Podcasts about chief data officers

Carole Lawson, Co-Founder and Chief Data Officer of MarketStorm, explains why the traditional marketing funnel no longer reflects how customers make decisions, and why marketers need a new approach to trust, influence, and measurement. SHOWPAGE: www.ninjacat.io/blog/wgm-podcast-how-trust-is-replacing-the-marketing-funnel  What Gets Measured is brought to you by NinjaCat — the enterprise marketing intelligence platform built for agencies, media companies, and brands that need to do more with their data. NinjaCat unifies your marketing data, automates reporting, and powers AI Agents that move your team from insight to action — faster, smarter, and at scale.

Most CDOs talk about data products. Very few approach them the way a product manager would starting with customer need, validating demand, and building only what people will actually use.

In this episode of Lunch with Leon, Leon speaks with Lauren Sager Weinstein, Chief Data Officer at TfL. Lauren discusses the rapid rise of AI, the risk of it magnifying human bias, and the ethics of transparency in journey planning tools optimised for commission over commuters. She also reflects on the trust Londoners place in the TfL brand, and the importance of bringing young people, especially women and those from underrepresented backgrounds, into transport and data careers.

When the Army launched its data operations center in April, the goal was to create a centralized service that will reduce the burden on warfighters. Over the long term, A-DOC hopes to make it easier for warfighters to use artificial intelligence and machine learning tools by turning raw data into refined intelligence. For more on the service's plans for the Army Data Operations Center, Federal News Network executive editor Jason Miller caught up with David Markowitz, the Army's chief data and analytics officer at the recent AFCEA TechNet Cyber conference.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Data innovation isn't about buying better tools. As Dallas and Cleveland show, it's about how leaders listen, support risk-taking, and work alongside agencies. The result? A culture where innovation thrives, and scales. Host Stephen Goldsmith speaks with Brita Andercheck, Chief Data Officer in Dallas, and Liz Crowe, Chief Innovation and Technology Officer in Cleveland, about how they drive transformative change across city governments. From reporting structures to how you listen to agencies to going out into the field yourself, they share the unglamorous work that actually drives transformation. In this episode, you'll learn: How to strike the balance between centralization and decentralization Why a problem-first approach beats technology-first Why data teams should measure their ROI The importance of collaboration and relationships How to create psychological safety so teams "fail smart" Guest: Dr. Brita Andercheck – Chief Data Officer and Director of the Office of Data Analytics and Business Intelligence, city of Dallas Dr. Liz Crowe – Chief Innovation and Technology Officer, city of Cleveland Listener Survey: bit.ly/datasmartpod Music credit: Summer-Man by Ketsa About Data-Smart City Solutions Data-Smart City Solutions, housed at the Bloomberg Center for Cities at Harvard University, is working to catalyze the adoption of data projects on the local government level by serving as a central resource for cities interested in this emerging field. We highlight best practices, top innovators, and promising case studies while also connecting leading industry, academic, and government officials. Our research focus is the intersection of government and data, ranging from open data and predictive analytics to civic engagement technology. We seek to promote the combination of integrated, cross-agency data with community data to better discover and preemptively address civic problems. To learn more visit us online and follow us on LinkedIn.

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

Ein Patient, mehrere Kliniken, in jedem Haus ein anderes klinisches Informationssystem: Wie führt ein Klinikkonzern seine Patientendaten überhaupt zusammen? Und warum ist genau das die Voraussetzung dafür, dass KI im Krankenhaus mehr wird als ein eingekauftes Tool? Die Sana Kliniken sind über Zukäufe gewachsen. Jedes übernommene Krankenhaus hat seine eigene IT mitgebracht, abgekapselt wie eine kleine Insel. Funktional sieht es überall gleich aus: ein klinisches Informationssystem (KIS) als Primärsystem, dazu Laborsystem, Radiologiesystem und Spezialsysteme für einzelne Funktionsbereiche, vom Herzkatheter bis zur Gastroenterologie. Nur sind die Systeme in jedem Haus andere. Dazu kommen sehr unterschiedliche Häuser: Maximalversorger mit 800 bis 900 Betten neben spezialisierten Herzzentren und kleinen Grund- und Regelversorgern. Das Ergebnis nennt Gunnar H. eine extreme Heterogenität, und zwar nicht nur bei den Daten, sondern zuerst bei Systemen und Prozessen. Gunnar H. ist Chief Data Officer der Sana Kliniken AG. Er kommt aus der Volkswirtschaftslehre, hat Wirtschaftsinformatik draufgesattelt, Data Warehouses in Versicherung und Telekommunikation gebaut, danach bei ProSiebenSat.1 Media, und ist seit fast 17 Jahren im Gesundheitswesen. Sein Data Office sitzt heute direkt beim Vorstand, im selben Ressort wie die Konzern-IT. Im Gespräch mit Jonas Rashedi geht es um die Bausteine, mit denen ein solcher Konzern das Datenproblem angeht: das Clinical Data Repository als zentrale, standardisierte klinische Datengrundlage. Den Master Patient Index, mit dem sichtbar wird, dass derselbe Patient in zwei Kliniken war, davor in einer ambulanten Praxis und danach in der Nachsorge. Die Schnittstellenstandards HL7 und FHIR, die es praktisch nur im Gesundheitswesen gibt und an denen deshalb niemand vorbeikommt. Und vorkonfigurierte klinische Datenmodelle statt selbstgebauter Data Warehouses, unter anderem aus der 2015 gegründeten Medizininformatik-Initiative und ihrem Kerndatensatz. Zwei Punkte machen die Folge sperriger als die übliche KI-Erzählung. Erstens Datenqualität: Ärztinnen und Ärzte sind nicht da, um zu dokumentieren, damit Data Scientists später schöne Auswertungen bauen. Wenn strukturierte Eingabe nicht ergonomisch ist, entsteht sie auch nicht. Zweitens die Reihenfolge. Sana hat eine KI-Community gegründet und eine KI-Strategie etabliert, aber der Satz, der hängen bleibt, ist nüchtern: Ohne Daten keine KI. Erst die Datengrundlage, dann die Tools. Außerdem: warum Data Governance nur funktioniert, wenn man sie über den Nutzen kuratierter Daten verkauft statt über Regeln. Wann es überhaupt sinnvoll ist, Data aus der IT herauszulösen. Wo KI in der Codier-Optimierung und bei der DRG ansetzen könnte, weil dort heute noch händisch gearbeitet wird. Und welchen Rat Gunnar einem Data Engineer gibt, der morgen anfängt: einen Technologie-Stack wirklich beherrschen statt drei halb, und danach die Daten verstehen. Zum Schluss die beiden Standardfragen. Privat wartet Gunnar darauf, dass auch die privaten Krankenversicherer eine elektronische Patientenakte anbieten. Und als Filmtitel für sein Data-Game wählt er Der Marsianer: ein Riesenproblem, das man in kleinen, datenbasierten Schritten löst, mit dem großen Ganzen im Blick. Teil 2 von 2. In Teil 1 spricht Jonas Rashedi mit der Chief Transformation Officerin der Sana Kliniken über die Transformationsperspektive. Anzeige: Quest Software, Trusted Data Platform, quest.com/mydata

In Episode 16 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Priya Enefer, Chief Information Officer at Hakluyt & Company, where they discuss why the tension between CIOs and CDOs isn't a people problem, but an organisational design problem.They explore how operating models, accountability, product thinking and executive alignment determine whether technology, data and AI become genuine competitive advantages or simply create duplication, politics and confusion.They also discuss:Why the tension between CIOs and CDOs is usually created by organisational design rather than the people themselves.Why every CIO role looks different and how organisational context should define the mandate.Why organisations should define accountabilities before they hire executives or choose job titles.Whether every organisation actually needs a Chief Data Officer.Why technology, data and product leadership must operate as one team if transformation is going to succeed.The lessons learned moving from Chief Product Officer to CIO and why she still thinks like a product leader.How separate technology, product and data strategies create duplication, confusion and competing priorities.Why product operating models fundamentally outperform traditional project delivery.Who should own AI and why there is no universal answer.Why many organisations are measuring AI activity instead of AI value and repeating mistakes made during previous technology waves.How AI risks becoming another executive land grab unless organisations are crystal clear on ownership and accountability.Why centralised data teams can become ivory towers.What the private sector can learn from government about delivering successful transformation.Why dashboards and insights in isolation is not leadership.Whether technology, product and data leadership roles will ultimately converge or simply become much more interconnected.Why organisational design, incentives and culture will ultimately matter far more than whichever AI model an organisation chooses.Thanks to our sponsor, Data & AI Literacy Academy.Data & AI Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity.If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data & AI literacy first.At Data & AI Literacy Academy, they don't just teach data skills. They empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. They're bridging the gap between business and data teams, so they can all work towards aligned outcomes.From those taking their first steps in data & AI literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that they offer. They embed a deep data culture shift through a transformative change management programme.They take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve.Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com.

Most CDOs are still measuring AI readiness with a BI-readiness checklist and it's costing them. 80 to 90 percent of enterprise data is unstructured, sitting outside every governance program built over the last three decades.

What if the biggest obstacle to AI success isn't the technology? It's the way organizations are structured. At Data Citizens on the Road by Collibra, I sat down with Joyce Snelders Senior Manager at Deloitte on The Ravit Show to discuss what organizations are experiencing as they move from AI experimentation to enterprise-wide adoption.A few key takeaways from our conversation:* Data governance has gone from a "nice to have" to a business priority because AI is only as good as the data behind it.* Many organizations are building AI agents without common standards, creating duplicate efforts and inconsistent outcomes across teams.* Chief Data Officers are increasingly becoming AI leaders, taking responsibility for both data and AI strategies.* The next phase of enterprise AI is not just about technology. It is about governance, operating models, and change management.* Leaders should start preparing for a future where digital FTEs work alongside human employees.One statement from Joyce stood out:Organizations don't have an AI problem. They have a governance and operating model problem.The companies that solve that challenge first will be the ones that scale AI successfully.#DataCitizens #Collibra #AI #DataGovernance #AIGovernance #EnterpriseAI #DataLeadership #TheRavitShow

The future of Meta with Chief Data Officer Alex Schultz. Alex Schultz, Chief Data Officer at Meta, joins Sam for a wide-ranging conversation covering why Meta believes agentic commerce could become the majority of its business, why stablecoins are a foregone conclusion, and why human taste and creative conviction matter more in an AI world than ever before. - Timecodes: 0:00 - Cold open: Warren Buffett's "man, machine, and dog" quote 0:29 - Show intro & Alex Schultz background (CMO → Chief Data Officer at Meta) 8:52 - Using data to find non-average insights: Asia trends, shoppertainment, conversational commerce 11:33 - Staying ahead of the puck: Stories, Messenger, missing TikTok's rise 13:32 - Creators, shoppertainment, and human vs. AI balance 18:00 - Bad AI work is bad work: Alex's Claude/Antarctic cruise data project 20:16 - Taste and conviction matter more than ever; Buffett quote revisited 23:07 - Fear-mongering vs. job creation (radiologists, software engineers) 24:39 - Agentic commerce, Meta's Libra history, and business agents on WhatsApp 27:21 - Stablecoins as the future of digital payments 35:42 - Agentic economy's impact on marketing and the funnel 38:56 - The metaverse, AR glasses, and Meta Ray-Bans/Oakleys 44:25 - Closing remarks - This episode is brought to you by RealFi, a smarter stablecoin, backed by real-world assets. Find out more at⁠ realfi.co⁠. - Ledn provides a secure and transparent way to access liquidity while maintaining your bitcoin holdings. Perfect 8 year track record of keeping clients assets safe. Don't sell your bitcoin. Get a bitcoin-backed loan. Check out your rate by using their loan calculator at⁠ ⁠⁠ledn.io⁠ - JPEG Trading is a global proprietary trading firm specializing in cryptocurrency and decentralized finance markets. From market structure and liquidity provision to quantitative trading strategies, JPEG Trading operates across the full spectrum of blockchain-based assets. Follow @jpegtrading on X to stay ahead of the latest developments in digital asset markets:⁠ https://x.com/jpegtrading⁠ - "Spotlight" features host Sam Ewen.

For most data leaders, governance feels like the thing standing between them and progress. Bharathi Rajan, Vice President of Digital, AI, and Data at Swire Coca-Cola, joins Cindi Howson to share how she built the data foundation powering a $3 billion supply chain operation. She breaks down how to turn data quality into a business accelerator, get ahead of demand signals, and build the foundation every AI initiative actually depends on. Key Moments: Building a Data Foundation Across a $3B Supply Chain (06:39): The starting point wasn't strategy. It was figuring out where the data was, who had it, and how to get it into the hands of actual decision-makers. Reframing Data Governance (10:22): Governance is the foundation that makes better decisions possible. Bharathi shares how education and relationship-building drove the mindset shift at Swire Coca-Cola. AI as an Enablement Factor (11:50): Bharathi reframes AI as a tool for operational efficiency and more impactful work, not a headcount threat. Building the Plant of the Future (14:41): A new $475 million Colorado facility gives Swire Coca-Cola a rare chance to design data and AI infrastructure from scratch. The Skills That Will Matter Most in an AI World (17:58): Bharathi breaks down what she tells young people and aspiring data leaders about building a career that AI can't replace. Key Quotes: “Governance is not red tape. It is important. If you want to make decisions with the right data, you need to have governance. You have to have that quality data flowing in.” - Bharathi Rajan “If you're passionate about something and then you know how to use technology to enable that, that makes the difference.” - Bharathi Rajan “AI, it's an enablement factor for us internally as to how can I help the enterprise really grow, create operational efficiencies, but also have people do more impactful work.” - Bharathi Rajan Mentions Swire Coca-Cola to Build $475 Million Bottling Plant in Colorado Springs, CO DataIQ100:  The most influential people in data and AI 2025 AI Women Power List Honorees The Let Them Theory by Mel Robbins Guest Bio Bharathi Rajan is a results-driven and experienced Chief Data Officer and AI strategist.  In her current role as Vice President Digital, AI & Data at Swire Coca-Cola, USA, Bharathi has saved multi-millions by adopting new tech stack and bringing in capabilities critical for Enterprise growth and performance. While consistently leading innovation and enabling AI & data literacy, Bharathi consecutively drives data and systems architecture confluence across the enterprise. Prior to joining Swire Coca-Cola, USA, Bharathi operated as a Senior Director of Operations in Data and Infrastructre for three years, focusing on Enterprise Reporting and Analysis (ERA), where she was specifically hired for her distinctive capabilities in data strategy, cloud migration, and creative efficiencies.  Bharathi is currently ranked #3 DataIQ100 North America for 2026. She has attended several panel discussions, in Emory University, Women in Tech, AI & Manufacturing and DataIQ. In 2026, Bharathi delivered a keynote on Data Leader as the Transformation Architect at the DataIQ summit. Additionally, Bharathi has spoken at various summits like Microsoft Ignite, Databricks, AI&Data summit and Snowflake summit over the years. Bharathi is also a mentor with WLDA Ventures and Women Tech Council. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Artificial Intelligence is everywhere—but what does it actually mean for community financial institutions? In this episode, Keith Fulton, Chief Data Officer at Jack Henry, joins BankTalk to explore how core providers are leveraging AI to help community financial institutions improve operations, enhance customer experiences, and make smarter decisions. Keith breaks down the differences between machine learning, generative AI, and AI agents in a way that every community bank CEO and executive can understand. Tune in for practical use cases, real-world applications, and a look at what's coming next as AI becomes a bigger part of the banking landscape.Send us Fan MailPresented by Remedy ConsultingFor more information on BankTalk:BankTalk  WebsiteSubscribe to BankTalk NewsRemedy Consulting WebsiteRemedy LinkedInTo speak on the BankTalk Podcast, please  email us. 

In this episode of UC Today, Kristian McCann sits down with Vishnu Shankar, Chief Data Officer at Draup, to explore why hiring, workforce planning, and skills strategy have moved beyond HR and into the CFO's office. As AI agents, automation, and evolving regulations transform the workplace, organizations can no longer think about recruitment as simply filling vacancies. Every hiring decision now carries implications for productivity, operational costs, compliance, and long-term business performance. Yet many businesses are still relying on outdated workforce planning models that fail to account for the growing role of AI alongside human talent. Vishnu explains why companies need to rethink how work itself is structured, moving away from traditional hiring frameworks towards AI-augmented operating models that combine people, software, automation, AI agents, and partners.  Key discussion points include: Why AI has transformed hiring decisions into strategic financial decisions for CFOs and boards.  How organizations should move beyond the traditional "build, buy, borrow" workforce model to embrace AI agents, automation, software, and human talent together.  Why measuring ROI from talent now means focusing on productivity, augmentation, and business outcomes—not simply headcount or cost reduction.  Practical steps organizations can take to build AI-ready workforce strategies while managing governance, compliance, and future skills.  Next Steps Subscribe to UC Today for more conversations on AI, HR, and enterprise technology.

¿Tu empresa hace IA o solo lo aparenta? Tito Neira retoma la conversación con Iván Herrero Bartolomé, Chief Data Officer de Grupo Intercorp (+30 empresas) y cofundador de CDO LATAM, dos años después de su primera visita. Hablan de la trampa de los pilotos eternos, el "teatro de la innovación", por qué la prueba de concepto siempre funciona (y producción es otra historia), la receta de construir carreteras — arquitectura, observabilidad, casos de bajo riesgo con resultados reales (+15-20% ventas, +10-15 NPS) — y las 3 decisiones que un CEO debería tomar en los próximos 90 días.00:00 Intro y regreso de Iván01:59 De Bilbao a Intercorp04:38 ¿Burbuja de pilotos? No: una trampa07:26 La tecnología está madura, nosotros no09:04 Licencias no son ventaja competitiva11:49 El teatro de la innovación15:24 POCs que no llegan a producción17:48 El gap del 85% al 98%18:46 El "AI Engineer" de dos meses21:04 Construye carreteras primero22:33 Arquitectura de IA y observabilidad25:55 Casos de bajo riesgo y resultados29:33 Del caso puntual al proceso completo32:14 Las 3 decisiones del CEO en 90 días38:42 Cierre✩ CURSOS DISPONIBLES

In today's MadTech Daily, we cover Meta naming a new CMO and its first-ever Chief Data Officer, Australia suing Amazon over Prime Video ads, and OpenAI expanding hiring to develop new AI advertising formats. 

Questions? Feedback? Send us a text!Most of the conversation around AI in higher education is focused on the classroom.But that's not where the real impact is happening.In this episode of Transformed, Higher Digital Co-founder and CEO Wayne Bovier sits down with Damian Clarke, VP of Technology at Alabama State University, to unpack where AI is actually creating value—and why so many institutions are looking in the wrong place.With experience spanning CIO and Chief Data Officer roles, Damian brings a practical, inside-the-system view of how universities operate—and where things quietly break down.This conversation centers on a simple shift: AI isn't just a tool for teaching. It's an operational force that exposes how institutions really function.Damian walks through:Why AI is driving the most value in operations—not academicsHow siloed structures limit progress—even with the right tools in placeWhy most prioritization models are reactive—and what that breaks over timeWhat it actually takes to move from experimentation to executionWhy small, targeted use cases outperform large-scale AI rolloutsHow data alignment becomes the foundation for anything to workWhat it means for CIOs to translate complexity into decisions leaders can act onThis episode reframes the conversation: the challenge isn't adopting AI—it's understanding where it fits, and whether the institution is structured to support it.If you're working inside higher education and trying to make sense of where AI actually matters, this conversation will sharpen how you think about it.Subscribe to Transformed for more conversations on how institutions are navigating real change—not just talking about it.#HigherEd #AIinEducation #CIOLeadership #DigitalTransformation #Transformed #HigherDigital #ITLeadershipSubscribe or follow TRANSFORMED wherever you listen, to get the latest episode when it drops and hear directly from leaders and innovators in higher ed tech and digital transformation best practices.Find and follow us on LinkedIn at https://www.linkedin.com/company/higher-digital-inc 

New research shows house hunters are pushing climate risk aside in the name of affordability. Analysis from property data firm Cotality suggests many flood-affected properties have outperformed neighbouring homes in the years since Cyclone Gabrielle. Craig Dargusch, Cotality's Chief Data Officer, spoke to John Campbell.

Most organizations are committing serious capital to AI and can't answer a basic question: how much are we actually spending, and on what? That accountability gap is about to close, whether data leaders are ready or not.

What happens when a technology provider can see patterns across 244 million account holders and thousands of financial institutions? In this episode of Technovation, Peter High speaks with Keith Fulton, Chief Data Officer at Jack Henry, about how data science and AI are transforming community banking. Keith explains how Jack Henry is leveraging industry-scale data to help banks reduce fraud, predict customer churn, identify hidden revenue opportunities, and prepare for an agentic AI future. He also shares how the company is democratizing innovation through citizen development, AI adoption programs, and enterprise-grade governance for employee-built applications. Key Highlights: Building the data foundation required for agentic AI Using consortium-scale data to uncover insights individual banks cannot see Predicting customer churn and identifying hidden commercial customers Applying AI to fraud prevention and customer protection Scaling innovation through AI adoption and citizen development This episode is presented by Celonis — Give AI the context it needs. Learn more at celonis.com/technovation

Can organizations really call themselves AI-ready if their data foundations still have gaps? In this episode of Tech Talks Daily, I sit down with Dave Shuman, Chief Data Officer at Precisely, to discuss the findings from the company's latest State of Data Integrity and AI Readiness Report. Drawing on insights from more than 500 senior IT leaders across the US and Europe, Dave explains why many organizations are confident in their AI readiness while simultaneously identifying infrastructure, data quality, and governance as their biggest obstacles. Our conversation focuses on what Dave describes as the AI data integrity gap, the growing disconnect between ambitious AI initiatives and the quality, consistency, and context of the data powering them. We explore why successful AI projects often perform well in controlled pilot environments before struggling when deployed at scale, and why many organizations continue to underestimate the importance of data lineage, semantic layers, governance, and observability. Dave also shares why he believes data governance and AI governance should be treated as a single discipline rather than separate initiatives. We discuss how businesses can move beyond vanity metrics such as token usage and agent counts to focus on outcomes that genuinely matter, including revenue growth, cost reduction, customer experience, and risk management. As the conversation turns to the future of agentic AI, Dave offers a practical perspective on what autonomous systems will require of organizations and why trust in data will become increasingly important as AI assumes greater responsibility behind the scenes. If your organization is investing heavily in AI and looking for measurable business value, this episode offers a timely reminder that successful AI strategies begin long before the first model is deployed. They begin with data integrity. Based on Precisely's latest research, Dave explains why companies making progress are focusing less on the latest AI tools and more on laying the foundations that enable those tools to deliver reliable outcomes. What role does data integrity play in your organization's AI strategy, and are you confident your data is truly AI-ready?

This week on Trending in Ed, host Mike Palmer is joined by Trending in Ed all-star Beth Rudden, CEO of Bast AI. From her roots digging in the dirt as an archaeologist to managing a $34 billion division as the Chief Data Officer of IBM Managed Services, Beth brings a deeply grounded, technical perspective to the artificial intelligence conversation. In this wide-ranging and insightful conversation, Mike and Beth skip the typical AI hype to explore what it actually takes to build explainable, trustworthy technology. Beth shares how Bast AI acts as an LLM-agnostic explainability layer—using a unique drinking chocolate analogy to demonstrate how they verify AI data rather than letting models hallucinate plausible narratives. They explore the practical application of using small language models (SLMs) for data enrichment, highlighted by Bast AI's meaningful work with Craig Hospital to translate complex neuro-spine outpatient procedures into accessible languages and analogies. KEY INSIGHTS: • Inverting the Chatbot Approach: Why defining what an AI can talk about is far more effective than building restrictive guardrails. • The Myth of "Human in the Loop": How shifting accountability to overworked humans can become a form of liability laundering. • Microservices vs. Agentic Harnesses: Looking at the risks of natural language agentic systems like Claude Code versus discrete, self-healing tasks. • Cognitive Offloading & Math Education: Why future technical skills should prioritize differential equations and the diversity prediction theorem over simple calculation. • Pattern Recognition vs. Choice: Defining true intelligence through the ability to choose wisely, rather than just matching mathematical patterns. They also cross paths with the Cynefin framework, explain how the human brain conserves energy by only holding two paradoxes at once, and unpack the cultural shifts reshaping modern engineering ethics. Stay ahead of the curve in education and technology! Please like and share this episode with your network, and follow the podcast on Apple Podcasts, Spotify, or your favorite player so you never miss an episode like this one. LINKS: Learn more about Bast AI: https://www.bast.ai Subscribe to Beth's Substack: https://bethrudden.substack.com TIMESTAMPS: 00:00 - Introduction and welcoming Beth Rudden back to the show 01:00 - The drinking chocolate analogy for Explainable AI 03:00 - Beth's lightning-round background: Archaeology to Chief Data Officer at IBM 05:00 - Getting "catfished by AI" and verifying facts with databases 07:00 - Mike on Gemini, RAG applications, and checking AI confabulation 09:00 - Enriched data and Small Language Models (SLMs) at Craig Hospital 12:00 - Epistemic security and inverting conversational technology 14:30 - Liability laundering and the illusion of "human in the loop" 15:30 - Agentic harnesses vs. self-healing microservices 20:00 - Understanding as labor and Conrad Wolfram's three-step math process 22:30 - Future human skills: Differential equations and jelly bean statistics 26:30 - Pattern recognition vs. true intelligence as the ability to choose 29:30 - Neurosymbolic systems and subjectivity in data science 34:30 - Shunting energy: The Cynefin framework and holding paradoxes 38:30 - Healthcare AI scribes and doctor burnout 44:30 - Trust architectures and building tech for the Maintenance Era 47:30 - Cultural devastation and the teleological suspension of ethics 49:00 - Final thoughts and wrapping up with Beth Rudden

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

In this episode, host Olivier Lafontaine is joined by four industry leaders from the Life Accelerated Summit: Richard Wiedenbeck, Principal Consultant at Avatar Solutions LLC; John Brabazon, CFO at SBLI; Matthew Busbee, Chief Data Officer at Pan-American Life Insurance; and Sheriff Balogun Jr., Head of Insurance and Wealth Technology at MassMutual. Together, they explore the current and future impact of artificial intelligence on the insurance industry. From navigating legacy systems to overcoming regulatory hurdles, the panelists dive deep into AI's role in business transformation. They discuss how AI can drive operational efficiencies, improve customer experience, and shape workforce dynamics. The conversation also highlights how insurance companies are preparing their teams for AI adoption, the challenges of managing AI agents, and the evolving leadership structures needed in the age of AI.   Key Takeaways: AI is not just a tool but a key player in reshaping insurance workforces and operational models. Talent management and knowledge transfer are critical as AI continues to automate processes. Balancing governance, AI responsibility, and data privacy is central to successful AI adoption. Organizational structures need to evolve, with a focus on AI ownership and oversight. Building an AI-enabled culture requires a shift in mindset and deep collaboration across teams. Jump Into the Conversation:(00:00) Intro and context - what this episode covers (02:15) Meet the panelists - Richard Wiedenbeck, John Brabazon, Matthew Busbee, and Sherriff Balogun Jr. (05:33) MassMutual's top technology priorities - talent, AI productivity, and legacy modernization (08:46) Pan-American Life's AI strategy across 20+ countries and regulatory complexity (12:46) SBLI's approach - small company, big transformation, policy admin overhaul (14:41) Ameritas' AI playbook - unit cost reduction, AI workers, and workforce mindset (20:02) The real challenges of technology transformation - legacy systems, buy-in, and change management (25:11) Knowledge management - capturing expertise before your best people retire (32:04) Who owns AI in the C-suite - CIO, COO, or something new entirely (36:08) AI workers in practice - the Herbie and Susie story from Ameritas (40:01) Rethinking the SDLC - why "deploy and learn" beats "test until perfect" (43:30) What happens when human expertise retires and AI is all that's left (46:02) AlphaGo, gamification, and how humans will learn from AI in the future (48:30) Takeaways - be intentional, build governance muscle, don't wait for perfection Resources: Connect with Richard Wiedenbeck: https://www.linkedin.com/in/richard-wiedenbeck-6a752/ Connect with John Brabazon: https://www.linkedin.com/in/john-brabazon-cpa-3a81901b/ Connect with Matthew Busbee: https://www.linkedin.com/in/mbusbee/ Connect with Sherriff Balogun Jr.: https://www.linkedin.com/in/sherriffbalogunjr/  

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.

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

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

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]---

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.

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/

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.

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