Podcasts about Data governance

Send us Fan MailMost companies chasing AI transformation are doing it in the wrong order. Manuel Barragan spent 20+ years inside organisations like Reuters and HSBC before building his own consultancy - and what he learned is this: technology cannot fix broken people and broken processes. It can only run them faster.What You Will LearnHow to identify whether your company is truly transforming or just adding tools to existing dysfunction Why putting technology before people is the single most expensive mistake in digital transformation What AI governance actually means - and why ignoring it is exposing your company's data to the world How to close the AI literacy gap inside your organisation before it becomes a competitive liability Why the conductor, the musicians, and the instruments all have to be ready before the concert beginsTimestamps01:30 — From Reuters CTO to Regional CEO: What 20 Years Inside the Giants Taught Him 08:15 — Why "We're Transforming" Is the Biggest Lie in Business Right Now 10:18 — The AI Hype Trap: Why It's the Same Mistake Companies Made with SAP 14:16 — Data Governance & AI Literacy: The Hidden Risk Destroying Companies From the Inside 21:06 — This or That: Corporate World vs Entrepreneurship, AI Liberates or Replaces, and MoreAbout the GuestManuel Barragan is a fractional executive and digital transformation strategist with 20+ years of leadership across Reuters, HSBC, Marsh, IBM, and multiple CEO and Managing Director roles across Latin America. His firm, DTS Strategist, works with corporations and SMEs to fix the people and process foundations that determine whether technology investments succeed or fail. He is currently writing a book on navigating digital transformation through the human lens. Connect with Manuel LinkedIn: Manuel Barragan Website: www.dtstrategist.comConnect with HinaHina's WebsiteHina's LinkedInHina's InstagramHina's Youtube Channel Subscribe for new episodes every Wednesday and Friday.Production Credit: Produced by @the32collective_ / https://www.the32collective.co/

In this episode of Future Finance, Paul Barnhurst and Glenn Hopper sit down with Dave Trier, CEO of ModelOp, to discuss how enterprises can govern, manage, and operate AI at scale. Dave shares insights on implementing AI responsibly, tracking ROI, managing risks, and creating an enterprise-wide AI portfolio that drives value while ensuring compliance and governance.Dave Trier leads ModelOp with a focus on customer value, product innovation, and enterprise execution. With over 20 years in data science, AI, analytics, cloud, and enterprise software, he brings technical expertise and a pragmatic leadership style, helping CIOs, CTOs, and AI leaders deploy AI effectively across organizations .In this episode, you will discover:How enterprises can scale AI responsibly and reliablyThe CFO's role in AI oversight and portfolio managementMeasuring AI value through ROI, usage, and internal feedbackDistinctions between AI governance and traditional data governanceImportance of change management and structured AI adoptionDave provides a framework for enterprise AI adoption, emphasizing disciplined management, measurable impact, and alignment with regulatory and operational requirements. This episode is essential for finance and tech leaders looking to integrate AI at scale while ensuring oversight, efficiency, and business value . Follow Dave:Website: https://www.modelop.com/LinkedIn: https://www.linkedin.com/in/davidetrier/Follow Glenn:LinkedIn: https://www.linkedin.com/in/gbhopperiiiFollow Paul:LinkedIn: https://www.linkedin.com/in/thefpandaguyFollow QFlow.AI:Website - https://bit.ly/4i1EkjgFuture Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai. Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.In Today's Episode:[00:00] – Trailer[02:38] – AI Compliance & Governance Challenges[04:35] – Distinction Between AI & Data Governance[07:28] – Measuring AI Value & ROI[12:41] – Treating AI as a Portfolio of Investments[15:05] – Change Management & Enterprise Adoption[17:39] – Wild West of AI & Need for Rigorous Processes[18:54] – CFO Oversight in AI Implementation[21:00] – Closing Remarks

Join us this week for The Tech Leaders' Podcast, where Gareth sits down with Nicola Mendelsohn, Head of Global Business Group at Meta, at Meta Conversations 2026 in the historic Methodist Central Hall in the heart of Westminster. Nicola talks about the new WhatsApp for Business, the technical challenges around it, the importance of data safeguarding and the role of Meta's Chief Privacy Officer. On this episode Nicola and Gareth discuss the challenges around Enterprise AI adoption and governance, and her advice to UK businesses. Timestamps: Introduction (1:58) Meta and Enterprise Messaging (4:30) Technical Challenges (14:51) Data Safeguarding and the Chief Privacy Officer (17:52) Enterprise AI Adoption and Governance (18:50) Advice for UK Businesses (26:55) https://www.bedigitaluk.com/

In this episode of Connected FM, host Dean Stanberry sits down with Melissa Kaan, Founder & CEO of NOVA IFM, to explore how facility teams can use CMMS platforms to drive smarter operational and capital decisions. They discuss the importance of quality data, why CMMS systems should function as decision engines rather than digital filing cabinets and how proactive maintenance strategies can improve response times, compliance and long-term asset performance. Melissa also shares practical insights on asset management, technician engagement, data governance and translating operational trends into meaningful capital planning conversations. The conversation highlights how facility leaders can improve CMMS adoption, strengthen reporting practices and use data more effectively to support both daily operations and long-term portfolio planning. This episode is sponsored by SiteMap®, powered by GPRS. Learn more at sitemap.com/ifma Timestamps: 00:00 Introduction 02:29 Minimum Viable CMMS Data 04:52 Must Have Data Fields 06:16 From Records to Decisions 09:16 First 90 Days Wins 11:00 Data to Capital Plans 13:27 Leadership Review Rhythm 14:59 One Step This Week 16:53 Data Quality Wrap Up   Connect with Us:LinkedIn: https://www.linkedin.com/company/ifmaFacebook: https://www.facebook.com/InternationalFacilityManagementAssociation/Twitter: https://twitter.com/IFMAInstagram: https://www.instagram.com/ifma_hq/YouTube: https://youtube.com/ifmaglobalVisit us at https://ifma.org

Developing a cancer drug is one of the most expensive, slow, and failure-prone processes in modern science. PharosAI is trying to change that – by building multimodal, AI-ready datasets from donated cancer tissue samples and making them available to researchers, biotech companies, and clinicians.In this episode, Technical Director Adrian La Porta speaks with PharosAI's Dr Lucie Burgess (COO) and Dr Emma Colliver (Research Fellow) about what it takes to curate cancer data at scale, why federated learning matters for patient privacy, where AI is already transforming diagnostics, and what synthetic patients could mean for clinical trials.Whether you work in life sciences, pharma manufacturing, digital health, or clinical data governance, understanding how AI is reshaping the drug discovery pipeline has direct implications for how and when new facilities will need to be built. This one is worth your time.Topics covered:00:00 Introduction00:01 What is PharosAI?00:06 Why AI can fundamentally change cancer care00:08 The venture behind the mission00:12 Lowering the barrier for UK biotech00:16 Are we at an inflection point?00:20 AI in diagnostics – what's already working00:24 Misconceptions about AI in biotech00:30 Data – acquiring, cleaning, and structuring00:34 Patient privacy, consent, and NHS data00:37 What cancer care could look like in 10 years00:40 Why this work matters personallySend us Fan MailTo learn more about Bryden Wood's Design to Value philosophy, visit www.brydenwood.com. You can also follow Bryden Wood on LinkedIn.

Health data affects artificial intelligence in important ways. Camille Nebeker, Ed.D., M.S., UC San Diego, explains why ethically sourced data is foundational to building trustworthy, AI-ready health data repositories. Nebeker examines how ethical sourcing applies across the full data lifecycle, including consent, governance, transparency, data quality, privacy, stewardship, and community engagement. She also shows how ideas from supply chain management and value sensitive design help teams identify ethical tensions and improve decision-making. This work helps explain why ethics cannot be added at the end of AI development and points toward more accountable data practices that support public trust and stronger downstream performance. Series: "Exploring Ethics" [Science] [Show ID: 41368]

Health data affects artificial intelligence in important ways. Camille Nebeker, Ed.D., M.S., UC San Diego, explains why ethically sourced data is foundational to building trustworthy, AI-ready health data repositories. Nebeker examines how ethical sourcing applies across the full data lifecycle, including consent, governance, transparency, data quality, privacy, stewardship, and community engagement. She also shows how ideas from supply chain management and value sensitive design help teams identify ethical tensions and improve decision-making. This work helps explain why ethics cannot be added at the end of AI development and points toward more accountable data practices that support public trust and stronger downstream performance. Series: "Exploring Ethics" [Science] [Show ID: 41368]

Health data affects artificial intelligence in important ways. Camille Nebeker, Ed.D., M.S., UC San Diego, explains why ethically sourced data is foundational to building trustworthy, AI-ready health data repositories. Nebeker examines how ethical sourcing applies across the full data lifecycle, including consent, governance, transparency, data quality, privacy, stewardship, and community engagement. She also shows how ideas from supply chain management and value sensitive design help teams identify ethical tensions and improve decision-making. This work helps explain why ethics cannot be added at the end of AI development and points toward more accountable data practices that support public trust and stronger downstream performance. Series: "Exploring Ethics" [Science] [Show ID: 41368]

Health data affects artificial intelligence in important ways. Camille Nebeker, Ed.D., M.S., UC San Diego, explains why ethically sourced data is foundational to building trustworthy, AI-ready health data repositories. Nebeker examines how ethical sourcing applies across the full data lifecycle, including consent, governance, transparency, data quality, privacy, stewardship, and community engagement. She also shows how ideas from supply chain management and value sensitive design help teams identify ethical tensions and improve decision-making. This work helps explain why ethics cannot be added at the end of AI development and points toward more accountable data practices that support public trust and stronger downstream performance. Series: "Exploring Ethics" [Science] [Show ID: 41368]

How do you intelligently surface access to your database? While at NDC Toronto, Richard spoke with Jerry Nixon about Data API Builder, Microsoft's tool that enables data professionals using Microsoft databases, including SQL Server, Postgres, CosmosDB, and MySQL, to provide an API layer with security, schema extraction, and governance policies. You can expose the API as a REST interface, a GraphQL interface, and an MCP server! This is a powerful tool for providing controlled access to data while still allowing for ad-hoc access. The potential is huge - you need to check it out! Links Data API Builder GraphQL Recorded May 7, 2026

In this episode of Future Finance, Paul Barnhurst and Glenn Hopper sit down with Dave Trier, CEO of ModelOp, to explore the challenges and opportunities of implementing AI at scale in enterprises. Dave shares how organizations can manage AI responsibly, measure ROI, and move from scattered pilots to a disciplined, industrialized approach. He also discusses the critical role of CFOs in AI oversight, change management, and creating measurable business value from AI initiatives Dave Trier is CEO of ModelOp, leading the company with a focus on customer value, product innovation, and enterprise execution. With over 20 years of experience across AI, data science, analytics, cloud, and enterprise software, Dave is a patent-holder and trusted partner to CIOs, CTOs, and AI leaders. Prior to becoming CEO, he shaped ModelOp's product strategy and held senior roles at Think Big Analytics, Powered by Action, and Accenture Technology Labs. He holds a BS in Electrical Engineering from the University of Notre Dame. In this episode, you will discover:How to industrialize AI delivery across an enterpriseManaging risk, governance, and compliance for AI implementationsMeasuring AI ROI using financial, feedback, and usage metricsThe CFO's role in AI oversight and rationalizing AI investmentsKey lessons for change management and process discipline in AI adoptionDave Trier highlights how enterprises can move from scattered AI pilots to a disciplined, industrialized approach that delivers measurable business value. He emphasizes the importance of governance, change management, and cross-functional collaboration to ensure AI initiatives succeed. CFOs play a key role in oversight, setting financial parameters, and rationalizing AI investments. Follow Dave:Website: https://www.modelop.com/LinkedIn: https://www.linkedin.com/in/davidetrier/Follow Glenn:LinkedIn: https://www.linkedin.com/in/gbhopperiiiFollow Paul:LinkedIn: https://www.linkedin.com/in/thefpandaguyFollow QFlow.AI:Website - https://bit.ly/4i1EkjgFuture Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai. Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.In Today's Episode:[00:00] – Trailer[02:07] – Meet Dave Trier, CEO of ModelOp[04:57] – ModelOp & AI Governance Explained[06:21] – AI vs Data Governance[08:11] – Evaluating AI ROI for CFOs[13:24] – AI as a Managed Investment Portfolio[16:43] – Change Management & Process Discipline[20:48] – CFO's Role in AI Oversight[27:38] – Tips to Maximize AI ROI[30:16] – Enterprise AI Complexity & Coordination[32:13] – Dave's Journey: Electrical Engineer to AI CEO[35:12] – Closing Thoughts

Modern work can be frustrating and chaotic—if you don't have the right tools. From context engineering to multimodal search, go behind the scenes and hear how Dropbox engineers are building AI that actually understands you, so you can focus on the work that matters most. If you're new to Working Smarter, we've travelled from the F1 track to the bottom of a lake, and heard real stories from chefs, doctors, lawyers, and founders about how AI is helping them do more of what they love about their jobs. But in our third season, we're talking to the people behind the tools—the engineers and product leaders building helpful, time-saving AI features into the Dropbox experience you already know and trust. You'll hear all about their work on agents, inference, security, and, of course, how the people building AI use AI themselves. ~ ~ ~  Working Smarter is brought to you by Dropbox. Find, organize, and share your work—all in one place—with context-aware AI from Dropbox. You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.ai This show would not be possible without the talented team at Cosmic Standard: producer Ben Montoya, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrator Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck.  Our theme song was composed by Doug Stuart.  Working Smarter is hosted by Matthew Braga. Thanks for listening!

Full show notes and transcript  - https://bit.ly/introducing-seam-podcastWatch on YouTube - https://youtu.be/rvRd4nep3QU-----Episode Summary:Dara and Matthew return after a break with a bumper news roundup covering frontier model updates, Google I/O and Next, Anthropic's Mythos and Project Glasswing, and the latest AI design tools. The main event is the official reveal of SEAM (Semantic Engine for Agent Mediation), Measurelab's new product that sits between LLMs and downstream data sources to bring governance, intelligence modelling, and consistent answers to AI-powered workflows.-----About The Measure Pod:The Measure Pod is your go-to fortnightly podcast hosted by seasoned analytics pros. Join Dara Fitzgerald (Co-Founder at Measurelab) & Matthew Hooson (Head of Engineering at Measurelab) as they dive into the world of data, analytics and measurement, with a side of fun.-----If you liked this episode, don't forget to subscribe to The Measure Pod on your favourite podcast platform and leave us a review. Let's make sense of the analytics industry together!

Get featured on the show by leaving us a Voice Mail: https://bit.ly/MIPVM  This episode explores why data governance must come before enabling Microsoft 365 Copilot, with insights from Khurram Hafeez. It breaks down how sensitivity labels, data loss prevention, and Microsoft Purview reduce the risk of unintended data exposure. You will hear practical guidance on preparing your environment, protecting sensitive information, and managing AI use across Microsoft tools and third‑party AI sites. The focus is on real‑world decisions organisations must make to safely adopt Copilot at scale.

This is the takeaway episode with Jason Doerr who has spent years watching governance programs undermine themselves. He walks through what pragmatic governance actually looks like and digs into PADU (Preferred, Acceptable, Discouraged, Unacceptable) as a practical roadmap framework. See omnystudio.com/listener for privacy information.

Jason Doerr has spent years watching governance programs undermine themselves by cataloging everything without a use case, naming data stewards who have nothing to actually do, and building central teams that become blockers instead of enablers. In this episode, he walks through what pragmatic governance actually looks like: start with use cases, give stewards real work to action on, and let the central team set principles rather than police behavior. He also digs into PADU (Preferred, Acceptable, Discouraged, Unacceptable) as a practical roadmap framework, how LLMs can accelerate semantic layer creation without generating vanity metrics, and why the governance operating model is shifting toward agentic management ... whether the governance community is ready for it or not.See omnystudio.com/listener for privacy information.

Link: http://www.five1.de/podcast/leitfaden-ai-governance Alle wollen KI nutzen. Aber kaum jemand hat sauber geklärt, wer eigentlich Verantwortung trägt, welche Daten genutzt werden dürfen und wann eine KI-Entscheidung vertrauenswürdig ist. Genau hier beginnt AI Governance. In dieser Folge von AI or DIE spricht Andreas Wiener mit Christian Bühler darüber, warum KI nicht an fehlender Technologie scheitert, sondern an fehlenden Leitplanken. Denn schlechte Daten, unklare Rollen und Schatten-KI werden durch künstliche Intelligenz nicht gelöst — sie werden skaliert. Christian erklärt, warum Unternehmen zuerst verstehen müssen, was bereits im Einsatz ist, bevor sie neue Regeln aufstellen. Denn viele Mitarbeitende nutzen längst KI-Tools, oft ohne klare Freigaben, ohne Governance und ohne Bewusstsein für Risiken. Dabei geht es nicht um Bürokratie. Es geht um Enablement. AI Governance soll Unternehmen nicht ausbremsen, sondern handlungsfähig machen. Sie schafft Rollen, Verantwortlichkeiten, Entscheidungsregeln und Transparenz. Erst dadurch wird KI aus einem Experiment zu einem skalierbaren Bestandteil des Geschäfts. Die zentrale Botschaft: Wer KI produktiv nutzen will, braucht Vertrauen. Und Vertrauen entsteht nicht durch Hype, sondern durch Governance. ⸻ Timestamps 00:00 – Intro: Warum AI Governance jetzt Pflicht wird 00:41 – Warum KI Datenprobleme sichtbar macht 01:24 – Feedback zur letzten Folge über Datenfundamente 02:15 – Unterschied zwischen Data Governance und AI Governance 03:56 – Kann man KI-Entscheidungen vertrauen? 05:16 – Warum Bestandsaufnahme der erste Schritt ist 06:20 – Schatten-KI und unbekannte Tools im Unternehmen 07:10 – Warum AI Governance mit einem Use Case starten sollte 08:28 – Bestandsaufnahme: Fehler finden und Blueprints erkennen 09:44 – Governance als Enablement statt Kontrolle 10:46 – Warum Regeln bessere Ergebnisse ermöglichen 12:05 – Rollenmodelle: Wer trägt Verantwortung? 13:55 – AI Owner, Risk Officer und klare Zuständigkeiten 14:39 – Warum Berater Governance anschieben, aber nicht leben sollten 15:55 – Betriebsrat, IT, Compliance und Management einbinden 17:20 – Warum AI Governance kontinuierlich weiterentwickelt werden muss 18:55 – Standards, Zertifizierungen und neue Anforderungen 20:00 – Fehlerkultur als Bestandteil von AI Governance 21:59 – Typische Fehler: sensible Daten, fehlende Transparenz, schlechte KPIs 23:19 – Warum AI Governance dynamischer ist als Data Governance 24:30 – Monitoring von AI-Systemen als neue Pflicht 25:13 – AI Governance als echter Wettbewerbsvorteil 26:37 – Die wichtigsten Prinzipien in einer Minute 28:18 – Vom Macher zum Dirigenten: Die neue Rolle des Menschen 30:29 – Guide, Austausch und Abschluss

Microsoft Purview sollte nicht pauschal als „sicher oder unsicher“ bewertet werden, da die Frage zu kurz greift. In der Cloud-Diskussion werden oft politische, rechtliche und technische Ebenen vermischt, was zu Verunsicherung führt. In dieser Interview Folge spricht Markus mit Sophie Gräfin von Brühl über diese spannenden Themen.

Loacker ist ein Familienunternehmen mit fast 100-jähriger Geschichte, das seine Waffel- und Schokoladenprodukte heute in über 100 Ländern verkauft. Mit diesem Wachstum sind auch die Anforderungen an eine strukturierte Datenstrategie gestiegen – und damit die Notwendigkeit, Data Governance ernsthaft anzugehen. In dieser Episode spricht Peter Bluhm mit Lisa Burger, Corporate Data Governance & Quality Management Manager bei Loacker, über den Weg des Unternehmens hin zu einer tragfähigen Datenstrategie. Themen der Episode: Warum internationales Wachstum Datensilo-Probleme und mangelndes Daten-Ownership mit sich bringt  Was Data Governance von Datenqualitäts-Management unterscheidet – und warum dieser Unterschied praktisch relevant ist Wie Loacker den Einstieg in das Thema strukturiert hat: von der Stakeholder-Analyse bis zum vierstufigen Vorgehensmodell Welche Rolle Sponsorship, interne Champions und strategische Kommunikation für den Erfolg solcher Initiativen spielen Warum Data Governance vor allem ein Change-Management-Thema ist – und was es braucht, damit daraus eine gemeinsame Haltung im Unternehmen wird  Über den Gast: Lisa Burger verantwortet bei Loacker den Bereich Corporate Data Governance & Quality Management und treibt dort den Aufbau einer unternehmensweiten Datenstrategie voran. 

Episode co-host: Marina Kaganovich, Enterprise Trust Lead, Office of the CISO, Google Cloud  Guest: James Sherer, Partner at BakerHostetler  Topics Is AI just an emerging technology or something bigger, deeper and different?  Is this another emerging technology or a fundamental shift? How to effectively govern something that is rapidly changing at unprecedented velocity?  We navigated the governance of the Internet and SaaS. What makes AI governance fundamentally different from the "Classic IT" or Data Governance models of the past? As we move toward Agentic AI, the line between tool and teammate blurs. Should we be governing AI agents through the lens of Technical Controls or Human Resources and behavioral contracts?  What if we hand even more responsibility to AI? Where are the tipping points as we shift from assistance to autonomy? How to avoid unintended, negative consequences when setting policy, contrasting risk-based vs. rights-based regulation and regulatory expectations Give us some practical takeaways for a defensible AI program - if an organization had to defend its AI program to a regulator or a judge tomorrow?  Related episodes: Video version EP235 The Autonomous Frontier: Governing AI Agents from Code to Courtroom EP161 Cloud Compliance: A Lawyer - Turned Technologist! - Perspective on Navigating the Cloud EP237 Making Security Personal at the Speed and Scale of TikTok

Link: https://www.hrcie.com/insurenxt-2026-in-koeln/ Eventkalender: https://www.aiordie-x.de/eventkalender/ Kontakt: https://www.hrcie.com/ Versicherungen waren schon datengetrieben, bevor AI zum Buzzword wurde. Doch jetzt steigt der Druck massiv. In dieser Folge von AI or DIE spricht Andreas Wiener mit Frank Hendricks über die Realität moderner Versicherungen: Solvency II, BaFin-Regulierung, Dunkelverarbeitung, Kostenumlage, Data Governance und die Frage, warum Versicherer heute Transparenz liefern müssen, die sie vor zehn Jahren noch gar nicht messen konnten. Dabei wird schnell klar: Versicherung ist längst kein klassisches Policengeschäft mehr. Es ist ein hochreguliertes Daten- und Steuerungsbusiness. Frank erklärt, warum Prozesse heute komplett automatisiert laufen müssen, wie Tarife bereits auf „Dunkelverarbeitbarkeit“ optimiert werden und weshalb Kostenverteilung in Versicherungen ein eigenes Universum ist. Außerdem geht es um die Rolle von IBM Planning Analytics / TM1 bei hochkomplexen Umlage- und Steuerungsmodellen, um regulatorische Nachweispflichten, Audit Trails, Governance und die Realität hinter modernen Versicherungs-IT-Systemen. Eine Folge für alle, die verstehen wollen, warum Versicherungen beim Thema Daten oft weiter sind als viele andere Branchen — und warum AI ohne saubere Steuerungslogik dort wertlos bleibt. ⸻ Timestamps 00:00 – Intro und Rückblick auf die erste Folge 01:04 – Warum Versicherungen extrem datengetrieben arbeiten 01:54 – Solvency II und die Folgen der Finanzkrise 03:41 – Transparenz als regulatorischer Zwang 05:06 – Dunkelverarbeitung und vollautomatische Schadenprozesse 07:15 – Warum Tarife heute auf Automatisierung optimiert werden 08:28 – Die HUK Coburg als datengetriebener Versicherer 10:32 – IBM Planning Analytics / TM1 bei der HUK 12:20 – Kostenumlage als zentrale Herausforderung 13:33 – Warum Versicherungen eigene Rechnungslogiken haben 14:49 – Das Problem klassischer ERP-Systeme 16:05 – TM1 als Nebenbuch und prüfungsrelevantes System 18:23 – Verteilungsschlüssel und regulatorische Nachweispflichten 20:25 – Echtzeit-Umlagen und In-Memory-Technologie 21:20 – Governance, Audit Trails und Compliance 24:53 – Monitoring, Deployment und Data Governance 27:53 – Warum TM1 seit Jahrzehnten erfolgreich eingesetzt wird 29:21 – Predictive Analytics und Simulationen im Controlling 31:57 – Fazit: Versicherungen als hochkomplexe Datenorganisationen 33:15 – Austausch, Community und Insurance Next in Köln

Nat D’Ercole, data transformation leader for AI and data at Deloitte Canada In the final episode of In The Channel’s three-part series from SAS Innovate 2026 in Grapevine, Texas, we sit down with Nat D’Ercole of Deloitte Canada for the practitioner perspective on enterprise AI transformation – what it looks like from inside the organizations actually doing the migration and governance work. The conversation opens on the reality of Viya migrations at enterprise scale. Deloitte’s approach starts with a scan of the client’s current environment – understanding which workloads are actually running the business versus which haven’t been touched in years – before building a roadmap that addresses cost structure, change management, and what a future-state architecture actually needs to look like. A central theme is data governance maturity as the key determinant of AI readiness. Nat introduces the concept of human hallucination – multiple versions of the truth produced when ungoverned data is accessed and wrangled without standards across an organization. His point is that the organizations that have already done the hard work of data governance are the ones genuinely positioned to move fast on AI. Those that haven’t are still stuck solving the foundation problem first. On OSFI E-21, Nat echoes what SAS Canada’s Ryan MacDonald described earlier in the series – regulation as a useful catalyst rather than a burden – and addresses the risk and fraud use cases where the Deloitte-SAS partnership is seeing the most active investment, including procurement integrity and financial scenario modeling. The episode closes on SAS AI Navigator as a complement to Deloitte’s own trusted AI framework, the use of AI-augmented engineering to accelerate migration timelines, and a thirty-year observation about the 80/20 problem – and why this might finally be the moment it gets flipped. Read Full Transcript Robert Dutt: Hello, and welcome to In The Channel from ChannelBuzz.ca, bringing news and information to the Canadian IT channel community for the last 16 years. I’m Robert Dutt, editor of ChannelBuzz.ca, and your host for the show. This is our third and final episode from last week’s SAS Innovate 2026 in Grapevine, Texas. And if you’ve been following along, you’ve heard the view from SAS Canada leadership – the AI maturity story, the governance urgency, what the mid-market channel opportunity looks like – and then the global channel strategy conversation with John Carey, the build-out of the indirect motion, the TD SYNNEX partnership, and where the channel goes from here. What we haven’t heard yet is what it actually looks like from inside a real enterprise engagement. That’s what this episode is. My guest is Nat D’Ercole, data transformation leader for AI and data at Deloitte Canada. Deloitte is one of SAS’s major global systems integrator partners, and Nat works with the kind of large Canadian enterprises that are right in the middle of the AI transformation conversation – Viya migrations, data governance strategy, OSFI E-21 readiness, risk and fraud modernization. The practitioner reality, not the roadmap. We talk about what it actually looks like to walk into a client and untangle 20 or 30 years of SAS implementation. We get into data governance maturity as the thing that most determines whether an organization is ready for AI. We talk about what Nat calls human hallucination, and why it’s not as different from the AI kind as you might think. And we close on a concept that Nat has been waiting 30 years to see become real – the 80/20 flip. Let’s get right into it. My chat with Nat D’Ercole. Nat, thanks for taking the time. I appreciate it. Nat D’Ercole: Pleasure to be here. Robert Dutt: Obviously, you guys are one of SAS’s major global partners, but for an audience that’s primarily VARs and MSPs – that kind of partner – the Deloitte AI and data practice might be a bit of a black box. Can you tell us a bit about what it looks like day to day? Who are your clients? What are they typically asking you to solve today? Nat D’Ercole: Of course. Our clients are facing complex issues in terms of how to manage their data, manage their models, and obviously working in an age of AI and sorting all that out in terms of where they are today, what are they using today, the cost of running all that today, to where they need to get to – both from a data, tech, people, and process perspective. So being a professional services firm focused on helping our clients with both advisory, implementation, and supporting our clients’ systems are key areas that our clients look to us for support. Robert Dutt: A little earlier, I talked with Ryan Macdonald, who leads SAS Canada. The subject of hidden SAS came up – in so much as a lot of customers end up finding they’re running SAS software, running key business functions on SAS software, and not necessarily even aware of it, because it’s just become such a part of the underpinnings. It’s just there. It’s invisible even to themselves. When you walk into a client that engages Deloitte on, say, a Viya migration, is that something that you often see? And what does that journey kind of look like? Nat D’Ercole: Great question, Robert. And that comment from Ryan really makes sense to me. Our clients have been using SAS for many, many years – some 20, 30 years, and maybe even longer. And so SAS is used for everything from data management, modeling, analytics, reporting, data wrangling, and so on and so forth. And it’s a web of solutions that organizations across departments have implemented. And so understanding what they currently have in place is a challenge. And so we do help them with that in terms of providing them with a scan of their current environment and helping them understand what workloads are actually running their business versus workloads that haven’t been touched in years. And with that, we’re able to help them with a roadmap to address those workloads and determine what is fit for purpose in terms of moving to a future state. Robert Dutt: You guys are dealing with big projects and pretty high-stakes stuff, and not the simplest thing – like a Viya migration at enterprise scale is clearly not a simple concept. What do you see as the real cost and complexity pressure points for customers? And how do you help clients navigate those without the project stalling out? Nat D’Ercole: You know, I think what’s really important is to understand – just building on my previous answer – understanding what is running their business and the cost structure associated to that. So obviously there’s technology licensing, there’s training on existing solutions, target solutions, change management, upskilling, etc. in terms of some of the key cost drivers. And let’s also refer to storage as well as another area of cost. So analyzing our clients’ environments and really taking a closer look at each of those buckets to help them figure out where are they now, and what are the opportunities, what are the options for them moving forward. Robert Dutt: Governance – obviously a big topic here – and the idea of governance and trust becoming inseparable from the AI conversation has been a big theme here and elsewhere. Curiously, what are you seeing in that, and is it changing what you’re being hired to do? Are clients coming to you with a technology problem, or are they coming to you with a governance and risk problem that has a technology component to it? Nat D’Ercole: Yeah, so clients are hiring us to solve a business problem that is enabled by technology, enabled by change. And to address your specific question around governance – governance comes in the form of data governance, AI governance, model governance, etc. We do find that the level of preparedness in organizations around data absolutely varies from immature to mature. So those organizations that have addressed data governance are those that are most prepared for the AI age and being able to take the next step. Now, not everything requires structured data and highly clean data. So depending on the use cases, it is quite possible to apply AI and begin to see benefit. However, more and more I do see organizations invest in things like master data management, invest in data governance, and invest in operating models. And those operating models are also AI-ready. So we’re starting to see the need for roles such as prompt engineers, AI engineers that are interrogating results of models, ensuring that there’s a continuous feedback loop – and where models are drifting or hallucinating or so on and so forth, that there’s a human loop catching that. So these are new roles that are being created and need to be part of an overall governance strategy. Robert Dutt: What role do you see yourselves playing in leveling up those organizations who haven’t gone far enough in governance thus far to get the most out of the AI future? Nat D’Ercole: I’m actually working with a client right now where they haven’t addressed data governance and they’re stuck with legacy solutions where very much it’s been the wild wild west – if I could use that term – in terms of accessing data, enabling analysts across the organization to wrangle that data and develop outputs that their leaders consume. And so when that happens without governance, you get things like what I refer to sometimes as human hallucination, where there’s multiple versions of the truth. Organizations do see that today. And to me, that’s the human side of these hallucinations that we’re seeing with AI. So for those organizations, in terms of leveling up, it is certainly approaching it from a people perspective first – ensuring leadership is in place, necessary roles around domain ownership, necessary standards and policies are in place. And really, what is the motivation for elevating data governance in the organization, ensuring that that messaging is clear from the executive level down. Robert Dutt: So if human-in-the-loop is the solution to AI hallucinations, is AI-in-the-loop the solution to human hallucinations? Just kidding. Moving on to the regulatory environment – first thing that comes to mind, especially because SAS is so big in regulated industries, is finance and OSFI E-21 in particular. When you’re working with organizations that have to meet that bar, do you see it creating real urgency in the conversations you’re having? Or are clients still finding ways to buy time or building out how they respond to some of the regulations that we see? Nat D’Ercole: Well, there’s nothing like having a catalyst in place to motivate – exactly. So yeah, I think that’s where regulation provides guidance, direction, standards. These are areas that organizations can look to in order to inform how they need to move forward as well. So that’s very much welcome, I would say, in terms of helping organizations steer their investments so that obviously they comply – and no one wants to be facing penalties. Robert Dutt: Sticking with financial services – risk and fraud is highlighted as an area of strength for the Deloitte/SAS partnership. Where are you seeing the most active investment and I guess the most interesting use cases right now? Nat D’Ercole: I would say in terms of risk and fraud, procurement integrity are areas that are horizontal across organizations. You can go from a fraud perspective – not just procurement, but other types of fraud within organizations. And then from a risk perspective, there are areas around financial risk where organizations need to ensure that they have proper scenario modeling in place to understand what stresses they need to address from an organization and modeling perspective. So I would say those are common use cases – asset liability management, treasury – just being more versatile, more accelerated in terms of running these scenarios. So solutions like SAS do provide capabilities to address that speed of process. Robert Dutt: In general terms, as you’ve been here this week at the event – whether it’s a specific announcement, whether it’s an area of conversation, whether it’s what the leadership at SAS is thinking about – what’s caught your eye, caught your ear, and made you think, “Oh, I need to learn more about that”? What’s been your headline of the event? Nat D’Ercole: The keynote – the interview that Jen Chase did with Mel Robins really hit home for me, and how she applied it to AI. And for me, ensuring that leaders are leaning in and providing the change that they want – or being the change that they want to see in the organization, living the change – and also helping organizations from a leadership perspective, executive perspective, to be comfortable. Many employees, I would say, across industries and organizations – some as Mel referred to – are afraid of what AI’s potential can do to their jobs. That’s a real human reaction. And so from a leadership perspective, creating the right environment for people to begin to lean in. I’ve said many times that, “Will your job be replaced?” – and oftentimes the answer to that is, “Yes, it’ll be replaced by those folks that are embracing AI.” So now is the time to lean in and begin to learn how to use it. So Mel’s comments definitely resonated. I looked around a large room – over probably 300 tables – and many people nodded with some of those remarks. So for me, that really resonated. Robert Dutt: Pulling on that leadership thread a little bit – from where you’re sitting, what does good leadership look like in terms of guiding that AI discussion? Because that can be everything from really understanding it, making the case for it, making clear communications – not pushing, but being behind the organization’s efforts – to the kind of stereotypical thou-shalt-from-on-high, “The board tells me I have to do AI. Everyone’s talking about AI, make it happen.” Nat D’Ercole: I think from an executive perspective, beginning to make investments in AI and ensuring that there’s a path forward for the organization – as individuals, departments, and then the enterprise. So that path forward, typically when we work with clients, we look to understand where the low-hanging fruit might be, both from an efficiency perspective and effectiveness. By effectiveness, being able to get insights faster, being able to run through processes faster, but at the same time ensuring – back to our previous comment – ensuring that the human is in the loop. Executives are also looking for ROI in use cases. And I would say that ROI should be looked at most definitely, but be somewhat lenient in terms of the payback timeframe. Some may be one year, some may be two years. The important thing is to start and begin to learn from the experiences, and have a set of – or journey roadmap of – use cases that will enable the organization to be more efficiently effective as a whole. Robert Dutt: One of the bigger announcements here – and certainly the ones that got a lot of the attention and a lot of stage time – was SAS AI Navigator, built around governing AI use cases, models, and agents all at scale. Does a tool like that change what you guys deliver, or does it slot into something you’ve already been building? Does it kind of augment manual processes for you? Nat D’Ercole: Yes, I would say it complements our trusted AI framework. I really like the visuals around the AI Navigator, and it really showed how AI could be green, could be yellow, and then could be red – and then ensuring that there’s a human loop addressing those red drift areas. So it certainly complements. And knowing how to bring the two together is, I would say, areas where clients will need help, and certainly what to prioritize first. Robert Dutt: In talking to Ryan, the idea of clients increasingly looking at engagements that involve the scale of a GSI such as yourselves alongside niche industry-specific partners in the same engagement – and kind of creating that ecosystem approach. Curious if that’s something that you’re seeing and building for, or still more of an exception than rule in Canada. Nat D’Ercole: I would say, going back to a previous question, we do lead from a business perspective and clients are coming to us to ensure that the technology investments that they are making make sense from an overall business perspective. And so how those investments are realized, we will often be an orchestrator of our alliances – both technology alliances and potentially industry-specific – where there’s expertise that we need to pull in as part of solutioning for our clients. So not abnormal, I would say. Where it’s justified, certainly our ecosystems and alliances are a key value driver for our success. Robert Dutt: What’s the common genesis of that? I’m curious how often it’s you guys pulling in another party because they add something to the engagement, versus customers having an incumbent or someone they want to work with alongside you. How does that start, basically? Nat D’Ercole: It really starts with having the conversation with the client – what are they thinking, and how can we help them best, bringing the best resources and capabilities to their problems. Clients may also have biases in terms of what they’re comfortable with. So it’s understanding that and advising them on whether that makes sense or doesn’t, and why. Robert Dutt: Let’s get meta with AI a little bit here. There’s a lot of conversation in consulting about using AI to deliver AI projects faster. Is that something that you guys are doing in this practice? And what does it look like if it is? Nat D’Ercole: Oh, absolutely, Rob. These are demands that our clients are requesting – that whenever there’s any engineering in place, whether it’s custom engineering or custom build solutions, custom build models, what have you, or migrations for that matter – migrating from legacy code, legacy reporting solutions, legacy SAS to SAS Viya, etc. – leveraging AI to accelerate time to value, lower the cost of delivering. And so to that end, we have developed accelerators. We do leverage AI and AI-assisted development engineering – AI-augmented engineering, if you will – to deliver overall lower total cost of implementation. Robert Dutt: What does the team that you’re building to do this work in Canada look like? I’m curious especially what the skills you’re most looking for are, and what are the skills that are hardest to find or most need to be developed because they’re brand new. Nat D’Ercole: Certainly data scientists, engineers, domain expertise in an industry that understands the business problems, understands the business language, change management – these are core consulting skills. I would say it just gets further augmented in the area of AI, and ensuring that resources have or are building experience or getting upskilled in the areas of AI to solution our clients’ problems. So I would say those are the key areas. And the last one is that trusted AI area as well – where our risk practice is focused on that. So from overall servicing a client, being able to pull from all facets of our multidisciplinary capabilities across the firm are key aspects in terms of why clients are coming to us to support them, because it’s not a technology problem. Robert Dutt: Last one for me – what does success look like for a Canadian organization that’s, let’s say, 18 months into this kind of a transformation? And what’s the one thing that most often determines whether they get to success or not with an AI project? Nat D’Ercole: I would say having clearly defined upfront business rationale – what does the future state look like from a business economics perspective? I’m not just talking about financial return. I’m talking about what does it mean for their people, and being able to sell that. Having that vision in place and actively working to chip away at building that out with the organization, within the organization – upskilling them so that they have the necessary skill sets to move forward, take on more themselves, et cetera. So you definitely need to have the persistence, the top-level leadership to continue to drive, and I would say celebrate successes, advocate for better ways of working, and the benefits that it’s driving for the organization. So just continuing to sell the benefits, continuing to provide that vision for employees so that they understand what this means for them as they move forward. Those use cases where AI is replacing just the redundant tasks that employees are working on to get a report out – these are all areas where AI can improve the efficiencies, improve the quality, improve the trust, so that employees can focus on those higher-order, higher-value areas, strategic thinking – things that they’ve been hired to do. I’ve been in this business for over 30 years and there’s always been that 80% of the time people are pushing data around, preparing data, and 20% is being spent on value-added activities. So AI really provides now the opportunity to flip that – finally. But obviously it does require safeguards, it does require executive support and leadership. So yeah, it’s a great time to be in, to be consulting, and to be working with clients to help them realize better ways of working. Robert Dutt: All right. Well, good luck in making that flip. It is a long time coming, as you say. I hope Innovate finishes strong for you, and thanks again for taking the time. Nat D’Ercole: Thank you, Robert. Robert Dutt: There you have it – Nat D’Ercole from Deloitte Canada. I’d like to thank Nat for his time, and that wraps up our three-episode run from SAS Innovate 2026. Thanks for listening. Few things I’m taking away from this one. First, the human hallucination concept. When organizations haven’t addressed data governance, you end up with multiple versions of the truth – different teams, different numbers, different answers to the same question. Nat’s point is that this is the human-side equivalent of what we’re trying to prevent with AI governance, and that the organizations that have already solved the data governance problem are the ones that are actually ready for AI. Not the ones with the best AI strategy – the ones with the cleanest data foundation. Second, the 80/20 flip. Nat’s been in this business for over 30 years. For most of that time, people have spent 80% of their time pushing data around and 20% actually doing value-added work. AI has the potential to flip that. That’s not a new observation, but hearing it from someone who’s been watching it not happen for three decades really gives it some weight. And third, Deloitte positioning as the orchestrator. They’re not just the big GSI anchor in these deals. They’re the ones pulling in niche specialists, aligning technology alliances, and making sure the business case holds together across all of it. That ecosystem John Carey described from the vendor side – this is what it looks like from the delivery side. Hope you enjoyed this special coverage from SAS Innovate 2026. As fate would have it, we’ll have a new series starting later this week – more on that to come, but safe to say I’m currently on my way to Las Vegas. If you found this one useful, follow or subscribe to the ChannelBuzz.ca podcast. We’re on Apple Podcasts, Spotify, YouTube, and most of the major directories. Ratings and reviews are greatly appreciated and really help others in the channel find the show. Until next time, I’m Robert Dutt for ChannelBuzz.ca, and I’ll see you in the channel.

Willkommen zur neuen Folge von Insurance Monday! Heute wird es besonders spannend: Alexander Bernert lädt zu einem Deep Dive in die Zukunft der Versicherungswelt – gemeinsam mit zwei Top-Gästen direkt aus dem Insurlab Germany: Peter Stockhorst, Digitalvorstand der Zürich Gruppe und Vorsitzender des Insurlab Germany, sowie Dr. Philipp Nolte, Geschäftsführer und Antreiber der Digital- und KI-Offensive.Gemeinsam nehmen sie euch mit an den Wendepunkt der Branche: Die Zeit der reinen KI-Experimente ist vorbei – KI muss skalieren und echten Mehrwert schaffen! Welche Rolle spielen Start-ups, wie gelingt Transformation wirklich, und warum ist „Venture Clienting“ kein Buzzword mehr, sondern echter Wettbewerbsvorteil? Speaker B, Speaker C und Speaker A werfen einen Blick hinter die Kulissen, liefern frische Insights und diskutieren über Leadership, Foresight und die nächsten Gamechanger, die Versicherer kennen müssen.Freut euch auf exklusive Einblicke, ehrliche Praxisberichte und inspirierende Impulse für alle, die in einer sich rasant verändernden Finanzwelt vorne mitspielen wollen!Schreibt uns gerne eine Nachricht!Folge uns auf unserer LinkedIn Unternehmensseite für weitere spannende Updates.Unsere Website: https://www.insurancemondaypodcast.de/Du möchtest Gast beim Insurance Monday Podcast sein? Schreibe uns unter info@insurancemondaypodcast.de und wir melden uns umgehend bei Dir.Dieser Podcast wird von dean productions produziert.Vielen Dank, dass Du unseren Podcast hörst!

Podcast: Don't Panic! It's Just Data Guest: Jignesh Patel, Director of Product Strategy at Stibo Systems and Elsebeth Gundersen Jensen, Product Owner at NetsHost: Dr Joe Perez, Data Analytics Expert and Amazon Bestselling AuthorWe're living in times of an always-on digital economy where there's no room for data errors. In the recent episode of the Don't Panic It's Just Data podcast, host Dr Joe Perez, Data Analytics Expert and Amazon Bestselling Author, sat down with Jignesh Patel, Director of Product Strategy at Stibo Systems and Stibo Systems' customer, Elsebeth Gundersen Jensen, Product Owner at Nets. Perez pointed out that even the smallest inconsistency can "ripple completely across an entire operation, instantaneously." This reality is prompting enterprise tech leaders to rethink how they manage, govern, and use data, especially with the rapid growth of AI adoption.Overall, the guests send out a clear message – trusted, real-time data is now a crucial part of business infrastructure.Also Watch: From Chaos to Launch: Your Product is Ready, Your Data Isn'tWhat is the Hidden Cost of Untrusted Data?For large enterprises, especially those growing through mergers and acquisitions, fragmented data systems are almost unavoidable. Jensen noted that when combining multiple customer portfolios, inconsistencies often arise in even the simplest fields, like organisation numbers formatted differently in various systems.“When you bring in different customer portfolios, you will also get this scattered data picture that you don't want in a master data management system,” she explained.According to Patel, the lack of trusted data impacts four key areas which includes customer experience, revenue growth, decision-making, and operational efficiency. Without a unified customer view, enterprises struggle to offer personalised experiences or spot cross-sell opportunities. Moreover, analytics based on unreliable data undermine executive confidence and increase compliance risks.These issues are made worse by speed. Alluding to her observations, Jensen told Perez and Patel that modern customers expect contract changes or service interactions to be updated almost instantly. “They don't want to wait a day,” she stated. “Everything should be faster, better, and accurate.”Also Watch: Why is a Customer Data Strategy a Competitive Edge?How are Enterprises Mastering Intelligence?Traditionally, Master Data Management (MDM) has focused on creating the “golden record,” a single, reliable version of key business entities like customers or products. While this remains important, Patel believes this idea is changing quickly in the AI era.“MDM is moving beyond data correctness towards what I call mastering intelligence,” he said. “AI systems rely on trusted context—understanding what entities are, how they relate, and the business rules that apply.”This change is part of a larger transformation in enterprise architecture. Decision-making is no longer limited to human-driven dashboards; it is increasingly spreading across applications, analytics platforms, and AI agents acting in real time. In such a setup, inconsistent data does not just create errors but it can amplify it.“AI doesn't eliminate the need for MDM or data governance. It emphasises it,” stated Patel. For enterprises heavily investing in AI, this insight is vital. Without a strong data foundation, AI models might provide insights but not dependable results.As enterprises move toward AI-driven and even agent-based business models, the need for trusted data will grow even more important. Patel highlights new questions from the C-suite – How will AI agents find my products? Why isn't my business being recommended?The answer increasingly depends on structured, high-quality data. “AI success is dependent on trustworthy data,” Director of Product Strategy at Stibo Systems says. “MDM and governance are the foundation for the next generation of intelligent business systems.”For enterprise leaders, the key directive to note is in the race to implement AI, data trust is the competitive edge and not only the requirement. Key TakeawaysReal-time trusted data is essential for enterprise AI success and operational resilience.Poor data quality directly impacts customer experience, revenue growth, and compliance.Modern Master Data Management (MDM) is evolving from “golden records” to AI-ready data intelligence.Proactive data governance must replace reactive data cleanup to scale in real-time environments.A unified data model is the foundation for accurate, consistent, and AI-driven business insights.Chapters00:00 Introduction to Data Governance and MDM02:06 The Shift to Real-Time Data05:27 Business Risks of Lacking Trusted Data08:20 Growth Through Mergers and Acquisitions15:29 The Role of MDM in AI Initiatives20:02 Transitioning to Proactive Data Management22:01 Advice for CIOs on Managing Product DataFor more information, please visit em360tech.com and stibosystems.com. To learn more about AI in the MDM space and how they're progressing enterprise analytics intelligently, follow:Stibo Systems LinkedIn: @StiboSystemsStibo Systems X: @StiboSystemsStibo Systems YouTube: @StiboSystemsGlobalEM360Tech YouTube: @enterprisemanagement360EM360Tech LinkedIn: @EM360TechEM360Tech X: @EM360TechFollow: @EM360Tech on YouTube, LinkedIn and X#MDM #DataGovernance #EnterpriseAI #DataQuality #TrustedData #AIStrategy #RealTimeData #DigitalTransformation #StiboSystems #TechPodcast

Cybersecurity now runs on data, and that dependence is reshaping how organizations think about privacy, risk, and governance. As teams collect more signals to detect threats, long‑standing assumptions about how data should be limited, shared, and protected are being tested. In this episode of #shifthappens, Bojana Bellamy, President of the Centre for Information Policy Leadership (CIPL), discusses why privacy and cybersecurity can no longer be governed in silos. She explains how regulation, global data flows, and technologies like AI are pushing organizations toward integrated risk models and shared accountability — shifting security and privacy from technical functions to leadership decisions.

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

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

Today, we're joined by Harsha Chintalapani, Co-Founder and CTO of Collate, an AI-native semantic intelligence platform. We talk about:Solving complex data challenges to drive success at UberThe dream of getting LLMs to identify context for improved semanticsThe challenges in applying meaning and semantics at the metadata levelHow open source attracts talentThe value of retaining the ability to model in the new world of AI-generated code

In this episode, I sit down with Bob Seiner, a true pioneer who has been working in data governance since before it was even called governance. We dive into why he calls BS on the trendy term "data enablement" and how his trademarked approach, Non-Invasive Data Governance, formalizes what organizations are already doing without beating employees over the head.We also unpack his latest concept, The Data Catalyst Cubed, and get into a fascinating discussion about the precarious state of data security in the age of LLMs and autonomous AI agents like OpenClaw. Plus, Bob shares some great war stories about building the T-DAN newsletter using Microsoft FrontPage back in 1997 and drops his best advice for standing out and building a personal brand in the noisy data industry.Where to find Bob:KIK Consulting: kikconsulting.com LinkedIn:   / robert-s-seiner-445313  Books: Non-Invasive Data Governance and The Data Catalyst Cubed

In part one of this episode, Host KJ Burke is joined by Jodey Hogeland, Global Technologist at Dell Technologies, to discuss early containerization foresight, the growing data deletion dilemma in an AI-driven world and how IT leaders are shifting from cost centers to strategic partners at the executive table. This conversation sets the foundation for understanding why infrastructure decisions matter more than ever. To learn more, visit cdw.ca Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In episode 284 of our SAP on Azure video podcast we talk about  Data Governance & Multi-cloud flexbility.Find all the links mentioned here: https://www.saponazurepodcast.de/episode284Reach out to us for any feedback / questions:* Goran Condric: https://www.linkedin.com/in/gorancondric/* Holger Bruchelt: https://www.linkedin.com/in/holger-bruchelt/ #Microsoft #SAP #Azure #SAPonAzure #Purview #Data #Governance #MultiCloud

The conversation begins with a close look at India's data protection regime, particularly the DPDP Act and its emphasis on consent. Nikhil challenges the perception that the law is overly consent-driven, pointing to a range of exemptions and alternative legal bases for processing data. At the same time, he highlights gaps in enforcement and deterrence, arguing that the current framework may struggle to address large-scale misuse of data or systemic harms. On AI governance, Nikhil makes a case that India does not need a sweeping, EU-style AI law, at least not yet. Given India's legislative pace, enforcement gaps, and how fast AI is evolving, he thinks strengthening existing laws and making targeted amendments is a far more practical path. He does, however, flag artificial intimacy as something that deserves serious attention soon. AI-powered companionship is supercharging the loneliness economy, building emotional dependency at scale, and raising risks that no existing framework is really built to handle. Closer to home, Nikhil offers a window into how AI is changing legal practice at Trilegal, where 75% of lawyers now use AI in their daily workflows. The firm is simultaneously building AI products, using them internally, and advising clients on AI risk, a position Nikhil sees as an advantage rather than a conflict. For him, the era of lawyers who write code and speak directly with engineers is not something to fear but a long overdue shift in what it means to practice technology law. Episode ContributorsNidhi Singh is an associate fellow at Carnegie India. Her current research interests include data governance, artificial intelligence and emerging technologies. Her work focuses on the implications of information technology law and policy from a Global Majority and Asian perspective. She has previously contributed to the Indian Express, The Secretariat, Medianama and HinduBusiness Line.Nikhil Narendran is a Partner in Trilegal's Bengaluru office and part of the TMT practice of the firm. He is a subject matter expert in the technology, media, and telecom communication space. Nikhil focuses on the interplay of technology, human lives, and commerce. He has substantial experience in advising companies on telecom, media and technology laws in relation to their entry into India, operations, strategy, policy, regulatory issues, disputes, and business models. Every two weeks, Interpreting India brings you diverse voices from India and around the world to explore the critical questions shaping the nation's future. We delve into how technology, the economy, and foreign policy intertwine to influence India's relationship with the global stage.As a Carnegie India production, hosted by Carnegie scholars, Interpreting India, a Carnegie India production, provides insightful perspectives and cutting-edge by tackling the defining questions that chart India's course through the next decade.Stay tuned for thought-provoking discussions, expert insights, and a deeper understanding of India's place in the world.Don't forget to subscribe, share, and leave a review to join the conversation and be part of Interpreting India's journey.

Why Data Governance Is the Key to AI Biosecurity, with Jassi Pannu and Doni Bloomfield Alan Rozenshtein, research director at Lawfare, spoke with Jassi Pannu, assistant professor at the Johns Hopkins Bloomberg School of Public Health and senior scholar at the Johns Hopkins Center for Health Security, and Doni Bloomfield, associate professor of law at Fordham Law School, about their proposed framework for governing biological data to reduce AI-enabled biosecurity risks. The conversation covered the origins of the proposal in the 50th anniversary of the 1975 Asilomar conference on recombinant DNA; the distinction between general-purpose AI models and biology-specific foundation models like genomic language models; the biosecurity threats posed by AI, including uplift of novice actors and raising the ceiling of expert capabilities; the proposed biosecurity data levels (BDL 0-4) framework and how it draws on precedents from biosafety levels and genetic privacy regulation; the challenge of capabilities-based rather than pathogen-based data classification; the institutional and regulatory mechanisms for enforcement, including the role of NIH grant conditions and a proposed mandatory federal regime; international collaboration and the importance of U.S. leadership given that most high-tier data is generated domestically; the relationship between the proposal and open-source biological AI development; and the offense-defense imbalance in biosecurity and the case for mandatory gene synthesis screening. Mentioned in this episode:Jassi Pannu and Doni Bloomfield et al., "Biological data governance in an age of AI," Science (2026)Jassi Pannu, Doni Bloomfield, et al., "Dual-use capabilities of concern of biological AI models," PLOS Computational Biology (2025)Dario Amodei, "The Adolescence of Technology" (2026)The Genesis Mission Executive Order (November 2025) Hosted on Acast. See acast.com/privacy for more information.

Winning the AI Trust Race Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ In this compelling discussion from the Ultimate Partner Winter Retreat, Vince Menzione sits down with Marc Monday of ServiceNow and marketing expert Ashleigh Vogstad to deconstruct the “tectonic shifts” currently hitting the tech industry. As the market moves from AI excitement into a period of “POC fatigue,” the conversation pivots to the essential groundwork required for success: clean data, governed workflows, and the transition from an attention economy to a trust-based machine economy. They explore how Gen Z's massive spending power is reshaping marketplaces and why simply automating a 27-step bad process with AI is a recipe for failure. Whether you are a partner manager or an entrepreneur, this episode provides a roadmap for staying human in a machine-to-machine world. Key Takeaways The market is experiencing “POC fatigue,” making it critical to transition from experimental AI to real-world value driven by central databases and knowledge graphs. ServiceNow is shifting focus toward “Control Tower” solutions to govern and orchestrate how various AI agents interact with mission-critical data. We are moving from a human-centric “attention economy” to a “trust economy” where machines make high-stakes decisions on behalf of users. Automating an existing 27-step approval process without rethinking the workflow first results in an “automated bad process” rather than a solution. By 2030, 75% of B2B buyers will be Gen Z, a demographic that favors authentic voices and direct-to-fan platforms like Substack over traditional channels. Hyperscaler partnerships are becoming essential “third-party validation” layers that allow AI agents to verify a company's win rates and credibility. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags ServiceNow, Marc Monday, Ashleigh Vogstad, Ultimate Partner, AI Fatigue, Agentic AI, Control Tower, Trust Economy, Knowledge Graph, Workflow Engine, Gen Z B2B, Marketplace, Hyperscalers, Machine-to-Machine, Data Governance, POC Fatigue, Substack, LinkedIn, Digital Transformation, Co-Selling, Partner Programs, ERP Intelligence, Uncanny Valley, Marketing Lag, Shared Business Planning. Transcript Ashleigh and Marc Monday Audio Episode [00:00:00] Ashleigh Vogstad: But the reality is, if you’re not using AI in a very meaningful way in your sales and marketing functions of your businesses, I mean you’re just way behind. [00:00:13] Vince Menzione: We just finished Ultimate Partners Winter Retreat here in beautiful Boca to a sold out crowd. Come join me now for a compelling discussion on the impacts of the tectonic shifts we’re all seeing. Maybe just a second about roles and responsibilities. Most of you know Ash from previous, uh, things you’ve been doing with us. [00:00:34] Vince Menzione: But, but maybe for you, Martin, this is your first time. [00:00:36] Marc Monday: Where should I [00:00:37] Vince Menzione: look there? Alternate partner. Their lives [00:00:38] Marc Monday: there? [00:00:39] Vince Menzione: Uh, yeah, over here is good. Either one. [00:00:41] Marc Monday: Look over there. Which would you prefer? [00:00:43] Vince Menzione: Um, this is good. [00:00:44] Marc Monday: Great. It’s, [00:00:45] Vince Menzione: and, but right now I’m just asking you for everybody, tell everybody who you are in your role. [00:00:49] Vince Menzione: ’cause you just shifted roles at ServiceNow. It’s [00:00:51] Marc Monday: true. It’s true. Hello everyone. My name is Mark one day and I lead the America’s partner business, uh, partner sales business at ServiceNow today. And effective Monday I’ll lead the global partner team. Uh, Jen Odes, who’s been on the podcast. Yes. She’s been and I are switching roles. [00:01:07] Marc Monday: Jen’s gonna go run the patch and I’m gonna run the programs, uh, effective next week. [00:01:11] Vince Menzione: That’s fantastic. [00:01:12] Marc Monday: And I live in Seattle. [00:01:15] Vince Menzione: You live in Seattle. Yeah. And you made the trip out here. I really appreciate that. It’s a long journey. And Vancouver or Whistler? So both of you came from the, from the West coast. [00:01:23] Marc Monday: This may be the first snowboarding panel in history of ultimate partner. [00:01:29] Ashleigh Vogstad: I liked the question earlier. Somebody asked, did anyone leave the snow to be here? It was literally a blizzard. I did not know if I would make it driving at 4:00 AM to the airport in a total whiteout. [00:01:41] Marc Monday: You’re getting zero sympathy from me Live in Whistler. [00:01:44] Vince Menzione: So, so Service now has been, uh, I would say on the forefront of this AI thing. I mean, like you were early in and control towers, that I always get the, the nomenclature wrong, but I do feel like we are seeing some, a level of fatigue right now. And I keep seeing, I mean, it feels like every, we’re getting whiplashed at least the last few weeks. [00:02:03] Vince Menzione: Are you seeing that? And what are the two or three biggest blockers you’re seeing now in the market? [00:02:10] Marc Monday: I think there’s, there’s a lot of excitement obviously in the marketplace, but there is a bit of AI fatigue. There’s a POC fatigue, I think that’s going on. I think the reality is we have to make AI real, and the reality is it starts with good data, uh, a, a central, uh, a database, and really making sure that that’s extensible through a knowledge graph. [00:02:31] Marc Monday: And then that provides us the ability to identify that workflow. Then importantly, um, making it real and, and as fast as possible. And I think that’s really important for the customer. One of the value props of ServiceNow, of course, is that we’ll meet the customer where they are with whatever their estate has, [00:02:47] Vince Menzione: right? [00:02:47] Marc Monday: So any hyperscaler, any workload, any core dataset, um, any LLM and, um, our history is as a workflow engine, and so we can bring that level of knowledge to their business. And then importantly, we bring together the governance and orchestration from a control tower perspective. [00:03:08] Vince Menzione: Nice. Ash had perspective on this, on the kind of the whiplash we’ve been feeling. [00:03:13] Vince Menzione: From From the marketing agency side? [00:03:15] Ashleigh Vogstad: Yeah. I mean, what comes to mind is the Miriam Webster dictionary said that LOP is the 2025 Word of the Year lop and Satchin Nadella actually came out with some press immediately following on that, saying that essentially that LOP is an exactly a useful construct to be having a conversation around the future of media. [00:03:37] Ashleigh Vogstad: But I think what this is pointing to is just we’re all navigating. Exactly how much AI is good ai, and maybe we will get into a little bit later, but what is the difference between selling to a human being and selling to a machine? Um, and really when we’re getting into this age agent landscape, it’s much more about that machine to machine conversation. [00:04:01] Ashleigh Vogstad: It’s not necessarily. Human eyeballs on recommendation links that is paid for by advertising. It’s more of a trust economy actually, where machines wanna be able to make decisions on our behalf with high trust so that you continue to enable that machine to make those decisions for you. [00:04:22] Vince Menzione: We talked about the data. [00:04:23] Vince Menzione: I thought we’d double click a little bit on that. In fact, that point it would normally have been here, but because of the snow wasn’t able to, they focus in on this governance and this data element. I was thinking maybe we could talk a little bit about that, because it doesn’t seem like AI will work properly if we don’t have the data to stay governed and clean, right? [00:04:42] Marc Monday: I think this is the amazing opportunity for the partners out there. They do this already. This is one of those assessments that’s so quick and not easy, but clear to deliver a value prop as a partner. Let’s get you ready for ai. Let’s make sure that we’re ensuring that your data’s in a extensible in a way across, uh, some sort of knowledge graph that can be accessed across a number of different, um, use cases. [00:05:09] Marc Monday: And oftentimes that’s multiple data sets. And so how do you get those columns and rows organized in a way that’s extensible for an agent, that we’re basically asking to do something that is an unique opportunity for partners right now. And I, I think that we maybe missed that step. So I see what I see happening right now is we’ve gotta come back to that as a starting point for the partners. [00:05:31] Vince Menzione: Let’s talk about agent ai or you also have orchestration AI as well. I wanna talk about their, your new service platform specifically, but maybe if you could double click with this on that. [00:05:42] Marc Monday: Well, I think that, you know, everyone is kind of trying to figure out how do we get there and who’s gonna orchestrate and govern what AI agent is calling on, what data set at what time, and what sequence. [00:05:54] Marc Monday: You may have a mission critical application that needs to have immediate access, and you may have other agents that have casual access. How do you control that in a meaningful way is gonna be become increasingly important. So we have the idea of this product that we call control tower. The control tower gives you the ability to manage that orchestration as well as the governance. [00:06:14] Vince Menzione: Any perspective on this? [00:06:17] Ashleigh Vogstad: I think I’ll share the perspective. As an entrepreneur, I know many people here represent. Companies that are our clients and are, are massive in scale and, and hyperscalers. But I think there are some people in the room who are running their own organizations. I think when I came out, Vince asked, you know, Ash growth mindset, how are you actually living this? [00:06:36] Ashleigh Vogstad: And we’re going through a journey in my business right now around what are all of the data sources that we have and how can we get that into an enterprise resource planning type system so that we can then overlay more intelligence. And that’s kind of where we’re at in the, it’s funny ’cause when you look at those maturity curves, they try and fit you in a box. [00:06:57] Ashleigh Vogstad: Nobody here likes being in a box. Um, and we’re in a corner. Yeah. In some ways it’s like we’re in that agentic box. I built an agent last week, funny enough for Microsoft actually, um, an executive comms agent, but in one hand we’re on that end and on the other, our data’s a mess and we really can’t apply a lot of intelligence to the majority of the data sources within our organization. [00:07:20] Ashleigh Vogstad: So we’re getting that all together right now. [00:07:22] Vince Menzione: When you came out, we talked a little bit, you were, you were mentioning having an advertising agency, marketing agency. The changes that are going on right now. Right? The attention economy and the trust economy. And I thought maybe you could double click with us on that. [00:07:35] Vince Menzione: ’cause that’s, uh, very interesting to see this shift. [00:07:39] Ashleigh Vogstad: It’s a huge shift. So, uh, 1964 Canadian philosopher, Marshall McCluen, he comes out and he says The medium is the message. [00:07:49] Audience Question: Yeah. [00:07:49] Ashleigh Vogstad: And so you wanna think about how is agenta a different medium and what are the biases that this medium inherently has? So in my media world, you know, you get these storytelling tools rolling out at Speed Chat, GBT, soa, and in the beginning they’re really at that low end of the curve. [00:08:08] Ashleigh Vogstad: You know, they can produce a shitty first draft, uh, but the content that they’re creating is really low emotional resonance. If you take kind of a neuroscientist perspective on this, and I’m definitely not a neuroscientist, but the part of your brain that’s responsible for that pattern recognition, your cortical sal circuit, that’s what’s kicking in. [00:08:29] Ashleigh Vogstad: And when you’re looking at, say, an advertisement, you’re starting to think, you know, is what I’m looking at actually commensurate with what I expect to see? And when it’s not, you can trigger that what psychologists call your uncanny valley. Now some will argue that on County Valley is really diminishing these days because AI generated media is getting better and better. [00:08:52] Ashleigh Vogstad: And I do think that it’s something you want to lean in, but you also wanna think intelligently around how you’re using this new medium and exactly what its, what its biases are. [00:09:03] Vince Menzione: Is that the gut syndrome? Like when you feel something in your gut? Is that what you described? [00:09:07] Ashleigh Vogstad: Yeah. Yeah. I mean, the classic example is Coca-Cola. [00:09:10] Ashleigh Vogstad: So 2024 Coca-Cola rolled out their very nostalgic for many of us holiday campaign, and they decided to use tools like Luma Dream Machine to make this whole Santa Claus North Pole, but AI generated universe. And it had that classic stuff around, you know, six fingered people and it gave you this. Kind of creepy post-apocalyptic vibe and the campaign completely tanked in market. [00:09:37] Ashleigh Vogstad: Or more recently, last year, mango rolled out a new fashion line Mango’s a huge global fashion retailer. They rolled out a new fashion line, and in their advertisements they had AI generated models and AI generated clothing. Like to sell a real line. So, you know, you, you have to really be thinking about, again, when we come to an attention economy based on human beings or a machine economy based on trust, many of these companies are still selling to us human beings. [00:10:09] Ashleigh Vogstad: And I, I think they can forget that at times. [00:10:12] Vince Menzione: So what’s your guidance to customers today and to this audience and viewers watching us today from a go-to market motion? In this world of ai, like what? What are you telling? What? How are you counseling these organizations? [00:10:25] Ashleigh Vogstad: You need to have an authentic voice. [00:10:27] Ashleigh Vogstad: We, we’ve heard this a million times, so I’ll try and put a bit of a, a different spin on it at platforms direct to fan platforms, things like Substack. Substack grew 48% last month. I mean, we are seeing this skyrocket, and that’s a new channel where you can have an authentic voice. Many people in this room, myself included, we live on LinkedIn as the business to business platform. [00:10:50] Ashleigh Vogstad: Consider expanding out into, into a new channel, um, would be one of my recommendations. Interesting. [00:10:57] Vince Menzione: Any, anything else from, uh, what you developed or what you use and ai and what do you, what, what tools do you recommend they use and what. [00:11:06] Ashleigh Vogstad: There. [00:11:06] Vince Menzione: Yeah. [00:11:06] Ashleigh Vogstad: What are we seeing with our, so I can give this example of this executive comms agent that we built. [00:11:12] Ashleigh Vogstad: Or even part, yeah, we’re building agents all the time, so what we try to do is think about what is our customer seeking to solve. We heard a lot today about outcomes, and then we challenge an AI first lens, which is how can we build something with AI to make this easier, better, faster, more creative? We’ll even do things, we’re a marketing agency, so we’ll even do things like beat the bot, pitch competitions. [00:11:37] Ashleigh Vogstad: So this is where you’re inviting your agent into the room and you’re asking it to put the pitch together, say for ServiceNow and Microsoft, and what can it come up with? And then we put it in a room of human beings and see who can out pitch. Bot, um, and come up with a more novel, creative idea. But the reality is, if you’re not using AI in a very meaningful way in your sales and marketing functions of your businesses, I mean, you’re just way behind. [00:12:07] Ashleigh Vogstad: And I see it a bit more advanced in all honesty and sales because I think some of your large. Organizations push the AI down to the sellers. Mm-hmm. Um, so they’re somewhat forced to use it, but in marketing, I’m still seeing a real lack, which is funny since generative AI came out in 2022 and everybody thought the marketing function was the one to really be disrupted and displaced. [00:12:30] Ashleigh Vogstad: I do think your marketing teams need to be leaning in more. [00:12:35] Vince Menzione: We were talking about trust earlier. I wanna weave this into the conversation. Right. How do, how do you. How do you think through trust and applying trust in the area I world, I’ll ask you both this question under service. Now think about it. How do you think about it or transcend? [00:12:54] Marc Monday: Maybe I’ll take a step back. I, I think just to kind of go back to the previous question, I think we’re in this age of massive complexity. Incredible complexity. Nina said it earlier, the customers kind of want us to tell them what to do. What are the steps? We’re at this dichotomy of this level of complexity that’s almost unimaginable and we have to make it simple. [00:13:18] Marc Monday: I think that’s the first one. And then that, that is put up against this notion of we have to go incredibly fast ’cause the market’s moving faster than we can even understand it. [00:13:28] Vince Menzione: Yeah. [00:13:29] Marc Monday: And then we have to add on this veneer, and this is where the partner community becomes so important of how do we scale? [00:13:35] Marc Monday: So how do you take simplicity, speed, and scale and bring it to market? It starts with the data, of course it starts with the workflow, but I might just take a giant step back and say one of the things that another partner opportunity you might run to really consider is automating a bad process, even with AI is still a bad process. [00:13:58] Marc Monday: So again, a partner opportunity is, let’s zoom back out and say if your approval. Takes 13 steps in 27 days, building an AI process around that. Without rethinking it might not be the right solution. So I think part of it is also like rather than just dictating all of the steps, part of it, to the point of telling the customer the steps is getting them to participate in that conversation. [00:14:29] Marc Monday: Why do you have 27 approval layers? Well. It’s the most dangerous thing in the language. It’s because we’ve always done it that way. Well, what if we did it differently? Yeah. And so I think that’s an area where the trust is a two-way street and you can’t just the part, the customer shouldn’t just outsource all of their decision making to you. [00:14:50] Marc Monday: At the same time, you have to bring them into that discussion of what are you trying to accomplish and what is your, um, risk appetite relative to that. [00:15:02] Ashleigh Vogstad: Yeah, that, that’s great, mark. I mean, trust is a really important conversation. I think about the Amazon versus Perplexity lawsuit right now that some are headlining the end of commerce. [00:15:14] Ashleigh Vogstad: Um, and so really this precedent setting case, what this is about is perplexity. Essentially is disintermediating the Amazon platform. So you know it’s making purchase decisions on your behalf, so, so this idea of trust in the agent world is something I think about a lot. And how do you optimize trust for this agentic world? [00:15:36] Ashleigh Vogstad: The professor I was mentioning, Eric Zow, who has this attention economy and the trust economy for agents where my research is leaning in is really around what is the hyperscaler layer on top of that. My working theory is that hyperscaler partnerships are just gonna become more important because the machines need to verify via trusted third party data sources what it is that you’re up to. [00:16:02] Ashleigh Vogstad: So how many deals have you done? Uh, what is your win rate percentage? This kind of information is incredibly valuable to the agent world, and so I think we’re gonna see an. Increasing lean in to these third party validation co-selling systems like partner center. [00:16:22] Marc Monday: I mean, just to add onto yeah. This idea, I mean, we do talk a lot about trust, but attention is probably underserved if I think about the role of a partner manager or an alliance director, it’s all about the trade-offs of what am I gonna spend my time on today? [00:16:37] Marc Monday: And you’re being pulled in a million directions, and I dunno about you, but it’s probably 900 to 10,000 unread emails and maybe you’ll respond to your immediate messages and if something happens, you’ll respond in in text. Part of it is also delineating between the busyness and the impact, and I think a lot of that’s also part of this discussion of how do we get focused on the outputs that matter. [00:17:02] Marc Monday: Really helping the customer get there through that discussion, which again goes back to it has to be a dialogue with the customer rather than just, this is the solution. Here’s our SOW. We’ll see you in six months. [00:17:14] Vince Menzione: Agree. We have a couple extra minutes. I was thinking of maybe opening it up for you. Any questions? [00:17:19] Vince Menzione: We have a mic in the back and I’m sure people have questions about this topic is, is fascinating to me and I wanna make sure that we’ve covered any of the questions we have. We have one right in the front from Shannon. [00:17:30] Marc Monday: Send the hard [00:17:31] Vince Menzione: questions over there. Not Yes. I’ll take the Easy books. Yeah. [00:17:36] Audience Question: You referenced marketing lag. [00:17:38] Audience Question: I think all of us would love to see marketing leading. [00:17:41] Ashleigh Vogstad: Yes. [00:17:42] Audience Question: Um, so how are you infusing within your marketing team at different levels around content creation? Um, there’s so much, uh, ego right on being a graphic designer or an editor, a copy editor that they. The human inflation in that conversation is a, is a hard thing to get them over. [00:18:02] Audience Question: And now AI can help this. How are you? [00:18:04] Ashleigh Vogstad: Yeah, let’s have a conversation after. But you just brought up a funny No, I’m gonna answer as well, but you brought up, brought up a funny, uh, conversation we had internally, just in the last 24 hours we’re interviewing for a new creative director and one of our candidates said, yes, but I don’t do Figma. [00:18:20] Ashleigh Vogstad: I’m not a UX person. I just laughed and I said, you know, the day is coming where It’s a designer, it’s a UX person, it’s a project manager, a program manager, a copywriter. You know, AI is condensing a lot of roles in that way. So I think being multidisciplinary in your skillset is, um. Is quite valuable, but I’ll also take this into a hyperscaler direction and say, no. [00:18:46] Ashleigh Vogstad: Here audiences, 75% of it buyers are going to be Gen Z by 2030. They have 12 trillion in spending power. I was in Silicon Valley yesterday, uh, helping a customer with a wind story. They did a $12 million transaction through Marketplace. Now that’s very impressive, but it would’ve been more impressive two years ago. [00:19:06] Ashleigh Vogstad: There are more and more, 10 million plus. Deals happening through marketplace. And so if you look at that Gen Z and start to understand them and their buying behavior, like another example is, I think it’s 80%, no, no half, sorry, half of Gen Z last month made a purchase via Instagram, TikTok, or YouTube. They are used to making these online transactions and average purchase price is going up. [00:19:35] Ashleigh Vogstad: You know, $500,000 plus is starting to be the average in some of these enterprise selling platforms. So as a marketing team, how are we kind of going in and leading the marketplace? Conversation I think is really critical and there’s technical elements to that. [00:19:52] Marc Monday: Maybe the caveman view of that would be, um, the other side, which is I think someone earlier said, we have to know where our customer is at. [00:20:00] Marc Monday: And a lot of our, we are very lucky. We live in this very insular tech bubble and we’re thinking about, you know, where we are 10 years from now and the customer’s gonna are gonna get there eventually, and it’s gonna happen faster. But I would say in marketing, I mean the two easiest use cases right now are around localization. [00:20:16] Marc Monday: Language localization and then specific market localization, like we don’t have to solve world hunger right now. There are some steps and those steps are some of the easy things. Localization probably is a big component of your marketing budget. That’s something that you can get really good, really fast language localization, addition market localization. [00:20:35] Marc Monday: This market is a healthcare market. This market is an SMB market. Those are two areas where that through partner marketing motion can to get accelerated very quickly and has a tremendous ROI. [00:20:47] Vince Menzione: Yeah. Great one. Nina, you had a question [00:20:50] Audience Question: three Mark. You, you just, you just hit on part of it is that value proposition message is, it’s really easy in AI to, to fine tune that. [00:20:59] Audience Question: The other thing that I’ll be very transparent about, um, at least in my organization and America’s partner, we only work with um, third party. Marketing vendors now that are AI first period. [00:21:12] Audience Question: Nice. We [00:21:12] Audience Question: completely cleaned out who the vendors are that we will approve to work with. Wow. Um, so because we can also see the cost reduction, but it is a mindset change. [00:21:22] Audience Question: They have to, they, if, if they’re gonna be positioning this, it has to be inherent. It has to be part of their culture of, at. [00:21:29] Marc Monday: Ashley made a really wonderful point. I mean, this bad first draft is so key and so, you know, in the past we would’ve spent. A couple days or maybe even a week on a really bad first draft. [00:21:40] Marc Monday: And the bad first draft is just to generate feedback. You can generate a bad, a good, bad first draft in a couple of minutes with the right prompts. [00:21:48] Vince Menzione: Yeah, good. Point. Point questions to the back, Steven. [00:21:55] Audience Question: Mark, as you guys are building out agents, the orchestration to manage them, is that taking you into workflows outside of ServiceNow? [00:22:05] Audience Question: Yes. [00:22:07] Vince Menzione: Repeat the question, sorry. Yeah. Just in case people aren’t getting [00:22:09] Marc Monday: Yes. The question is, um, for ServiceNow specifically, um, is that taking you out of your traditional business? And I think he, he means it’s probably business in it, and the answer is yes. So our value promise is that we can go north, south, east, west, across the estate. [00:22:24] Marc Monday: Regardless of the workflow. So there are scenarios where we are expanding. Of course, we have a commitment to driving the CRM business, moving beyond just customer service management, but all the way through the process to CPQ and we’ll productize many of those things. But the reality is, if the workflow touches, let’s say. [00:22:42] Marc Monday: Uh, a, a big database, you know, from one of your known providers, uh, an HCM system, your our traditional IT system. This is maybe around service delivery of a particular set of kit to a new employee for onboarding or offboarding across a number of those systems of record. Yes, we’ll continue to do that, and honestly, it’s the value promise for us that because we are capable of working with. [00:23:06] Marc Monday: Every hyperscaler, every application, every data set, we can go up and down and across the state. [00:23:12] Audience Question: Hi everyone. I’m Jen Pauls. Hey, Jen. I have a um, I have a question for you. So when you’re incorporating AI, and also you mentioned trust, how do you make sure that the offerings that you’re coating on are feasible specifically for that whole individual partner and client? [00:23:34] Audience Question: And you’re not repeating. Something. Does that make sense to you? Yeah. Like how do you make sure that there is an individualized component that is original in thought, even though you’re feeding this pipeline, all these combined thoughts? [00:23:51] Marc Monday: I, I don’t wanna push back on the premise, but I do think in some instances, partners, implementers will have competing solutions that do effectively the same thing. [00:23:59] Marc Monday: Ideally they’re differentiated, but I do think publishing a, a standard. Particularly from a security and a reliability perspective, what that traditionally we would’ve called that API standard, and then a level of validation, either via human validation or systemic AI validation is really key. Um, the solution that gets marketed, let’s say, in our marketplace should work and it should be secure and it should be reliable. [00:24:25] Marc Monday: So we processes to manage that, if that’s the question. [00:24:29] Audience Question: Right? Well, it would, you know, yes. Yes. But. Um, when you’re trying to create a dispute or an offering, right, that’s specific to that particular partner, this is where I’m going. How do you make sure that the thoughts that are coming in are specifically, I guess, individualized for that one partner and what they’re doing and how they’re going to make a new, um, new, uh, track or a new journey in what you’re selling? [00:24:57] Ashleigh Vogstad: I mean, I would answer that I think with differentiation is still really important. And if anything, if we had an 80 20 rule for 80% of the lift is coming from ai, we’re all still here and employed because there is a rule for the, the human, at least currently in that 20%. And I would say. Running teams who are often building new offers and products, both on the ISV and SI side of things. [00:25:25] Ashleigh Vogstad: Getting that unique differentiation is critically important. Like that’s where a lot of value is created. Or you could look at, I mean Nabil probably has stories about this all day in the MSP world is it’s really challenging for MSPs to differentiate on top of their core offering, but that is where value creation happens. [00:25:43] Ashleigh Vogstad: Yeah. Nina more, I’ll [00:25:44] Audience Question: just piggyback on that. My recommendation to a lot of, of our partners today is build out agents at that 80% watermark. Right? And that’s a little bit what you were talking about, the 80, 20, 80% of that functionality. Quite honestly, if you’re looking at an call center or something, is something that can be ported. [00:26:05] Audience Question: The, the magic is working with the partner on what X 20 is that differentiates their business, their experience, how, uh, the applicability to. So I, I will, I, to your point about ology, the premise, I mean it, to me, I think repeatability is, is awesome. It’s a superpower. It’s gonna get us there faster. It’s in that 20%. [00:26:31] Audience Question: Yeah. [00:26:34] Vince Menzione: Thank, perfect, thank you. [00:26:36] Marc Monday: Maybe I’ll close with with one really simple use case just for all of us that are in the partner profession and we work in alliances or partner management. The easiest and best, most effective use case for us as power users today is a shared business plan. Here are the goals and objectives of us as a vendor or a platform provider. [00:26:57] Marc Monday: Here are the goals and objectives of us as the implementer or a resell partner. Um, and in the past I used to describe this as a really complicated bow tie. On one side, you’d have our goals, and on the other side you’d have the, the, the implementer’s goals. And you’d spend all this time weaving together a knot and try to tie it together. [00:27:16] Marc Monday: That activity can happen in about five seconds with the right prompt. And you can very quickly say, oh, you guys think about a CV. We think about a RR Oh, your fiscal year is, is offset. Your fiscal year isn’t, oh, you call this product something different. Um, we care about platform revenue. We care about services revenue. [00:27:35] Marc Monday: You can reconcile that into a pretty darn good shared scorecard and business plan in a matter of seconds. Yeah, and that is a huge time saver. I [00:27:45] Vince Menzione: love that. [00:27:47] Ashleigh Vogstad: It’s just an ama uh, it just thumbs up for me because that joint business planning just doesn’t happen enough. I, I’m in some of the biggest alliances on, on the planet really, and it’s shocking to me how little joint business planning is done. [00:28:00] Ashleigh Vogstad: And for the marketing question, Shannon, like how can marketers lean in? I mean, market development funds are made available based on things like joint business plugs. [00:28:09] Vince Menzione: That’s right. Yeah, really great point. Great voice. Thank you so much. So good to have you finally have you here. Thank you, mark and Ash. [00:28:17] Vince Menzione: Thank you so much [00:28:18] Audience Question: Owens. [00:28:19] Vince Menzione: Don’t forget, ultimate Partner Live is coming soon, May 11th through the 13th in beautiful Bellevue, Washington. I hope to see you there.

Join Kieran Devlin from UC Today as he sits down with Chris Stapenhurst, Director of Product Management at Arctera. If you are navigating the complexities of modern compliance and data governance, this conversation highlights exactly why the "build it yourself" model is becoming obsolete.As data volumes explode and regulations tighten ahead of 2027, the traditional on-premise data center is struggling to keep up. Chris Stapenhurst explains that AI is incredibly power-hungry - noting that large tech companies are even purchasing nuclear power stations to fuel data centers - and most firms simply cannot manage that level of hardware overhead on their own.In this insightful discussion, Kieran and Chris break down the critical role of elasticity in the AI era. They discuss why "building for tomorrow" often leads to wasted budget on unused capacity, and how SaaS models offer a smarter alternative.Key discussion points include:The Power of Elasticity: Understanding how cloud infrastructure allows organizations to "burst" up resources to meet immediate AI processing demands and shrink back down instantly, ensuring you only pay for what you use.Data Governance & Hygiene: How Arctera aggregates and normalizes content from disparate sources (whether on-prem or cloud) to create the clean, accessible data foundation required for accurate AI insights.Agility Through SaaS: The compliance advantage of a SaaS model, which delivers seamless quarterly updates to address new regulations without the costly downtime and IT resource drain associated with upgrading on-premise systems.Next Steps:Are your infrastructure and compliance strategies ready for the next wave of AI regulations? Visit the Arctera website to learn more about their cloud-native solutions.

Key Takeaways Herain Oberoi, Microsoft's general manager for data security, privacy, and compliance, recently held a session where he outlined top security challeneges within the AI era. Specifically, Oberoi outlined three concerns enterprises must address to build secure, scalable AI operations. He stressed strict access controls and disciplined data hygiene to prevent oversharing and sensitive data leakage. Second, regulatory compliance now requires continuous auditability of AI agent operations, with Microsoft Purview Compliance Manager enabling on-demand proof of control. Finally, fragmented solutions increase cost and complexity, while expanded Purview unifies data security, governance, and compliance in a single pane of glass. Enterprises that quickly adapt to rising security expectations will be best positioned to scale AE operations and realize the full value of the AE era. Visit Cloud Wars for more.

The episode centers on D&H's strategic approach to vendor selection, AI program development, and partner enablement within the evolving landscape for MSPs and IT solution providers. Colin Blair, Executive Vice President for cybersecurity at D&H, details a governance-driven process for curating vendor relationships, with emphasis on aligning with Gartner quadrant leaders, peer insight metrics, and channel-partner readiness. D&H's focus remains on SMB and mid-market segments where complexity is increasing, especially around compliance, data governance, and cybersecurity. Supporting this curated model, Colin Blair notes that D&H maintains onboarding rigor but rarely offboards vendors within its advanced solutions group, citing ongoing hyper-growth and the need to continuously add value for partners. The vendor evaluation emphasizes data-driven benchmarks and sustained relationship-building at industry events. The company is prioritizing supply chain strength for MSPs, driven by measurable factors such as profitability, cultural compatibility, and proven channel strategies. The conversation also highlights the expansion of the Go Big AI program, which aims to increase AI literacy among both partners and end customers. Training initiatives reached over 5,000 partners, focusing on foundational applications like Microsoft Copilot and AI PCs, while acknowledging that project success is heavily dependent on data quality and governance. Use cases where implementations see traction are typically well-defined, such as Vision AI for video analytics in healthcare and security verticals. The need for tailored, consultative conversations is cited as significant, as end customers and partners often lack clarity on automation priorities or AI readiness. The implications for MSPs and IT leaders are pragmatic: sustainable advantage is less about technology adoption and more about managing operational complexity, ensuring data governance, and enhancing cybersecurity postures. Decision-makers are cautioned to assess both the maturity and applicability of AI solutions, invest in targeted literacy and consultation, and anchor their vendor relationships in measurable business value. The focus should be on careful risk management, transparent partnership evaluation, and supporting clients through consultative, outcome-driven initiatives rather than broad or speculative technology bets.

How should life science companies govern their data to meet increasingly structured regulatory submission requirements and actually get value from AI? Cary Smithson shares lessons from decades of helping organizations modernize their regulatory, quality, and R&D operations.Cary discusses why data governance has become urgent across three fronts — structured submissions, cross-functional interoperability, and AI reliability — and walks through the foundational steps companies should take, the organizational challenges they'll hit, and what measurable results look like when governance is done right.A few of Cary's key takeaways:Regulatory submissions are no longer just documents — they're structured data that demands consistent master data, controlled vocabularies, and traceable lineageStart with scope and pain points, not a boil-the-ocean exercise — pilot governance in one or two high-value use cases, then scaleData ownership belongs in the business, not IT — IT facilitates, but stewards and business owners should be accountable for their dataTools support governance but don't replace it — get the people and process foundation right before selecting platformsAI reliability depends on governed data — without standardized inputs and clear provenance, models produce unreliable or unexplainable outputsTie governance to business outcomes people are already measured on — submission cycle time, audit readiness, right-first-time metrics — or compliance won't stickAbout Cary SmithsonCary Smithson is Managing Partner and Owner of LeapAhead Solutions, Inc., where she leads a consulting practice focused on IT strategy, data governance, and business process consulting for life sciences. She leads the DIA RIM Working Group and the DIA RIM Intelligent Automation Team and co-authored the DIA RIM eBook. With experience spanning large consulting firms (Grant Thornton, PharmaLex), enterprise technology organizations (OpenText), and her own practice, Cary has served clients including Regeneron, Bristol-Myers Squibb, Johnson & Johnson, Daiichi Sankyo, Bayer, and BeiGene. She is a recognized thought leader who regularly presents at industry conferences on regulatory information management, intelligent automation, and AI adoption in life sciences.About The FDA GroupThe FDA Group helps life science organizations rapidly access the industry's best consultants, contractors, and candidates. Our resources assist in every stage of the product lifecycle — from clinical development to commercialization — with a focus on staff augmentation, auditing, remediation, QMS, and other specialized project work in Quality Assurance, Regulatory Affairs, and Clinical Operations. Learn more: https://www.thefdagroup.com/

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 is the lifeblood of the modern economy - powering decision-making, daily life, and the AI revolution. But governing data is full of trade-offs: openness and trust vs privacy and regulation; cross-border sharing vs sovereignty; and timeliness vs quality. In this episode of Behind the Numbers, host Ashley Ward is joined by OECD Chief Statistician Steve MacFeely to explore why data governance has become a core policy challenge - and what it means for the future of official statistics and AI. We follow Steve's journey from Cork to Geneva to Paris; unpack his thoughts on metadata; and find out where he thinks the international statistical community should double down – or back off – over the coming decade. Host: Ashley Ward, Director's Office Advisor and Communications Manager (OECD Statistics and Data Directorate) Guest: Steve MacFeely, OECD Chief Statistician (OECD Statistics and Data Directorate) To learn more about the OECD, our global reach, and how to join us, go to www.oecd.org/en/about.html To keep up with latest at the OECD, visit www.oecd.org/ Get the latest OECD content delivered directly to your inbox! Subscribe to our newsletters: www.oecd.org/en/about/newsletters.html

Most companies don't realize it yet, but the way they built their technology foundations is quietly becoming a liability.Cloud costs are rising. Platforms change underneath you. AI is reshaping infrastructure from hardware to data to governance. And the strategies that once felt “safe” are now the ones creating the most risk.In this episode of IT Visionaries, host Chris Brandt sits down with Mano Bhattacharya, CTO of Nutanix, to unpack what's really happening inside enterprise technology right now. This isn't a conversation about chasing the newest tools or betting on a single future. It's about why adaptability has become the most important design principle in modern tech.Mano explains why many organizations are rethinking long-held assumptions about virtualization, cloud, and containers, and why the smartest teams are building infrastructure that gives them options over the next three to five years. They explore how AI changes the entire stack, not just applications, why data has become the real bottleneck, and why moving fast without a coherent plan can be more dangerous than moving slowly. Chapters:00:00 - The VMware Exodus Wave is Coming03:34 - VMware Broadcom Acquisition: What Changed and Why It Matters05:56 - Three Migration Paths: Stay, Move to Cloud, or Modernize09:59 - Why Containers on VMs Make Sense for Most Enterprises15:40 - The Five Stages of VMware Migration Grief21:20 - VMware Admin to Nutanix Admin: Closing the Skills Gap24:14 - The Cloud-in-a-Box Philosophy: From Boxes to Software32:30 - Opening Up the Platform: Pure Storage and Third-Party Integrations40:54 - AI Infrastructure: The End-to-End Challenge48:01 - Enterprise AI Strategy: Use Cases, Economics, and Governance56:44 - What's Next: Building the Invisible Platform for AI  -- 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.

In this episode, we dive deep into the challenges and opportunities of HR data governance, exploring how organizations can improve data quality, ownership, and usability in a rapidly evolving AI landscape. Join us for practical insights from seasoned HR analytics experts on building a data-driven culture that supports strategic decision-making. Key Topics: Why HR data is often unreliable and the impact on decision-making The role of ROI and cultural mindset in improving HR data quality The importance of ownership, stewardship, and clear definitions in data governance How AI and machine learning magnify data quality issues if governance is lacking Practical steps to start building your HR data governance framework The critical role of documentation, data catalogs, and system integration Common pitfalls: managing multi-system data consistency and avoiding errors Quick wins: focusing on key metrics and stakeholder collaboration Timestamps: 00:00 - Introduction: Why HR data governance matters today 02:30 - Challenges HR faces with data quality and accuracy 06:15 - Why organizations struggle to demonstrate ROI from HR data 09:00 - Cultural and mindset barriers to effective data management 11:00 - The impact of AI and machine learning on HR data quality 12:30 - Context and system integration challenges across HR tech stack 15:11 - Defining HR data governance: Ownership, stewardship, and quality 17:00 - Creating a data glossary and system of record for HR data 19:05 - Real-world examples of poor HR data visibility and audit issues 21:00 - Using chatbots and AI: risks, benefits, and data consistency 24:00 - The importance of documentation and version control in AI applications 27:40 - Practical steps to start your HR data governance journey 30:00 - The significance of aligning metrics and defining owners 33:00 - Building a culture of data excellence and quick wins 36:00 - Addressing expectations for pristine data and managing realities 37:00 - Final recommendations for HR leaders to improve data governance Connect with Guests: Raswinder Singh - LinkedIn | Twitter Ankit Abrol - LinkedIn | Twitter

Episode 43: "For the Love of" Our WaterwaysAs we know, love heals. When we love and receive love, every cell in our bodies benefit. We are celebrating all of the things we are grateful for and love. Mother Earth offers us abundance and all the things we need to be well. Today, we are celebrating our waterways. Water is life. Over the past several years we have prioritized profit over the wellness of our waterways. Today, we are going to talk to Keyana Pardilla, a water warrior, an indigenous scholar and water protector. Keyana will share her research and passion for respecting and protecting our waterways. Please lean in with us as we deepen our understanding and our connections to water.Wabanaki Words Used:Apc-oc (again in the future, parting, good-bye, farewell) - https://pmportal.org/dictionary/apc-oc Topics Discussed:  Museum of Beadwork - https://www.museumofbeadwork.org/Indian Island - https://www.penobscotnation.org/Sipayak - https://wabanaki.com/about-us/University of Maine - https://umaine.edu/Project Venture - https://wabanakiphw.org/departments/wabanaki-public-health/our-next-generation/experiential-learning-programs/project-venture/PFAS - https://erefdn.org/pfas/Bigelow Labs - https://www.bigelow.org/Coastal Maine Botanical Gardens - https://www.mainegardens.org/Data Governance - https://en.wikipedia.org/wiki/Data_governanceWaYS - https://www.wabanakiyouthinscience.org/Darren Ranco - https://en.wikipedia.org/wiki/Darren_RancoTony Sutton - https://umaine.edu/mitchellcenter/people/anthony-sutton/Antiques Roadshow - https://www.pbs.org/wgbh/roadshow/events/2025/boothbay-me/ Wabanaki Tribal Nations:Houlton Band of Maliseet Houlton Band of Maliseet Indians | Littleton, ME (maliseets.net)Mi'kmaq Mi'kmaq Nation | Presque Isle, ME (micmac-nsn.gov)Passamaquoddy Tribe Indian Township Passamaquoddy Tribe @ Indian Township | Peskotomuhkati MotahkomikukPassamaquoddy Tribe Sipayik Sipayik Tribal Government – Sipayik (wabanaki.com)Penobscot Nation Penobscot Nation | Departments & Info | Indian Island, Maine Special Thanks/Woliwon: Guest: Keyana PardillaProducer: Gavin AllenPodcast Team: Becky Soctomah Bailey, Macy Flanders

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

Ready to get started with Purview? Richard chats with Erica Toelle about the first steps you can take to harness the power of Purview in your organization. Erica explains that Purview is an umbrella product that covers several infosec technologies, including information rights management, data loss prevention, structured data governance, and more. When preparing for M365 Copilot, you want to start tagging sensitive information in your organization, and Purview can help by using LLMs to identify potentially sensitive content. You can also monitor how data is used, the types of prompts sent to M365 Copilot, and more. This can help you bootstrap M365 Copilot by using Purview to see which data Copilot uses, and then tune the access rules for that data. "Getting your data estate in order" is not a destination; it's a journey, and Purview can give you a map!LinksMicrosoft PurviewData Security Posture ManagementData Governance with Microsoft PurviewConditional Access with Microsoft PurviewSharePoint Advanced ManagementMicrosoft 365 ArchiveEndpoint Data Loss PreventionPurview Browser ExtensionRecorded January 7, 2026

In this episode of the Crazy Wisdom podcast, host Stewart Alsop welcomes Roni Burd, a data and AI executive with extensive experience at Amazon and Microsoft, for a deep dive into the evolving landscape of data management and artificial intelligence in enterprise environments. Their conversation explores the longstanding challenges organizations face with knowledge management and data architecture, from the traditional bronze-silver-gold data processing pipeline to how AI agents are revolutionizing how people interact with organizational data without needing SQL or Python expertise. Burd shares insights on the economics of AI implementation at scale, the debate between one-size-fits-all models versus specialized fine-tuned solutions, and the technical constraints that prevent companies like Apple from upgrading services like Siri to modern LLM capabilities, while discussing the future of inference optimization and the hundreds-of-millions-of-dollars cost barrier that makes architectural experimentation in AI uniquely expensive compared to other industries.Timestamps00:00 Introduction to Data and AI Challenges03:08 The Evolution of Data Management05:54 Understanding Data Quality and Metadata08:57 The Role of AI in Data Cleaning11:50 Knowledge Management in Large Organizations14:55 The Future of AI and LLMs17:59 Economics of AI Implementation29:14 The Importance of LLMs for Major Tech Companies32:00 Open Source: Opportunities and Challenges35:19 The Future of AI Inference and Hardware43:24 Optimizing Inference: The Next Frontier49:23 The Commercial Viability of AI ModelsKey Insights1. Data Architecture Evolution: The industry has evolved through bronze-silver-gold data layers, where bronze is raw data, silver is cleaned/processed data, and gold is business-ready datasets. However, this creates bottlenecks as stakeholders lose access to original data during the cleaning process, making metadata and data cataloging increasingly critical for organizations.2. AI Democratizing Data Access: LLMs are breaking down technical barriers by allowing business users to query data in plain English without needing SQL, Python, or dashboarding skills. This represents a fundamental shift from requiring intermediaries to direct stakeholder access, though the full implications remain speculative.3. Economics Drive AI Architecture Decisions: Token costs and latency requirements are major factors determining AI implementation. Companies like Meta likely need their own models because paying per-token for billions of social media interactions would be economically unfeasible, driving the need for self-hosted solutions.4. One Model Won't Rule Them All: Despite initial hopes for universal models, the reality points toward specialized models for different use cases. This is driven by economics (smaller models for simple tasks), performance requirements (millisecond response times), and industry-specific needs (medical, military terminology).5. Inference is the Commercial Battleground: The majority of commercial AI value lies in inference rather than training. Current GPUs, while specialized for graphics and matrix operations, may still be too general for optimal inference performance, creating opportunities for even more specialized hardware.6. Open Source vs Open Weights Distinction: True open source in AI means access to architecture for debugging and modification, while "open weights" enables fine-tuning and customization. This distinction is crucial for enterprise adoption, as open weights provide the flexibility companies need without starting from scratch.7. Architecture Innovation Faces Expensive Testing Loops: Unlike database optimization where query plans can be easily modified, testing new AI architectures requires expensive retraining cycles costing hundreds of millions of dollars. This creates a potential innovation bottleneck, similar to aerospace industries where testing new designs is prohibitively expensive.

Summary In this episode, Andy talks with Olivia Montgomery, Associate Principal Analyst at Capterra and a PMP. They discuss how artificial intelligence is reshaping project management tools, skills, and expectations. Olivia brings a rare perspective, combining hands-on experience leading a PMO with years of research into how organizations evaluate, adopt, and struggle with project management software. Olivia and Andy explore why buying AI-powered tools is often easy, but realizing real value from them is much harder. Olivia explains the shift from buying software based on seat count to buying based on capability, why security is both the top source of satisfaction and frustration, and how unclear success metrics can quietly derail adoption. They also dig into the hidden risks of delegating too much to AI, including data governance blind spots and misplaced trust in tools that feel intuitive but have real limitations. You'll also hear why emotional intelligence is becoming more important as technology advances, how PMs can stress-test AI tools before committing, and which skills will separate the next generation of project leaders from the rest. If you're trying to prepare for the future of AI, tools, and skills in project management, this episode is for you! Sound Bites "Buying tools is very easy. Realizing the value is extremely difficult." "Security is not IT's job. It's the whole company's job." "If your main metric is just 'use AI,' that's a red flag." "AI is very good at predicting what is most likely to happen next, and terrible at predicting black swan events." "Emotional intelligence is what helps you move forward when technology can't." "Use AI to generate a first draft. That's the safest place to start." "If you don't know the topic well yourself, you won't spot when AI gets it wrong." "Confidence in AI can grow faster than readiness, and that's where problems start." "AI can flag a risk, but it cannot tell you why people are stuck." "Data governance is going to set project managers apart in the future." "No matter what job you have in ten years, emotional intelligence will still matter." Chapters 00:00 Introduction 02:00 Start of Interview 02:10 Olivia's Role and Career Path 06:53 Shifts in How Organizations Choose PM Software 08:23 The Security Satisfaction and Frustration Paradox 11:25 Why AI Tools Are Easy to Buy but Hard to Use Well 20:18 Warning Signs of Overconfidence in AI 24:03 How to Stress-Test AI Tools Before Buying 27:50 Why Emotional Intelligence Matters More with AI 34:28 The Future of Project Management Software 40:08 Skills That Will Define the Next Generation of PMs 45:20 Where to Follow Olivia's Work 46:20 End of Interview 46:40 Andy Comments After the Interview 49:15 Outtakes Learn More You can follow Olivia Montgomery and her research on LinkedIn at linkedin.com/in/olivia-montgomery. For more learning on this topic, check out: Episode 479 with Matt Mong, about the AI skills you need to stay relevant in the years ahead Episode 463 with Faisal Hoque, on how to transcend the fear and hype around AI Episode 384 with PMeLa, the first-ever interview with an AI on a leadership or project management podcast Level Up Your AI Skills Join other listeners from around the world who are taking our AI Made Simple course to prepare for an AI-infused future. Just go to ai.PeopleAndProjectsPodcast.com. Thanks! Pass the PMP Exam If you or someone you know is thinking about getting PMP certified, we've put together a helpful guide called The 5 Best Resources to Help You Pass the PMP Exam on Your First Try. We've helped thousands of people earn their certification, and we'd love to help you too. It's totally free, and it's a great way to get a head start. Just go to 5BestResources.PeopleAndProjectsPodcast.com to grab your copy. I'd love to help you get your PMP this year! Join Us for LEAD52 I know you want to be a more confident leader, that's why you listen to this podcast. LEAD52 is a global community of people like you who are committed to transforming their ability to lead and deliver. It's 52 weeks of leadership learning, delivered right to your inbox, taking less than 5 minutes a week. And it's all for free. Learn more and sign up at GetLEAD52.com. Thanks! Thank you for joining me for this episode of The People and Projects Podcast! Talent Triangle: Business Acumen Topics: Artificial Intelligence, Project Management Software, Project Management, Business Acumen, Data Governance, Security, Emotional Intelligence, AI Adoption, Future Of Work, Leadership Skills, Technology Strategy The following music was used for this episode: Music: Echo by Alexander Nakarada License (CC BY 4.0): https://filmmusic.io/standard-license Music: Tuesday by Sascha Ende License (CC BY 4.0): https://filmmusic.io/standard-license

Rob Hughes — CISO at RSA and Champion of a Passwordless FutureNo Password Required Season 7:  Episode 1 - Rob HughesRob Hughes, the CISO at RSA, has more than 25 years of experience leading security and cloud infrastructure teams. In this episode, he reflects on his unconventional career path, from co-founding the original Geek.com and serving as its Chief Technologist during the early days of the internet, to leading security and systems design at Philips Home Monitoring.Jack Clabby of Carlton Fields, P.A. and Kayley Melton welcome Rob for a wide-ranging conversation on identity, leadership, and the realities of modern cybersecurity. Rob currently leads RSA's Security and Risk Office, overseeing cybersecurity, information security governance, and risk across both RSA's products and corporate environment.Rob explains his dream for a passwordless future. He unpacks why passwords remain one of the largest sources of cyber risk, how real-world incidents and password-spraying attacks have accelerated change, and why phishing-resistant technologies like passkeys may finally be reaching a tipping point.  The episode wraps with the Lifestyle Polygraph, where Rob lightens the conversation with stories about gaming with his kids, underrated horror films, and classic cars.Follow Rob on LinkedIn: https://www.linkedin.com/in/robert-hughes-816067a4/Chapters: 00:00 Introduction to No Password Required01:43 Meet Rob Hughes, CISO at RSA02:05 The Role of a CISO in a Security Company05:09 Transitioning to the CISO Role08:00 The Early Days of Geek.com12:14 Launching a Startup During the Dot Com Boom14:30 The Push for a Passwordless Future18:21 Tipping Point for Passwordless Adoption20:20 Ongoing Learning in Cybersecurity26:09 Managing Stress in High-Pressure Environments33:46 The Lifestyle Polygraph Begins34:15 Career Insights in Cybersecurity36:08 Dream Cars and Personal Preferences39:58 Underrated Horror Films41:19 Creating a Cybersecurity Monster

AI systems are moving fast, sometimes faster than the guardrails meant to contain them. In this episode of Security Matters, host David Puner digs into the hidden risks inside modern AI models with Pamela K. Isom, exploring the governance gaps that allow agents to make decisions, recommendations, and even commitments far beyond their intended authority.Isom, former director of AI and technology at the U.S. Department of Energy (DOE) and now founder and CEO of IsAdvice & Consulting, explains why AI red teaming must extend beyond cybersecurity, how to stress test AI governance before something breaks, and why human oversight, escalation paths, and clear limits are essential for responsible AI.The conversation examines real-world examples of AI drift, unintended or unethical model behavior, data lineage failures, procurement and vendor blind spots, and the rising need for scalable AI governance, AI security, responsible AI practices, and enterprise red teaming as organizations adopt generative AI.Whether you work in cybersecurity, identity security, AI development, or technology leadership, this episode offers practical insights for managing AI risk and building systems that stay aligned, accountable, and trustworthy.

Algorithms and automations have been buds for a decade plus.