Podcasts about Data governance

Mike & Tommy tackle whether AI agents can solve what the Power BI admin portal can't—giving you a true tenant-level, workspace-free view of who can access what in Fabric. They break down Taylor B.'s real-world agent that stitches together audit logs, model APIs, grants, direct links, and app audiences into a central governance store, and weigh in on whether Microsoft's native governance story is ready or still a DIY puzzle.From Purview's role to the risks of letting agents reason over permissions, they explore what it would take to make data governance in Fabric actually feel complete—and what builders like Taylor should ship now versus wait for.Mailbag question from Taylor B.: OneLake Architectural Guidance | Fabric Task Flow Studio | Chicagoland Power BI MeetupGet in touch:Send in your questions or topics you want us to discuss by tweeting to @PowerBITips with the hashtag #empMailbag or submit on the PowerBI.tips Podcast Page.Visit PowerBI.tips: https://powerbi.tips/Watch the episodes live every Tuesday and Thursday morning at 730am CST on YouTube: https://www.youtube.com/powerbitipsSubscribe on Spotify: https://open.spotify.com/show/230fp78XmHHRXTiYICRLVvSubscribe on Apple: https://podcasts.apple.com/us/podcast/explicit-measures-podcast/id1568944083‎Check Out Community Jam: https://jam.powerbi.tipsFollow Mike: https://www.linkedin.com/in/michaelcarlo/Follow Tommy: https://www.linkedin.com/in/tommypuglia/

Not all work happens in writing. Teams that work with photos, videos, and audio need AI that works for them too. This is why, with Dropbox, you can search within multimedia content for key moments and important information—not just text. In this episode, we talk with Appu Shaji and Hicham Badri, two Dropbox machine learning engineers who are part of the team that makes all of this possible. They explain how multimodal search works—from understanding the context of the initial query, to identifying objects and actions in complex scenes—and how they ensure those models work fast, even at Dropbox-scale. ~ ~ ~  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!

In this episode of The Voice of Retail podcast, host Michael LeBlanc sits down with one of retail technology's most accomplished leaders: Julie Averill, former Chief Information Officer of Lululemon and REI, and author of the new book Chief Impact Officer: Real Transformation Comes from Human, Not Just Artificial Intelligence. Joining the conversation is Menachem Salinas, Co-Founder and Chief Revenue Officer of Nimble, the expert AI web search platform making live web data enterprise-grade. A self-described "serial retail technologist," Julie's career spans the defining chapters of modern retail technology. She spent a decade at Nordstrom leading pioneering omnichannel initiatives, drove a technology transformation as CIO of REI, and then took a leap to a then-$2 billion company called Lululemon — where, over eight years as CIO, she helped power the brand's growth to $10 billion. Today, she runs her own advisory business focused on enterprise AI adoption, and her new book makes the case that real transformation comes from people, not just technology. Drawing on years of parsing signal from noise in the CIO chair, Julie delivers a masterclass in AI reality-checking. Her verdict on where retail stands in the AI journey? "Maybe the first inning." She argues the technology itself is now the easy part — the real reasons so many AI pilots fail are data quality, governance, workflow readiness and organizational capability. Julie shares the exact questions she asks to separate a slick demo from a solution built to survive contact with a real omnichannel operation: Whose data does this run on? What workflow actually changes, and for whom? Who owns the errors? What does this cost at scale? And she tells a cautionary tale of a board-driven AI pitch that promised everything — and why "yes, we can do everything" is the surest sign of an immature vendor. Julie also maps the future of the CIO role itself: from technology gatekeeper to curious, strategic business enabler. With AI tools now arriving through the browser, control is gone — the new job is curation, education and helping executives tell what's real. The businesses that win, she argues, will be the ones that learn to evolve continuously and dream about what was previously impossible. Menachem Salinas adds the vendor-side perspective, explaining how Nimble delivers real-time competitive pricing, digital shelf monitoring and out-of-stock intelligence across thousands of retail sites — and why he believes agentic commerce will transform e-commerce within twelve months. His candid ShopTalk Barcelona assessment: even the world's biggest brands rate no better than five out of ten on AI data readiness. Julie's book Chief Impact Officer is available now everywhere books are sold. Learn more at julieaverill.com and nimbleway.com. Michael LeBlanc is the president and founder of M.E. LeBlanc & Company Inc, a senior retail advisor, keynote speaker and now, media entrepreneur. He has been on the front lines of retail industry change for his entire career. Michael has delivered keynotes, hosted fire-side discussions and participated worldwide in thought leadership panels. He brings 25+ years of brand/retail/marketing & eCommerce leadership experience with Levi's, Black & Decker, Hudson's Bay, CanWest Media, Pandora Jewellery, The Shopping Channel and Retail Council of Canada to his advisory, speaking and media practice.Michael produces and hosts a network of leading retail trade podcasts, including the award-winning No.1 independent retail industry podcast in America, Remarkable Retail with his partner, Dallas-based best-selling author Steve Dennis; Canada's top retail industry podcast The Voice of Retail and Canada's top food industry and one of the top Canadian-produced management independent podcasts in the country, The Food Professor with Dr. Sylvain Charlebois from Dalhousie University in Halifax.Rethink Retail has recognized Michael as one of the top global retail experts for the fifth year in a row, the National Retail Federation has designated Michael as on their Top Retail Voices for 2025 and 2026. Thinkers 360 has named him on of the Top 50 global thought leaders in retail. If you are a BBQ fan, you can tune into Michael's cooking show, Last Request BBQ, on YouTube, Instagram, X and yes, TikTok.Michael is available for keynote presentations helping retailers, brands and retail industry insiders explaining the current state and future of the retail industry in North America and around the world.

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

Was ist ein Datenprodukt – und warum scheitern so viele daran? Ales Zeman ist seit über 25 Jahren bei Quest und bringt die Vendor-Sicht auf Trusted Data Products mit. Mit einer Lego-Analogie, einem ehrlichen Versicherungs-Case (350 Excel-Sheets, 6 Monate) und einem Trust Score aus 9 Kriterien zeigt er, wie KI die Erstellung von Monaten auf Tage verkürzt – wenn das Fundament stimmt. In dieser Folge erfährst du: → Warum ohne Business-Intent jedes Datenprodukt scheitert → Die Lego-Analogie: Ziel, Bausteine, Anleitung, Marketplace → Wie KI die Erstellung von Datenprodukten von Monaten auf Tage verkürzt → Wie ein Trust Score aus 9 gewichteten Kriterien Vertrauen messbar macht → Warum über 90% der KI-Projekte scheitern – meistens an den Daten davor Über den Gast: Ales Zeman ist seit über 25 Jahren bei Quest und seit mehr als 30 Jahren im IT-Umfeld tätig. Sein Studienschwerpunkt war Künstliche Intelligenz. Heute beschäftigt er sich mit Trusted Data Products und Datenmanagement-Plattformen. MY DATA IS BETTER THAN YOURS ist ein Projekt von BETTER THAN YOURS, der Marke für richtig gute Podcasts.

AI has made software easier than ever to build. That does not mean every internal tool should become the backbone of your business. In this episode, Bryan sits down with Chris Guthrie to break down the growing temptation inside cannabis companies to vibe code internal tools, dashboards, CRMs, ERPs, and operational systems in the name of saving money. The problem is not the first version. The problem is what happens two months later, when a field does not map, the workflow breaks, the person who built it is unavailable, and a production team is now relying on code nobody fully understands. Chris explains the difference between a useful AI-built prototype and an enterprise-grade system that can survive real operators, messy data, security needs, compliance, multiple departments, M&A diligence, and future FDA-style requirements. This conversation covers why descriptive data does not create predictive insight, why Google Sheets to vibe coding can become a path to failure, and why the real AI opportunity starts after the business has clean data, clean processes, and a system strong enough to trust. This episode covers: The weekend build that fails Descriptive data will not predict Clean books before M&A Chapters   00:00 The Rise of AI in Software Development 03:07 Challenges of Internal Software Development 06:09 Understanding What 'Works' in Software 09:10 The Risks of Vibe Coding 12:05 The Importance of Established Infrastructure 14:58 Cost Considerations in Software Development 17:56 Predictive Insights and Data Utilization 21:13 Defining Core Business Focus 23:17 The Challenges of AI Implementation 27:00 Understanding AI's Limitations 28:58 The Importance of Data Governance 31:11 Navigating Change Management in Cannabis 35:51 Preparing for Industry Evolution 40:09 The ROI of ERP Systems Our Links: Bryan Fields on Twitter The Dime on Twitter Extraction Teams: Want to cut costs and get more out of every run? Unlock hidden revenue by extracting more from the same input—with Newton Insights. At Eighth Revolution (8th Rev), we provide services from capital to cannabinoid and everything in between in the cannabinoid industry. The Dime is a top 5% most shared  global podcast The Dime is a top 10 Cannabis Podcast  The Dime has a New Website. Shhhh its not finished.

Everyone is talking about AI governance. Almost nobody is talking about the part that actually decides whether it works. I had a blast chatting with Gaurav Bhandari, AVP and Head of Data and Analytics consulting at Infosys, on The Ravit Show at Data Citizens on the Road by Collibra. One line stuck with me. Roughly 80% of AI governance is just governing the data that feeds your models. We have been here before. Data governance started as a compliance and privacy problem in regulated industries. Then data became the asset everyone wanted to mine for value. Now AI has raised the stakes again, because a model is only as good as the context behind it.Gaurav broke that context down into five things every enterprise has to get right:- Trust. Can you rely on the output.- Ethics. Even when you trust it, is it the right answer to put in front of people.- Regulations. Are you staying compliant as the rules keep shifting.- Privacy. Do people still control their own data.- Security. Is everything safe once it sits inside your workflow.Miss one of these and your AI agents are running on shaky ground.What stood out to me was how the Infosys and Collibra partnership fits this moment. Ten plus years working together, and not just in finance. Retail, manufacturing, life sciences too. Collibra brings the platform. Infosys weaves the policies, controls, and structure into one governance story instead of a pile of disconnected tools.His advice for the next 12 months was refreshingly simple. Stop thinking about data governance. Start building data plus AI governance.The companies that treat these as one problem will move faster than the ones still treating them as two.Full interview is live now.Follow The Ravit Show for more conversations from across the Data and AI world, and subscribe to the newsletter to stay ahead.#data #ai #collibra #governance #infosys #api #datacitizen #theravitshow

Les agents IA vont-ils vraiment transformer nos organisations ? Productivité démultipliée, automatisation avancée, réinvention des processus… Les promesses sont nombreuses. Mais qu'en est-il des résultats concrets ?Tarik Boukherissa, Lead Solution Architect chez Databricks, apporte, dans cet épisode de Trajectoires, une grille de lecture sur les agents IA pour distinguer ce qui relève du discours de ce qui transforme vraiment le fonctionnement des organisations.Il partage des exemples concrets issus de projets récents et explique pourquoi le vrai défi n'est pas l'intelligence des agents, mais leur capacité à accéder au bon contexte au bon moment. Un échange qui aborde aussi la question de l'équilibre entre automatisation et contrôle humain, et ce que les prochaines années pourraient changer à une échelle encore difficile à anticiper.

What if the biggest obstacle to successful AI adoption isn't the technology at all, but the state of your data and the way work gets done inside your business? In this episode, I speak with Jim Spignardo, Director of Cloud Strategy & AI Enablement at ProArch, about why so many AI initiatives struggle to deliver lasting value and what business leaders should be doing before deploying the next AI tool. After more than 25 years working across networking, cloud, cybersecurity, and enterprise technology, Jim has seen plenty of technology trends come and go. His perspective on AI is refreshingly grounded in experience rather than headlines. Instead of focusing on the latest models or features, he explains why data governance, business processes, and user adoption remain the biggest factors in determining whether AI succeeds or fails. One of the topics that stood out for me was Jim's definition of AI enablement. Rather than viewing AI as another application to deploy, he argues that real value comes from embedding AI into everyday workflows and helping people rethink how work is performed. That means identifying repetitive tasks, improving decision making, and creating measurable outcomes that executives can clearly understand. We also discuss why many businesses are carrying years of technical debt into their AI initiatives. Poor data quality, outdated processes, and unclear ownership can all limit the effectiveness of AI, regardless of how advanced the underlying technology may be. Jim explains why companies that invest time in cleaning and governing their data today will be far better positioned to build reliable AI systems tomorrow. Another fascinating part of our conversation focuses on ProArch's own AI adoption journey with Microsoft 365 Copilot. Rather than attempting a company-wide rollout overnight, Jim describes a phased approach built around real use cases, structured training, internal champions, and measurable success. It's a practical roadmap that many technology leaders could adapt inside their own businesses. We also tackle one of the biggest concerns surrounding AI: jobs. Jim believes AI should be viewed as a way to augment people rather than replace them, allowing employees to spend less time on repetitive administrative work and more time applying creativity, expertise, and critical thinking where it delivers the greatest business value. If you're responsible for technology strategy, cloud transformation, or AI adoption, this conversation offers practical advice on avoiding common mistakes while building a stronger foundation for long-term success. How prepared is your business for enterprise AI, and have you addressed the data, governance, and cultural challenges before expecting AI to deliver measurable results?

Today, we're wrapping up our series of revisiting some of our most-listened-to Unscripted segments of 2025. We're returning to a topic that not everyone thinks about or talks about but it critical in our data-driven industry … data governance. NAMIC CEO Neil Alldredge chatted with Data Governance Institute founder Gwen Thomas. They talk about how and why data governance is everyone's responsibility.Today's episode is sponsored by Holborn. 

In this week's episode of the Compliance Files podcast series, Steven Roberts, Vice Chairperson of the Compliance Institute's Data Protection and Information Security (DP&IS) Working Group speaks with Members of the DP&IS WG, John Magee - Partner at DLA Piper and Global Co-Chair Data, Privacy & Cybersecurity,  Alan Moore – Data Protection Officer and Head of Data Governance at Pobal and  Flavien Corolleur - Senior Legal Counsel and Data Protection Officer at SS&C Technologies on the EU Digital Omnibus. This podcast builds on the recent Compliance Institute webinar on this topic and considers the potential impact of the Omnibus on GDPR and data protection. It also examines the practical steps organisations can take ahead of the Omnibus's introduction.

When AI is at its best, the conversations can feel uncanny—almost magical in their accuracy, relevance, and speed. For that you can thank the AI agents that work together behind the scenes to search, reason, and sift through all your content to get you what you need to do your job. We talk with Jongmin Baek and Marta Mendez, two Dropbox machine learning engineers, about building conversational AI that's helpful, useful, and grounded in your team's shared context, so you can spend more time on the work that really matters. ~ ~ ~  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!

In this episode of AI at Work, Tim Staton sits down with Kate Marshall, founder of the GRAI Fractional Chief AI Officer and author of AI at Work. They explore practical how to use AI at work, effective ways to use AI at work, and answer the common question how can AI help me at work while addressing can I use AI at work responsibly in professional environments.   Kate shares expert guidance on AI at work who runs the office — the critical balance between human leadership and intelligent systems. The conversation dives deep into what is AI and data governance, AI and data governance and privacy OECD frameworks, and the role of the Centre for AI and Data Governance in helping organizations stay compliant. They also unpack what is AI and cybersecurity, AI and machine learning cybersecurity, generative AI and cybersecurity, and tackle the debate AI and cybersecurity which is better when building secure, resilient systems.   The AI Revolution and Human Potential Tim Staton and Kate Marshall discuss a vision where AI augments human potential instead of replacing it. Kate emphasizes shifting the narrative from job loss to exponential human growth by letting AI handle repetitive tasks. This approach reduces fear and helps employees embrace new ways to use AI at work for more creative, high-value outcomes.   Challenges in AI Adoption and Implementation Many organizations struggle to move beyond experimentation. Kate highlights common pitfalls including legacy systems, data integrity issues, and resistance rooted in uncertainty about how can AI help me at work versus the risks involved. She stresses the need for clear strategy rather than simply “grabbing onto AI” without proper planning.   Data Integrity, Governance & Security Data concerns remain a major barrier. Kate strongly recommends enterprise-grade tools over free versions and explains best practices around AI and data governance, privacy regulations like HIPAA, and protecting sensitive information. The discussion covers responsible data handling and why governance must be foundational to any successful AI strategy.   Training, Customization & Human Oversight Kate addresses the problem of generic “AI slop” and shows how proper training and customization lead to outstanding results. She clarifies the difference between automations and adaptive AI agents while reinforcing the importance of keeping humans in control. Listeners gain practical insights on how to use AI at work effectively, including the human-in-the-loop model that prevents errors and ensures alignment with business goals.   Strategic Implementation & Future of Work The episode provides a clear roadmap: start with goals, not tools. Kate's book AI at Work offers a practical checklist-based approach ideal for non-technical leaders. They also explore how organizational structures and roles are evolving, the rise of fractional Chief AI Officers, and how to prepare teams for the significant changes ahead. Whether you're wondering can I use AI at work, seeking proven ways to use AI at work, or need clarity on AI and cybersecurity, this conversation delivers actionable strategies, responsible AI policies, and a balanced view on the future of work.   Key Topics: The importance of AI literacy for organizational leadership Balancing technological innovation with human elements Common pitfalls in AI adoption, including data integrity and governance Practical steps to move from AI experimentation to strategic implementation Developing responsible AI policies that protect sensitive data Human in the loop vs. human out of the loop How to train teams effectively for AI adoption Organizing around clear goals, not just the tools The future of work: preparing for AI integration and workforce transformation   This episode is essential listening for leaders and professionals who want to thrive in the AI era. Subscribe for more expert conversations on how to use AI at work responsibly and effectively.   Connect With Kate Marshall: LinkedIn: https://www.linkedin.com/in/kate-b-marshall/ Website: https://thegr.ai/ Book: https://a.co/d/0dZ15wKd     Connect With Tim Website: timstatingtheobvious.com Facebook: https://www.facebook.com/timstatingtheobvious YouTube: https://www.youtube.com/channel/UCHfDcITKUdniO8R3RP0lvdw Instagram: @TimStating TikTok: @timstatingtheobvious LinkedIn: https://www.linkedin.com/in/tim-staton-04b41a271/ SKOOL Community: https://www.skool.com/timstatingtheobvious-9537/about?ref=de9c7e65d8ba4eeabc1a8eea413c125b

Standard media execution models face steep performance drops due to fragmented campaign tool dashboards and manual button-clicking busywork.In this episode, Dylan Conroy sits down with Dane Kunkel, SVP of Performance & Transformation at Horizon Media, to map out the workflow automation and data security frameworks needed to scale modern agency performance.Strategic themes analyzed include:Transitioning media buying teams from manual entry tasks into strategic Strategy Optimization roles.Establishing strict data governance frameworks to safely onboard external AI tools.Bypassing platform black boxes with specialized multi-channel enterprise strategies.Using low-code natural language interfaces to build personalized dashboard trackers.Lowering overall customer acquisition costs through organic content testing loops.Dane Kunkel is the SVP of Performance & Transformation at Horizon Media, building enterprise controlled AI workflows and cross-platform verification networks to support leading brands.Connect with the Guest & Partners:Connect with Dane Kunkel on LinkedIn: https://www.linkedin.com/in/kunkeldane/Explore Horizon Media's Performance Frameworks: https://www.horizonmedia.com/Optimize Automated Paid Media Scale with Strike Social: https://strikesocial.com/guaranteed-paid-social-media-ads-outcomes/Follow Host Dylan Conroy on LinkedIn: https://www.linkedin.com/in/dylanconroy/How can marketing operations leads ensure consistency of output when teams use low-code AI tools to build custom dashboards?Ensuring consistency requires setting up clear enterprise controls and validation guidelines to verify custom analytics code before deployment. While natural language interfaces let operators build custom tracking views quickly, unmanaged tool creation can introduce data compliance errors. Operations leads should track platform usage metrics and pull successful prototypes back into a centralized database environment, giving teams local operational flexibility while preserving overall brand safety and uniform client reporting.Why does data governance act as the primary rate-limiter for enterprise AI tool onboarding?Data governance limits onboarding speed because connecting multi-channel data systems with external AI models introduces data leaks and compliance risks for proprietary brand metrics.Enterprise organizations process massive amounts of first-party consumer files that cannot be uploaded to public models without violating privacy rules. Overcoming this roadblock requires tech directors to construct isolated database connections and clear governance guardrails, ensuring that internal automation speeds do not compromise data privacy.

Erstes Thema: SearchLeak (CVE-2026-42824). Varonis Threat Labs hat eine dreistufige Angriffskette in Microsoft 365 Copilot Enterprise Search entdeckt: Ein präparierter Microsoft-Link, dessen URL-Parameter Copilot als Prompt interpretiert, kombiniert mit einer HTML-Rendering-Race-Condition und Bings Search-by-Image-Endpunkt als unfreiwilligem Exfiltrationsproxy. Ein Klick reicht – E-Mails, MFA-Codes, Kalendereinträge, alles was der User sehen darf, fließt ab. Microsoft hat server-seitig gepatcht. Das Muster – KI-Assistent wird durch Prompt Injection zur Datenwaffe – ist strukturell: EchoLeak, Reprompt, jetzt SearchLeak, drei Angriffe derselben Klasse.Max bringt einen Blogpost von Google Cloud CISO Chris Betz, der beschreibt, wie Google seine eigene KI intern wie einen Insider behandelt: mit Least Privilege, Monitoring, Auditing und Segmentierung. Was mich interessiert: auch Google musste dafür erst mal sein Asset Management nachziehen und konsolidieren. Die Kernbotschaft bleibt trotzdem richtig – wenn Angreifer mit Machine Speed arbeiten, muss die Verteidigung das auch. Für CISOs bedeutet das: Model Security, Agent Security, Data Governance werden zur Pflicht, nicht zur Kür.Dann Robert über NPM 12: Install Scripts von Dependencies laufen nicht mehr automatisch, bestimmte Remote-Dependencies werden blockiert. Überfällig und sinnvoll – aber 30-40% der NPM-Malware läuft erst beim Import, nicht bei der Installation. Wer einen Maintainer kompromittiert, kommt weiterhin durch. Gute Iteration, kein Allheilmittel.Zum Abschluss: Apple erweitert Private Cloud Compute auf die Google Cloud. Dasselbe Zero-Trust-Prinzip wie bisher – selbst Google soll keinen Zugriff auf verarbeitete Nutzerdaten bekommen. Clevere Partnerschaft statt Frontier-Rennen.SearchLeak / CVE-2026-42824 (Varonis)https://www.varonis.com/blog/searchleakGoogle Cloud CISO Chris Betz: AI Threat Defensehttps://cloud.google.com/blog/products/identity-security/how-google-cloud-is-applying-ai-to-threat-defenseNPM 12 / Risky Business Soapbox mit Paul McCartyhttps://risky.biz/soapbox_npm12Apple Private Cloud Compute auf Google Cloudhttps://security.apple.com/blog/private-cloud-compute-google-cloud

What does it take to build a health organization that will still be running on a strong digital and AI foundation in five years, ten years, or twenty? In this closing conversation, John Henderson, Vice President and Chief Information and Digital Officer at Rady Children's Health, takes the long view. Drawing on his work leading the digital integration of CHOC into Rady Children's Health, launching private generative AI platforms to support clinical and administrative work, and building an AI-ready data infrastructure that reaches beyond the EHR, John shares what it actually takes to align your organization around a multi-year digital vision. John Henderson, VP and CIO, Rady Children's Health Janae Sharp, Founder, The Sharp Index Subscribe to Digital Health Talks on Apple Podcasts, Spotify, YouTube, or wherever you listen.Learn more about HealthIMPACT Live events, virtual forums, and healthcare leader conversations at healthimpactlive.com.Interested in being a guest, sponsoring, or joining the HealthIMPACT community? Visit healthimpactlive.com/digital-health-talks.

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

How do you build AI that actually understands you and the work you do? It all starts with having the right context.  We talk with Dropbox staff product manager Noorain Noorani and principal engineer Sean-Michael Lewis about the art of context engineering and how Dropbox connects to all the tools your team needs for work—so you get AI that works wherever you do.  ~ ~ ~  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!

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

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!

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.

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

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

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.

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.

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

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.