Podcasts about Data governance

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.

Data Governance entsteht nicht durch Regeln allein – sondern durch Pflege, Verantwortung und gemeinsames Verständnis. In dieser Episode sprechen wir mit Kiomi Soledad Spriestersbach über den Aufbau wirksamer Data Governance bei der Lufthansa Group – und warum sich dieser Prozess erstaunlich gut mit Backkunst und Gartenarbeit vergleichen lässt. Aus einer zentralen Perspektive heraus werden viele dezentrale Organisationseinheiten verbunden. Dabei hat sich vor allem eines verändert: das Verständnis von Datenprodukten. Datenqualität, Metadaten und klare Rollen sind heute entscheidend, um Daten nutzbar, vertrauenswürdig und sicher bereitzustellen – insbesondere bei großen Mengen personen- und technikbezogener Daten. Die zentrale Botschaft: Daten sind kein reines IT-Thema. Gute Data Governance entsteht dort, wo Fachlichkeit und Technik gemeinsam Verantwortung übernehmen. Ziel ist nicht Perfektion, sondern angemessene Qualität im jeweiligen Kontext. Unterschiedliche Anwendungsfälle brauchen unterschiedliche Standards – insbesondere mit Blick auf KI als Treiber für hochwertige Daten. Eine Episode über Rahmen statt Regeln, über Teilen statt Verstecken – und darüber, warum man Daten genauso bewusst pflegen (und auch entsorgen) sollte wie in einem Garten.

Charlotte Ledoux est une experte Data Gouvernance, elle accompagne de très belles boîtes comme Pernod Ricard ou Disney. En parallèle, elle crée du contenu sur LinkedIn sur ce sujet avec beaucoup de succès (+50K abonnés) et est identifiée par les leaders data comme l'experte n°1 sur la Data Gouvernance.On aborde :

In dieser Episode teilt Peter Bluhm die wichtigsten Erkenntnisse aus vier intensiven Gesprächen mit Entscheidern und Experten: von der AXA Schweiz über einen mittelständischen Maschinenbauer bis hin zu akademischen Spezialisten für Unternehmensplanung und Data Governance. Die 5 zentralen Learnings: Datenstrategie entwickelt sich in Phasen – nicht als Big Bang  Der Pareto-Ansatz bei Datenqualität: 80/20 statt 100% Perfektion Datenqualität ist kontextabhängig – ohne Verwendungszweck keine sinnvolle Messung Erwartungstreue Planwerte statt ambitionierte Wunschszenarien Data Governance – der größte Fehler ist, nichts zu tun  Zentrale Erkenntnisse aus den Gesprächen: Die AXA Schweiz entwickelte ihre Datenstrategie über 10 Jahre – von explorativen Ansätzen mit einem Server unter dem Schreibtisch bis zur Data Mesh-Architektur. Learning by Doing statt Masterplan. Mehrer Compression startete mit 80 Prozent Datenqualität und verbesserte iterativ. Das Ergebnis: Die Liefertreue stieg von 77 Prozent auf über 95 Prozent, ohne jahrelange Datenbereinigung. Erfolgreiche Datenarbeit erfordert keine Perfektion, sondern eine realistische, pragmatische Herangehensweise. Themen:  Business Intelligence | Datenstrategie | Data Governance | Datenqualität | Unternehmensplanung | KI-Projekte | Controlling | Performance Management Die Gesprächspartner:  Dr. Kathrin Braunwarth, Mitglied der Geschäftsleitung, AXA Schweiz  Jens Elfert, Bereichsleiter Finanzen, Mehrer Compression  Prof. Dr. Werner Gleißner, TU Dresden  Dr. Christiana Klingenberg, IDIGMA  Prof. Dr. Christine Weber, TH Würzburg-Schweinfurt 

"Balanced Scorecards für AI müssen nicht konzernweit sein. Warum? Weil jede Abteilung mit AI eine andere Art von Wert erzeugen möchte und wird."

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

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

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

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

Mathias is Head of Data for Marketing at N26, the Berlin-based neobank valued at over $9 billion. He joined as a Senior Data Analyst in May 2020 and has since scaled the team to 12 people.We cover :

This Podcast is sponsored by Team Simmer.Go to TeamSimmer and use the coupon code DEVIATE for 10% on individual course purchases.The Technical Marketing Handbook provides a comprehensive journey through technical marketing principles.Sign up to the Simmer Newsletter for the latest news in Technical Marketing.NEW SIMMER COURSE ALERT!  - Data Analysis with R - taught by Arben Kqiku (coupon code doesn't apply to this course)Latest content from Simo AhavaRun Server-side Google Tag Manager On Localhost ArticleLatest content from Juliana JacksonThe distance between what gets funded and what works has never been wider. (subscribe to the newsletter for more amazing content)Mentioned in the episode:Superweek Analytics SummitMeasurecamp HelsinkiConnect with Sayf Sharif:LinkedinThree Bears DataOptiMeasure This podcast is brought to you by Juliana Jackson and Simo Ahava.

Widersprüchliche Berichte aus verschiedenen Systemen, unklare Zuständigkeiten bei Stammdaten und endlose Diskussionen über die „richtige" Zahl – viele CFO, Controller und IT-Verantwortliche erleben diese Herausforderungen täglich. Daten werden als „das neue Öl" bezeichnet, doch im Alltag herrscht oft Chaos statt Strategie. Genau hier setzt Data Governance an – als strukturierter Rahmen für den professionellen Umgang mit der Unternehmensressource Daten. Zu Gast: Dr. Christiana Klingenberg - Leiterin Data Management Center of Excellence bei iDIGMA und Prof. Dr. Kristin Weber - Vizepräsidentin für Digitalisierung und CIO der TH Würzburg-Schweinfurt. In dieser Episode: Warum Data Governance eine „Journey" ist – kein klassisches Projekt mit Anfang und Ende. Wie das qualitätsorientierte Framework auf drei Ebenen aufbaut: von der Strategie über Rollen und Kultur bis zur technischen Infrastruktur. Wie man Datenqualität messbar und die versteckten Kosten schlechter Daten sichtbar macht. Welche Rolle Data Governance bei KI-Anwendungen spielt und konkrete erste Schritte für den Einstieg. Beide Autorinnen bringen rund 20 Jahre Erfahrung aus Beratung, Forschung und Praxis mit und haben kürzlich die zweite Auflage ihres Fachbuches „Data Governance - Der Leitfaden für die Praxis" im Hanser Verlag veröffentlicht.

Widersprüchliche Berichte aus verschiedenen Systemen, unklare Zuständigkeiten bei Stammdaten und endlose Diskussionen über die „richtige" Zahl – viele CFO, Controller und IT-Verantwortliche erleben diese Herausforderungen täglich. Daten werden als „das neue Öl" bezeichnet, doch im Alltag herrscht oft Chaos statt Strategie. Genau hier setzt Data Governance an – als strukturierter Rahmen für den professionellen Umgang mit der Unternehmensressource Daten. Zu Gast: Dr. Christiana Klingenberg - Leiterin Data Management Center of Excellence bei iDIGMA und Prof. Dr. Kristin Weber - Vizepräsidentin für Digitalisierung und CIO der TH Würzburg-Schweinfurt. In dieser Episode: Warum Data Governance eine „Journey" ist – kein klassisches Projekt mit Anfang und Ende. Wie das qualitätsorientierte Framework auf drei Ebenen aufbaut: von der Strategie über Rollen und Kultur bis zur technischen Infrastruktur. Wie man Datenqualität messbar und die versteckten Kosten schlechter Daten sichtbar macht. Welche Rolle Data Governance bei KI-Anwendungen spielt und konkrete erste Schritte für den Einstieg. Beide Autorinnen bringen rund 20 Jahre Erfahrung aus Beratung, Forschung und Praxis mit und haben kürzlich die zweite Auflage ihres Fachbuches „Data Governance - Der Leitfaden für die Praxis" im Hanser Verlag veröffentlicht.

„Mit der Datenstrategie haben wir erkannt, dass die Nutzung von Daten als strategische Ressource einer der großen Game Changer ist.“

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

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

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

In this episode, we share a webinar hosted by Progress about data and AI readiness. Here is the webinar description:In today's business landscape, data is your most powerful asset, but only if it's accessible, digestible and ready for implementation. Progress recently surveyed enterprise leaders to understand the state of data readiness, uncovering critical insights across four key areas.In this on-demand webinar, you'll hear David Loshin, Data and Information expert from Knowledge Integrity, Inc., dive deeper into the findings from Progress' latest report. The report results highlight that while many organizations recognize the potential of AI, data challenges are still a major barrier.   You'll leave the webinar with key takeaways around topics like these:Data Literacy & Accessibility: Are teams able to find and use the data they need?Business Initiative Support: How well does your data landscape enable innovation and growth?Data Readiness for AI: Is your data prepared to fuel AI and advanced analytics initiatives?Data Governance & Risk Management: Are proper controls in place to facilitate  compliant, trustworthy AI outcomes?You can download the full report here: https://www.progress.com/resources/papers/data-trends 

In this episode, Kris Kovacs hosts Troy Wyatt, CEO of DataOps, and Shawn Gilfedder, CEO of Kitsap Credit Union, to discuss the transformative impact of agentic technology on credit unions. The conversation begins with an introduction to the participants and a brief overview of their organizations. Shawn shares insights into Kitsap's journey towards leveraging data and AI to enhance member experiences, emphasizing the importance of internal culture and collaboration in adopting new technologies. Troy elaborates on how DataOps supports credit unions in managing their data effectively, enabling them to create personalized services for their members. The discussion also touches on the recent changes in Washington state law that allow credit unions to make direct equity investments in tech companies, highlighting Kitsap's pioneering role in this area.As the conversation progresses, the speakers delve into the challenges and opportunities presented by agentic technology, including the need for a cultural shift within organizations to embrace innovation. They discuss the importance of understanding business processes and data governance, as well as the potential for AI to enhance operational efficiency. The episode concludes with reflections on the future of credit unions in a rapidly evolving technological landscape, emphasizing the need for continuous learning and adaptation to meet member needs effectively.Follow the Pod:https://twitter.com/fintechcombineFollow Kris Kovacs:https://twitter.com/ManagementByteshttps://www.linkedin.com/in/kriskovacs/https://www.instagram.com/kriskovacs/The Fintech Combine is Produced and Edited by Anson Beckler-JonesFollow Anson Beckler-JonesInstagram - @ansonandcoYoutube - @ansonandco

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

This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubCheck out more here:https://gotopia.tech/episodes/409Dr. Larysa Visengeriyeva - Author of "The AI Engineer's Guide to Surviving the EU AI Act" & Independent Consultant for EU AI Act EngineeringBarbara Lampl - Behavioral Mathematician at empathic business by Barbara LamplRESOURCESLarysahttps://x.com/visengerhttps://bsky.app/profile/visenger.bsky.socialhttps://github.com/visengerhttps://www.linkedin.com/in/larysavisengerBarbarahttps://x.com/BarbaraLamplhttps://www.linkedin.com/in/barbaralamplhttps://barbara-lampl.tumblr.comLinkshttps://ml-ops.orghttps://github.com/visenger/awesome-mlopshttps://eur-lex.europa.eu/eli/reg/2024/1689/oj/enghttps://machinelearningcanvas.comhttps://louisdorard.gumroad.com/l/mlcanvashttps://ml-ops.org/content/crisp-mlDESCRIPTIONBarbara Lampl interviews Larysa Visengeriyeva, software engineer and "godmother of MLOps", about her new book on AI engineering and compliance. What starts as a discussion about the EU AI Act quickly reveals a deeper truth: the real challenge isn't regulatory compliance - it's fundamental engineering practices.Larysa argues that quality AI systems require robust MLOps, comprehensive documentation, and proper data governance, whether regulation mandates it or not. Drawing from frameworks like CRISP-ML and the Machine Learning Canvas, the book provides practical checklists and methodologies for taking AI projects from prototype to production. Written partially in Ukraine during wartime, this "battle-tested" guide addresses the gap between technical and non-technical stakeholders, offering a common language for building sustainable AI systems.RECOMMENDED BOOKSLarysa Visengeriyeva • The AI Engineer's Guide to Surviving the EU AI Act • https://amzn.to/42SKOuULakshmanan, Robinson & Munn • Machine Learning Design Patterns • https://amzn.to/4ox4EosPhil Winder • Reinforcement Learning • https://amzn.to/3t1S1VZDiana Montalion • Learning Systems Thinking • https://amzn.to/3ZpycdJBernd Rücker • Practical Process Automation • https://amzn.to/3cs3BSHLauren Maffeo • Designing Data Governance from the Ground Up • https://amzn.to/3QhIlnVKatharine Jarmul • Practical Data Privacy • https://amzn.to/46XPrnsZhamak Dehghani • Data Mesh • https://amzn.to/3tTCwACKate Stanley & Mickael Maison • Kafka Connect • https://amzn.to/40Jq5JzBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

Today's guest is Sandro Venturini, Executive Director at UBS Asset Management Switzerland. Sandro brings deep expertise in fund structuring, cross-border launches, and data integration for financial services. Sandro joins Emerj Editorial Director Matthew DeMello to explore how fragmented fund data creates silos in mergers and launches, and how a single source of truth enables AI to streamline compliance and reporting. Sandro also shares practical takeaways, such as using AI to anticipate stakeholder concerns from term sheets, generate draft prospectuses that slash legal fees and formation costs, and customize investor reporting to reduce manual errors across share classes. Just a quick note for our audience that the views expressed by Sandro Venturini on today's program do not reflect those of UBS or its leadership. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the 'AI in Business' podcast!

Data as a Product: Was steckt dahinter?Warum ist AI überall, aber der Weg von der Datenbank zu "Wow, das Modell kann das" wirkt oft wie ein schwarzes Loch? Du loggst brav Events, die Daten landen in irgendwelchen Silos, und trotzdem bleibt die entscheidende Frage offen: Wer sorgt eigentlich dafür, dass aus Rohdaten ein zuverlässiges, verkaufbares Datenprodukt wird.In dieser Episode machen wir genau dort das Licht an. Gemeinsam mit Mario Müller, Director of Data Engineering bei Veeva Systems, schauen wir uns an, was Datenteams wirklich sind, wie "Data as a Product" in der Praxis funktioniert und warum Data Engineering mehr ist als nur ein paar CSVs über FTP zu schubsen. Wir sprechen über Teamstrukturen von der One-Man-Show bis zur cross-functional Squad, über Ownership auf den Daten, Data Governance und darüber, wie du Datenqualität wirklich misst, inklusive Monitoring, Alerts, SQL-Regeln und menschlicher Quality Control.Dazu gibt es eine ordentliche Portion Tech: Spark, AWS S3 als primärer Speicher, Delta Lake, Athena, Glue, Airflow, Push-Pull statt Event-Overkill und die Entscheidung für Batch Processing, obwohl alle Welt nach Streaming ruft.Und natürlich klären wir auch, was passiert, wenn KI an den Daten rumfummelt: Wo AI beim Bootstrapping hilft, warum Production und Scale tricky werden und wieso Verantwortlichkeit beim Commit nicht von einem LLM übernommen wird.Wenn du Datenteams aufbauen willst, Data Products liefern musst oder einfach verstehen willst, wie aus Daten verlässlicher Business-Impact wird, bist du hier genau richtig.Bonus: Batchjobs bekommen heute mal ein kleines Comeback.Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:

Drs. Kaul and Adegunsoye discuss Veterans Affairs' innovative approach to understanding and treating pulmonary fibrosis among veterans by leveraging extensive electronic health records and data repositories. The work focuses on addressing healthcare disparities, exploring risk factors, and developing a hub-and-spoke telehealth model to provide specialized care to veterans in rural and underserved areas.

Stop treating data governance as a "data cop" function and start using it as a high ROI offensive weapon. In this episode, Peter Kapur, Head of Data Governance and Data Quality at CarMax, breaks down how to move beyond defensive compliance to drive profitability, customer experience, and better data science outcomes.Critical Insights for LeadersShift from defense to offense Data defense covers the mandatory regulatory and legal requirements like privacy and cybersecurity. Data offense involves everything else that hits your bottom line, such as investing in data quality to save or make money.Prioritize problems over frameworks Avoid bringing rigid policies and "data geek" terminology to business leaders. Instead, spend time listening to their specific data struggles and apply governance capabilities as solutions to those problems.Data quality makes governance tangible Without high quality data, governance is just a collection of abstract policies. Improving data quality empowers data scientists to produce better models and gives analytics teams the ability to discover and trust their data.Key Moments in the Conversation02:41 Defining the clear line between defensive regulation and offensive growth 06:03 Why data quality and data governance must sit together to be effective 11:00 Shifting from "data school" to "business school" to communicate value 13:12 Quantifying the ROI of data governance through customer wins and time savings 18:35 Actionable advice for starting an offensive strategy from scratch Wisdom from the Episode"If we meet the laws, we meet the regulations, we meet the legal, how do we leverage our data? It is a mindset shift versus, let me lock my data down, no one use it." Tactical Advice for ImplementationEnsure adoption through personalization Design tools and processes that are personalized to specific roles so they feel like a natural part of the workflow rather than a burden.Focus on the eye of the consumer Treat every person in the organization as a "data citizen" and remember that data quality is ultimately defined by the needs of the people consuming it.Join the ConversationSubscribe to the podcast on your favorite platform to catch every episode. Follow us on LinkedIn to stay updated on the latest trends in data leadership.

Meet Susannah Hammond, a trailblazer in governance, risk management, and compliance (GRC) with over 30 years of experience in financial services and technology. A chartered accountant and a distinguished fellow of the Chartered Institute for Securities and Investment, Susannah is a powerful voice in her field. As the recent winner of the Risky Women Write competition, her groundbreaking article on embedded supervision is set to revolutionize compliance. She champions a bold vision where regulators gain direct access to firms' systems, transforming the regulatory landscape into a more efficient and collaborative process. With her insights, Susannah is paving the way for a future where technology enhances compliance, saves costs, and builds trust—an evolution that could change the game for GRC forever! SHOW NOTES01:32 Career Journey 10:48 Embedded Supervision and Its Potential 16:07 Data Governance and AI in Compliance 21:02 Chat Bankman-Fried? 25:36 Predictions and Future of GRC Get transcripts, links, and read her winning article: https://www.riskywomen.org/2025/12/podcast-s8e14-embedded-supervision-with-susannah-hammond/

As organizations race to adopt AI, many discover an uncomfortable truth: ambition often outpaces readiness. In this episode of the ITSPmagazine Brand Story Podcast, host Sean Martin speaks with Julian Hamood, Founder and Chief Visionary Officer at TrustedTech, about what it really takes to operationalize AI without amplifying risk, chaos, or misinformation.Julian shares that most organizations are eager to activate tools like AI agents and copilots, yet few have addressed the underlying condition of their environments. Unstructured data sprawl, fragmented cloud architectures, and legacy systems create blind spots that AI does not fix. Instead, AI accelerates whatever already exists, good or bad.A central theme of the conversation is readiness. Julian explains that AI success depends on disciplined data classification, permission hygiene, and governance before automation begins. Without that groundwork, organizations risk exposing sensitive financial, HR, or executive data to unintended audiences simply because an AI system can surface it.The discussion also explores the operational reality beneath the surface. Most environments are a patchwork of Azure, AWS, on-prem infrastructure, SaaS platforms, and custom applications, often shaped by multiple IT leaders over time. When AI is layered onto this complexity without architectural clarity, inaccurate outputs and flawed business decisions quickly follow.Sean and Julian also examine how AI initiatives often emerge from unexpected places. Legal teams, business units, and individual contributors now build their own AI workflows using low-code and no-code tools, frequently outside formal IT oversight. At the same time, founders and CFOs push for rapid AI adoption while resisting the investment required to clean and secure the foundation.The episode highlights why AI programs are never one-and-done projects. Ongoing maintenance, data validation, and security oversight are essential as inputs change and systems evolve. Julian emphasizes that organizations must treat AI as a permanent capability on the roadmap, not a short-term experiment.Ultimately, the conversation frames AI not as a shortcut, but as a force multiplier. When paired with disciplined architecture and trusted guidance, AI enables scale, speed, and confidence. Without that discipline, it simply magnifies existing problems.Note: This story contains promotional content. Learn more.GUESTJulian Hamood, Founder and Chief Visionary Officer at TrustedTech | On LinkedIn: https://www.linkedin.com/in/julian-hamood/Are you interested in telling your story?▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full▶︎ Spotlight Brand Story: https://www.studioc60.com/content-creation#spotlight▶︎ Highlight Brand Story: https://www.studioc60.com/content-creation#highlightKeywords: sean martin, julian hamood, trusted tech, ai readiness, data governance, ai security, enterprise ai, brand story, brand marketing, marketing podcast, brand story podcast, brand spotlight Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Episode Overview In this episode of Talk Retail To Me, Parker Avery's chief revenue officer, Dan Wittner, and Blue Yonder's head of global retail strategy, Jim Hull, discuss the evolving landscape of retail, focusing on the integration of artificial intelligence, the importance of data quality, and the transformation of store models into fulfillment centers. They explore the challenges retailers face with labor and the need for dynamic data models to adapt to changing consumer demands. The conversation also highlights the upcoming NRF 2026 Big Show and the excitement surrounding innovations in the retail sector. Key Takeaways AI is now a core part of retail strategy and must be tied to business outcomes. The most successful initiatives focus on measurable impact, such as cost reduction, revenue growth, or operational efficiency. Human adoption is critical to AI success. AI works best when it augments teams and is embraced by users, rather than positioned as a replacement for people. Data quality and dynamic data models are foundational to modern retail. Clean, consistent data and continuously evolving models are essential for accurate forecasting and decision-making. Stores are evolving into hybrid fulfillment hubs. Retailers are hyper-focused on redesigning store models to support fulfillment alongside shopping, improving speed, cost, and customer experience. Labor models must adapt to support fulfillment-enabled stores. New operating models require updated labor standards, expanded skill sets, and more flexible workforce planning. NRF 2026 will showcase retail innovation moving from concept to reality. The event is expected to highlight proven, market-ready solutions delivering real value at scale. Episode Highlights Why most AI projects succeed or fail in retail How retailers are rethinking demand signals and forecasting The growing importance of data quality and dynamic data models The evolution of stores into fulfillment and service hubs What to expect at NRF 2026 (& meet Parker Avery at booth #2703) Related Content Blog Post: Optimizing Data Readiness for AI in Retail Podcast: Retail's Data Reckoning: Your First Move in Transformation Case Study: New Data-Driven Solution Strengthens Grocer's Competitive Pricing Capabilities Insights: The Expert Guide to Data Governance in Retail  

In this episode of How I Met Your Data, Christina Hois, a data leader with over 30 years in financial services, joins Anjali and Junaid to explore why data challenges rarely stem from technology — and almost always come down to people. Christina shares why breaking down cultural silos, building stakeholder trust, and prioritizing data tied to real business outcomes matter more than deploying new tools. The conversation highlights the critical role of emotional intelligence, credibility, and collaboration in making data governance actually work. The takeaway is clear: technology enables scale, but trust and teamwork determine success.  

Algorithms and automations have been buds for a decade plus.

Mike & Tommy dive into "Data Governance in the Age of Fabric IQ," exploring whether Fabric IQ is truly the missing link for effective data governance or just another AI feature. They discuss practical strategies for organizations to enhance their governance frameworks and ensure successful implementation.https://blog.fabric.microsoft.com/en-us/blog/introducing-fabric-iq-the-semantic-foundation-for-enterprise-ai?ft=Allhttps://learn.microsoft.com/en-us/fabric/iq/https://learn.microsoft.com/en-us/fabric/governance/governance-compliance-overviewGet 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/

⬥EPISODE NOTES⬥Artificial intelligence is reshaping how public health organizations manage data, interpret trends, and support decision-making. In this episode, Sean Martin talks with Jim St. Clair, Vice President of Public Health Systems at a major public health research institute, Altarum, about what AI adoption really looks like across federal, state, and local agencies.Public health continues to face pressure from shifting budgets, aging infrastructure, and growing expectations around timely reporting. Jim highlights how initiatives launched after the pandemic pushed agencies toward modernized systems, new interoperability standards, and a stronger foundation for automated reporting. Interoperability and data accessibility remain central themes, especially as agencies work to retire manual processes and unify fragmented registries, surveillance systems, and reporting pipelines.AI enters the picture as a multiplier rather than a replacement. Jim outlines practical use cases that public health agencies can act on now, from community health communication tools and emergency response coordination to predictive analytics for population health. These approaches support faster interpretation of data, targeted outreach to communities, and improved visibility into ongoing health activity.At the same time, CISOs and security leaders are navigating a new risk environment as agencies explore generative AI, open models, and multi-agent systems. Sean and Jim discuss the importance of applying disciplined data governance, aligning AI with FedRAMP and state-level controls, and ensuring that any model running inside an organization's environment is treated with the same rigor as traditional systems.The conversation closes with a look at where AI is headed. Jim notes that multi-agent frameworks and smaller, purpose-built models will shape the next wave of public health technology. These systems introduce new opportunities for automation and decision support, but also require thoughtful implementation to ensure trust, reliability, and safety.This episode presents a realistic, forward-looking view of how AI can strengthen the future of public health and the cybersecurity responsibilities that follow.⬥GUEST⬥Jim St. Clair, Vice President, Public Health Systems, Altarum  | On LinkedIn: https://www.linkedin.com/in/jimstclair/⬥HOST⬥Sean Martin, Co-Founder at ITSPmagazine and Host of Redefining CyberSecurity Podcast | On LinkedIn: https://www.linkedin.com/in/imsmartin/ | Website: https://www.seanmartin.com⬥RESOURCES⬥N/A⬥ADDITIONAL INFORMATION⬥✨ More Redefining CyberSecurity Podcast: 

⬥EPISODE NOTES⬥Artificial intelligence is reshaping how public health organizations manage data, interpret trends, and support decision-making. In this episode, Sean Martin talks with Jim St. Clair, Vice President of Public Health Systems at a major public health research institute, Altarum, about what AI adoption really looks like across federal, state, and local agencies.Public health continues to face pressure from shifting budgets, aging infrastructure, and growing expectations around timely reporting. Jim highlights how initiatives launched after the pandemic pushed agencies toward modernized systems, new interoperability standards, and a stronger foundation for automated reporting. Interoperability and data accessibility remain central themes, especially as agencies work to retire manual processes and unify fragmented registries, surveillance systems, and reporting pipelines.AI enters the picture as a multiplier rather than a replacement. Jim outlines practical use cases that public health agencies can act on now, from community health communication tools and emergency response coordination to predictive analytics for population health. These approaches support faster interpretation of data, targeted outreach to communities, and improved visibility into ongoing health activity.At the same time, CISOs and security leaders are navigating a new risk environment as agencies explore generative AI, open models, and multi-agent systems. Sean and Jim discuss the importance of applying disciplined data governance, aligning AI with FedRAMP and state-level controls, and ensuring that any model running inside an organization's environment is treated with the same rigor as traditional systems.The conversation closes with a look at where AI is headed. Jim notes that multi-agent frameworks and smaller, purpose-built models will shape the next wave of public health technology. These systems introduce new opportunities for automation and decision support, but also require thoughtful implementation to ensure trust, reliability, and safety.This episode presents a realistic, forward-looking view of how AI can strengthen the future of public health and the cybersecurity responsibilities that follow.⬥GUEST⬥Jim St. Clair, Vice President, Public Health Systems, Altarum  | On LinkedIn: https://www.linkedin.com/in/jimstclair/⬥HOST⬥Sean Martin, Co-Founder at ITSPmagazine and Host of Redefining CyberSecurity Podcast | On LinkedIn: https://www.linkedin.com/in/imsmartin/ | Website: https://www.seanmartin.com⬥RESOURCES⬥N/A⬥ADDITIONAL INFORMATION⬥✨ More Redefining CyberSecurity Podcast: 

In this essay, Juan Carlos Mijangos Noh reflects on an experience of creating a microcosm of an anarchist community in a Yucatecan Maya Village in Mexico. The experience involved women students trained in a neoliberal university who, despite that, were able to perform in an anarchistic fashion. Juan Carlos Mijangos Noh is a full professor at the Autonomous University of Yucatan, Mexico. His most recent publications are: Creating an Anarchist Community: How can Students from a Neoliberal University Participate?, published by Anarchist Studies (https://journals.lwbooks.co.uk/anarchiststudies/vol-33-issue-2/); and The Canicab Charter: A Mayan Model for Data Governance, published by the International Council of Media Literacy (https://ic4ml.org/journal-article/the-canicab-charter-a-mayan-model-for-data-governance/). A longer version of this article appeared in Anarchist Studies 33.2 (2025). For a version of this podcast in Spanish, fast forward to 15.34 Anarchist Essays is brought to you by Loughborough University's Anarchism Research Group and the journal Anarchist Studies. Follow us on Bluesky @anarchismresgroup.bsky.social Our music comes from Them'uns (featuring Yous'uns). Artwork by Sam G.

AI-integrated tools, such as OpenAI's Atlas and Microsoft Teams, are introducing new trust and identity risks, particularly through vulnerabilities like prompt injections and guest access features. The Atlas browser, launched on October 21, 2025, has been identified as having security flaws that could allow attackers to inject harmful instructions. Similarly, Microsoft Teams has a vulnerability that permits attackers to bypass security protections when users join external tenants as guests. These developments highlight the fragility of AI integrations and the need for robust security measures in collaborative environments.The FBI has reported over $262 million in losses due to account takeover fraud schemes, with more than 5,100 complaints filed this year. Cybercriminals are employing social engineering tactics to gain unauthorized access to online banking and payroll accounts, often locking victims out by changing passwords. The FBI recommends that individuals monitor their financial accounts closely, use complex passwords, and enable multi-factor authentication to mitigate these risks. This trend underscores the importance of managing trust and identity in security practices, as attackers increasingly exploit human vulnerabilities rather than technical flaws.In the managed service provider (MSP) sector, a recent survey by OpenText Cybersecurity revealed that while 92% of MSPs are experiencing growth driven by interest in AI, fewer than half feel prepared to implement AI tools effectively. This marks a significant decline from the previous year's 90% readiness. Additionally, 71% of MSPs reported that their small and medium-sized business clients prefer bundled security solutions, indicating a shift towards integrated offerings that simplify decision-making for clients. The findings suggest that MSPs need to focus on data governance and readiness before deploying AI solutions.For MSPs and IT service leaders, the key takeaway is that modern security is increasingly about managing identity and data governance rather than merely adding more tools. As AI vulnerabilities and account takeover fraud become more prevalent, providers must prioritize establishing secure trust boundaries and effective data management practices. By doing so, MSPs can differentiate themselves in a competitive market, ensuring they are equipped to deliver secure AI solutions and meaningful automation to their clients. Three things to know today00:00 New AI, Collaboration, and Fraud Threats Underscore That Identity—not Infrastructure—is the Real Security Battleground05:15 Survey Shows MSPs Expanding Services Amid AI Interest, Yet True Opportunity Lies in Readiness and Governance07:45 New MSP Integrations, Funding, and AI Platforms Underscore the Shift Toward Identity and Data Governance as the True Control Plane This is the Business of Tech.     Supported by:  https://try.auvik.com/dave-switchhttps://scalepad.com/dave/

What does it take to be ready to deploy M365 Copilot in your organization? Richard talks to Nikki Chapple about her latest incarnation of the M365 Copilot Readiness Checklist, working step-by-step to bring M365 Copilot into the organization without causing data leak issues. Nikki discusses utilizing existing tools to accurately identify sensitive data, archiving outdated information, and monitoring data usage by both users and agents - allowing you to detect issues before they escalate. The conversation also delves into the process of identifying issues, discussing policy changes, and how to communicate those changes so that people can take advantage of the power of these new tools without feeling threatened. It's a journey!LinksMicrosoft PurviewSharePoint Advanced ManagementDefender for Cloud AppsRestricted SharePoint SearchMicrosoft 365 ArchiveSharePoint Restricted Content DiscoveryData Security Posture Management for AINikki's Readiness ChecklistM365 Copilot Oversharing BlueprintMicrosoft Purview Secure by Default BlueprintPrevent Data Leaks to Shadow AI BlueprintRecorded November 7, 2025

OpenAI has declared a "code red" as it faces intensified competition from companies like Google and Anthropic, prompting a shift in focus towards enhancing the capabilities of its ChatGPT platform. CEO Sam Altman has urged staff to prioritize improvements in speed, reliability, and personalization, delaying other initiatives such as advertising. This strategic pivot comes as OpenAI also announced an ownership stake in Thrive Holdings, a company that acquires and transforms service firms, particularly in accounting and IT, with the aim of integrating AI to improve operational efficiency.A recent study by Stebo Systems highlights a significant gap between Chief Information Officers' (CIOs) perceptions of their organizations' AI readiness and the actual state of their data management. While 91% of U.S. business leaders recognize the importance of customer data management, only 31% fully trust their data. The study reveals that 76% of organizations still rely on side spreadsheets to address data issues, indicating a lack of centralized data governance. This fragmentation leads to wasted time and lost revenue, underscoring the need for robust data governance frameworks as organizations aim to adopt AI-driven services.The episode also discusses the implications of surveillance AI, particularly a model developed by Securis Technologies that predicts and prevents crimes among inmates using data from prison phone calls. While the company claims success in disrupting criminal activities, concerns about civil liberties and the ethical use of such data have been raised. This situation serves as a cautionary tale for IT service providers, emphasizing the importance of establishing governance frameworks to mitigate risks associated with AI deployment in business environments.For Managed Service Providers (MSPs) and IT leaders, the key takeaway is the necessity of controlling their own AI strategies. As OpenAI integrates vertically into the service industry, independent MSPs must differentiate themselves by building their own AI capabilities and data governance practices. The episode stresses that without a solid data foundation, AI initiatives are likely to fail, and MSPs should focus on enhancing data quality and governance to ensure successful AI implementations and maintain client trust. Four things to know today 00:00 OpenAI Pauses Ads Amid “Code Red” While Buying Into MSP Rollup, Raising Platform–Competitor Concerns05:16 Leaders Say They're Ready for AI — but Their Data Still Lives in Side Spreadsheets07:49 Securus Call-Monitoring AI Shows How Predictive Surveillance Could Spill Into Workplace Tools10:27 New AI Helpers and Simple M365 Assessments Raise the Bar for Modern IT Providers This is the Business of Tech.    Supported by:  https://getflexpoint.com/msp-radio/https://cometbackup.com/?utm_source=mspradio&utm_medium=podcast&utm_campaign=sponsorship

Send us a textIn this episode, Joe sits down with Vishnu Varma to explore the evolving landscape of cybersecurity and data management. Vishnu shares his journey from India to the US, detailing his experiences at Cisco and the rise of cloud security. They delve into the challenges of managing vast amounts of data in the age of AI, discussing how BonFi AI is innovating in data security. Tune in to learn about the importance of context in data protection and the future of cybersecurity in a rapidly changing digital world.00:00:19 Introduction to Vishnu's Journey00:00:30 Entering the US and Cisco00:02:18 Cloud Security and AI00:02:48 Data Governance and Challenges00:08:47 The Expansiveness of Cloud00:11:00 AI's Appetite for Data00:12:11 Data Security in the JNI Era00:14:29 The Importance of Context00:16:13 Data Used by Enterprises00:22:24 Conclusion and Future Trendshttps://www.bonfy.ai/Bonfy.aiBonfy ACS is a next-gen DLP platform built for the AI era. Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.Support the showFollow the Podcast on Social Media! Tesla Referral Code: https://ts.la/joseph675128 YouTube: https://www.youtube.com/@securityunfilteredpodcast Instagram: https://www.instagram.com/secunfpodcast/Twitter: https://twitter.com/SecUnfPodcast Affiliates➡️ OffGrid Faraday Bags: https://offgrid.co/?ref=gabzvajh➡️ OffGrid Coupon Code: JOE➡️ Unplugged Phone: https://unplugged.com/Unplugged's UP Phone - The performance you expect, with the privacy you deserve. Meet the alternative. Use Code UNFILTERED at checkout*See terms and conditions at affiliated webpages. Offers are subject to change. These are affiliated/paid promotions.

In this episode, we explore the fascinating concept of sensory living networks with Prof. Dimitra Simeonidou, director of the Smart Internet Lab, Paul Wilson, chair of the advisory board for SmartCitiesWorld and Simon Saunders, honorary professor at the Smart Internet Lab. Discover how these intelligent networks can revolutionise emergency responses and provide critical data to enhance urban living. Join us as we delve into the potential benefits and ethical considerations of utilising such technology for the greater good of society.Chapters:(00:00) Introduction(01:15) Sensory Living Networks Explained(05:30) Emergency Services and Data Utilisation(10:00) The Role of Regulation(15:45) Global Standards and Cooperation(20:30) Future of AI and Data Governance(25:00) ConclusionThis is an 18Sixty Production.

Jeff Marraccini is back and he brought Daniel Ayala with him. Jeff is a former VP and CISO, and Daniel is one of the hosts of Great Security Debate podcast. Jeff shares his journey from IT infrastructure to a CISO role, discussing the challenges of securing software and the importance of continuous adaptation. The conversation then shifts into the evolving landscape of AI, the potential pitfalls of zero trust and data governance, and the implications of regulation on VPN usage. The episode wraps up with a look at the critical role of open source software in the IT industry, highlighting its benefits and the security risks it poses.

This is the takeaway episode with Jessica Zuñiga is the Head of AI Readiness & Data Governance at BlueYeti and one of the most important voices in data governance in Mexico and Latin America. Tim and Juan discuss with Jessica about when data and AI governance truly becomes a business enabler and how to use frameworks like DAMA effectively. If you like what you heard, you should check out the full episode!See omnystudio.com/listener for privacy information.

Jessica Zuñiga is the Head of AI Readiness & Data Governance at BlueYeti and one of the most important voices in data governance in Mexico and Latin America. Tim and Juan discuss with Jessica about when data and AI governance truly becomes a business enabler and how to use frameworks like DAMA effectively. See omnystudio.com/listener for privacy information.

This is the takeaway episode with Jessica Zuñiga is the Head of AI Readiness & Data Governance at BlueYeti and one of the most important voices in data governance in Mexico and Latin America. Tim and Juan discuss with Jessica about when data and AI governance truly becomes a business enabler and how to use frameworks like DAMA effectively. If you like what you heard, you should check out the full episode!See omnystudio.com/listener for privacy information.

Jessica Zuñiga is the Head of AI Readiness & Data Governance at BlueYeti and one of the most important voices in data governance in Mexico and Latin America. Tim and Juan discuss with Jessica about when data and AI governance truly becomes a business enabler and how to use frameworks like DAMA effectively. See omnystudio.com/listener for privacy information.

In this episode of "Alter Everything," we sit down with Andrew Merrill, Alteryx product specialist and advocate, to explore best practices for integrating AI and LLMs into data analytics processes. Some topics we discuss include proven design patterns for generative AI, such as feedback loops, routing, and RAG architectures, and learn how to avoid common pitfalls like token overuse and data governance challenges. Andrew shares real-world use cases, tips for leveraging Alteryx Co-pilot, and strategies for prompt engineering to maximize workflow efficiency. Panelists: Andrew Merrill, Alteryx Consultant - @CoG, LinkedInMegan Bowers, Sr. Content Manager @ Alteryx - @MeganBowers, LinkedInShow notes: Alteryx Gen AI ToolsAlteryx Co-pilotAlteryx Inspire Interested in sharing your feedback with the Alter Everything team? Take our feedback survey here!This episode was produced by Megan Bowers, Mike Cusic, and Matt Rotundo. Special thanks to Andy Uttley for the theme music.

In this episode, IBM's Kal Gyimesi and Vishal Rane explore how AI is redefining efficiency in asset lifecycle management. They share why strong data governance is the foundation for successful AI adoption — noting that only 25% of AI projects meet ROI expectations due to disconnected systems. The conversation breaks down different types of AI, including generative AI, machine learning, chat assistance and intelligent agents, and how each drives smarter decision-making. They also emphasize the need for continuous testing, monitoring and responsible deployment to ensure reliability and long-term value across operations.00:00 Introduction to AI in Facility Management01:38 The Hype and Reality of AI03:10 Types of AI in Organizations04:34 Asset Lifecycle Management07:18 Data Governance and AI Implementation08:31 Corporate Real Estate and Digital Transformation12:09 Conclusion and Key TakeawaysThis episode is sponsored by ODP Business Solutions! 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

Systems should make life easier, not more complicated. That idea runs through our conversation with technology strategist “VPN,” whose journey from SAP in India to the UN in Geneva to advising global institutions shaped a simple practice: start with the problem, then use data and AI to serve people with clarity and care.We dig into what most teams get wrong about data—confusing volume with insight and falling into confirmation bias. Instead of chasing clever dashboards, we map a workflow where hypotheses are tested, methods are transparent, and systems explain themselves in plain language. The result is trust. And trust is what unlocks adoption, the critical moment when data actually changes a decision. From HR policy Q&A to legal discovery, we show how AI can strip away repetitive labor so humans focus on context, tradeoffs, and fairness.Designing for the public means building for real settings: clinics with noise, fields with poor connectivity, and city services that must be accessible, secure, and easy to use. We explore digital twins, predictive maintenance, and crowdsourced reporting—and why each only works when the loop closes and action is visible. Along the way, we share a framework for people-first AI strategy: educate users, co-design with business owners, choose use cases where automation is safe and useful, and require explainability where stakes are high. The through line is constant: human judgment at the end of the loop, with AI as the force multiplier.If you care about ethical AI, public sector innovation, and data that leads to better outcomes—not just faster reports—you'll find practical steps you can apply today. Subscribe, share with a colleague who wrangles dashboards for a living, and leave a review with one question you want AI to help your community answer next.Send us a textCheck out "Protection for the Inventive Mind" – available now on Amazon in print and Kindle formats. The views and opinions expressed (by the host and guest(s)) in this podcast are strictly their own and do not necessarily reflect the official policy or position of the entities with which they may be affiliated. This podcast should in no way be construed as promoting or criticizing any particular government policy, institutional position, private interest or commercial entity. Any content provided is for informational and educational purposes only.

Episode OverviewIn this episode, Malcolm Hawker sits down with Jonathan Ong, Data Director for Mecklenburg County Public Health, to unpack how a small, resource-constrained team built a thriving data governance program from the ground up. They explore the practical realities of securing executive buy-in, building data literacy, and shifting governance from a compliance exercise to a catalyst for better decision-making. Whether you lead data in government or the private sector, this conversation offers a playbook for making governance simple, scalable, and impactful.Episode Links and ResourcesFollow Malcolm Hawker on LinkedInFollow Jonathan Ong on LinkedIn

In this conversation, Frank Verdeja shares his extensive experience in e-commerce and data management, discussing the importance of bridging the communication gap between technical teams and business stakeholders. He emphasizes the role of data integrity in e-commerce and the growing significance of data management systems in the age of AI. Frank expresses his enthusiasm for discovering new e-commerce companies and supporting startups in the Minneapolis area.TakeawaysFrank has 13 years of experience in e-commerce and data management.He emphasizes the importance of context in communication between technical and business teams.Data integrity is crucial for businesses of all types.Frank's company focuses on data governance and observability.He believes that understanding the value of data helps prioritize tasks.The rise of AI has made data management systems more important than ever.Customers prefer to own their data and need suitable platforms.Frank enjoys learning about new e-commerce companies in the Twin Cities.He has a passion for supporting startups and new players in the market.The conversation highlights the intersection of technology and business in e-commerce.Chapters00:00Introduction to E-commerce and Data Management02:57Bridging the Gap: Technology and Business Communication05:33Data Integrity and Its Role in E-commerce06:06The Future of E-commerce and Data Management06:06TC - Outtro All AV version 1.mp4