DaviesThe business problem is the business problem. You never have as much data as you want, as fast as you want, as cleanly as you want. People are always people, but the opportunities are always the opportunities.” - Cameron Davies“I think sometimes we get so enamored with the technology… We forget it's all in the service of a business problem.” - Cameron DaviesMentionsByte By Yum!Yum! Brands to accelerate AI innovation in an industry-first collaboration with NVIDIA2025 AI & Data Leadership Executive Benchmark SurveyGuest Bio Cameron Davies currently serves as the Chief Data Officer at Yum! Brands since July 2020. Prior to this role, Cameron held the position of Senior Vice President of Corporate Decision Sciences at NBCUniversal, Inc. from September 2013 to July 2020, overseeing the Corporate Management Sciences and NBCU News Group Insights teams, focusing on advanced analytics and data strategies. Cameron's career at Walt Disney Co. spanned from October 1996 to September 2013, where responsibilities included leading the Walt Disney World Resort Forecast and Planning teams and managing global Yield Management. Cameron established and led the Corporate Center of Excellence in Management Science and Integration, collaborating with Disney executives on analytics initiatives. Earlier in the career, from May 1989 to June 1996, Cameron served as a Professor of Finance and Accounting at Pensacola Christian College, teaching various business courses. Cameron holds a Master of Business Administration (MBA) in Marketing Research and Operations Management from the UWF Lewis Bear Jr. College of Business and a Bachelor of Science in Business/Accounting from Pensacola Christian College. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

This is the takeaway episode with Danielle Crop is the EVP Digital Strategy & Alliances at WNS, former Chief Data Officer of Albertson and also the former Chief Data Officer of American Express where we chat about how data leaders and practitioners need to be thinking about AI and how to focus on what truly provides business value, who will be building models, and how roles will be evolving in organizations. If you want to learn what is going through the mind of an executive data leader, this episode is for you. If you like what you heard, you should check out the full episode!See omnystudio.com/listener for privacy information.

Tim and Juan chat with Danielle Crop is the EVP Digital Strategy & Alliances at WNS, former Chief Data Officer of Albertson and also the former Chief Data Officer of American Express about how data leaders and practitioners need to be thinking about AI and how to focus on what truly provides business value. We get into who will be building models, and how roles will be evolving in organizations. If you want to learn what is going through the mind of an executive data leader, this episode is for you!See omnystudio.com/listener for privacy information.

What if artificial intelligence could help end world hunger? In this special episode recorded live from GITEX Global in Dubai, I sit down with Magan Naidoo, Chief Data Officer at the United Nations World Food Programme, to discuss how data and AI are transforming humanitarian work at scale. Magan paints a powerful picture of the global food security crisis, where hundreds of millions of people face hunger across more than 80 countries. He explains how the World Food Programme is using technology to predict food shortages, optimise supply chains, and deliver aid faster and more effectively. Behind every algorithm sits a simple goal: getting food to those who need it, when they need it most. We explore how AI models are helping the organisation make sense of enormous datasets, identifying patterns that humans alone could not process quickly enough. From predicting drought-related crop failures to reducing the cost of food delivery through smarter routing, Magan reveals how data-driven decisions are saving both time and lives. He also shares the organisation's commitment to ethical AI, strong data governance, and privacy protection in every region they operate. As the only UN agency with a formal AI strategy, the World Food Programme is setting a benchmark for how large-scale institutions can use technology responsibly and effectively. Magan's story highlights the importance of trust, collaboration, and resilience in a mission where failure is not an option. Could AI truly be the key to solving one of humanity's oldest challenges? And what lessons can every organisation learn from how the World Food Programme blends compassion with computation? Tune in, then share your thoughts.