Podcasts about Data quality

Industrial Talk is onsite at Octave Live and talking to Tobias Pforr, Principal Business Strategist at Octave about "Unleashing technology to improve the utility markets. Overview Tobias Pforr discussed his role at Octave, a company focused on network intelligence systems for utilities. He highlighted the importance of accurate asset documentation and the challenges of managing dynamic elements like weather and electric vehicle consumption. Pforr emphasized the need for clean, high-quality data to ensure system resilience and actionable insights. He shared his background in utility operations and innovation, including his experience with a Swiss utility and the establishment of a startup incubator. Pforr also touched on the regulatory complexities and the evolving demands on utilities, stressing the importance of managing complexity and maintaining system reliability. Outline Barcelona Cyber Security Congress Announcement Scott introduces the Barcelona Cyber Security Congress, emphasizing its importance for cybersecurity professionals.The event is scheduled for November 3-5 in Barcelona, with networking opportunities and expert discussions.Scott mentions their own participation and encourages listeners to mark their calendars.Contact information for the event is available on Industrial Talk. Introduction to Industrial Talk Podcast Scott welcomes listeners to the Industrial Talk Podcast, celebrating industry professionals and their contributions.The podcast is broadcasting live from Octave Live in Austin, Texas, featuring various industry guests.Scott introduces Tobias Pforr, who has a challenging last name to pronounce. Tobias Pforr's Background and Role Tobias Pforr explains his name origin and its French and German roots.Tobias shares his experience with Hexagon, joining in 2022 and working with laser scanners and asset management.He discusses his transition to the enterprise software division and his current role at Octave.Tobias highlights his background in industrial engineering, MBA, and new business development. Challenges in the Utility Space Tobias describes his experience working at a utility in Switzerland, focusing on corporate development and innovation.He discusses the challenges utilities face, including energy price fluctuations, regulatory requirements, and market dynamics.Tobias mentions the startup incubator he established within the utility to foster innovation.He explains his role at Octave, focusing on networks and services, and the importance of understanding customer problems. Octave's Solutions for Utilities Tobias explains Octave's strong documentation solution, emphasizing the importance of accurate data.He discusses the transformation of network information systems (NIS) into network intelligence systems.Octave's solution helps utilities manage dynamic elements like weather, production surpluses, and electric vehicle consumption.Tobias highlights the need for actionable insights to keep the system resilient and functioning. Data Quality and Real-Time Updates Scott and Tobias discuss the challenges of maintaining accurate data in utilities.Tobias suggests using mobile devices to check the accuracy of asset locations in real-time.He emphasizes the importance of continuous data cleaning and validation during daily operations.Tobias shares personal experiences with operators and grid owners to illustrate the need for high-quality data. Impact of Storms and System Resilience Scott and Tobias discuss the impact of storms on utility systems and the need for real-time updates.Tobias explains the importance of prioritizing challenges and focusing on core value in utility operations.He highlights the role of documentation in ensuring system resilience and managing dynamic elements.Tobias emphasizes the need for a collaborative approach between developers and users to create effective solutions. Future of Utilities and Regulatory Requirements Scott and Tobias discuss the future of utilities, emphasizing the increasing complexity and regulatory requirements.Tobias explains the importance of managing complexity and ensuring system reliability.He highlights the challenges of remote work and the need for digital skills in utility operations.Tobias discusses the role of technology in helping utilities navigate regulatory requirements and maintain system resilience. Octave's Role in Utility Modernization Tobias explains Octave's role in helping utilities modernize their systems and manage dynamic environments.He discusses the importance of documentation and data quality in ensuring system reliability.Tobias highlights the need for a collaborative approach between developers and users to create effective solutions.He emphasizes the importance of continuous improvement and adapting to changing market dynamics. Conclusion and Contact Information Scott thanks Tobias for the insightful conversation and encourages listeners to connect with him on LinkedIn.Tobias provides his contact information and invites listeners to reach out for further discussions.Scott wraps up the podcast, emphasizing the importance of storytelling and human interaction in business success.The podcast concludes with a reminder to visit Industrial Talk for more insights and connections. If interested in being on the Industrial Talk show, simply contact us and let's have a quick conversation. Finally, get your exclusive free access to the Industrial Academy and a series on “Why You Need To Podcast” for Greater Success in 2026. All links designed for keeping you current in this rapidly changing Industrial Market. Learn! Grow! Enjoy! TOBIAS PFORR'S CONTACT INFORMATION: Personal LinkedIn: https://www.linkedin.com/in/tobias-pforr/ Company LinkedIn: https://www.linkedin.com/company/octaveintelligence/ Company Website: https://www.octave.com/ PODCAST VIDEO: https://youtu.be/OfvvE__546I THE STRATEGIC REASON "WHY YOU NEED TO PODCAST": OTHER GREAT INDUSTRIAL RESOURCES: NEOM: https://www.neom.com/en-us Hexagon: https://hexagon.com/ Arduino: https://www.arduino.cc/ Fictiv: https://www.fictiv.com/ Hitachi Vantara: https://www.hitachivantara.com/en-us/home.html Industrial Marketing Solutions:  https://industrialtalk.com/industrial-marketing/ Industrial Academy: https://industrialtalk.com/industrial-academy/ Industrial Dojo: https://industrialtalk.com/industrial_dojo/ We the 15: https://www.wethe15.org/ YOUR INDUSTRIAL DIGITAL TOOLBOX: LifterLMS: Get One Month Free for $1 – https://lifterlms.com/ Active Campaign: Active Campaign Link Social Jukebox: https://www.socialjukebox.com/ Industrial Academy (One Month Free Access And One Free License For Future Industrial Leader): Business Beatitude the Book Do you desire a more joy-filled, deeply-enduring sense of accomplishment and success? 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Tommy Cotter is Director of Data Products at Benzinga, a financial media company building the data infrastructure that sits behind trading platforms and investment apps used by millions of people daily. He's been navigating the shift to AI-assisted workflows in a space where speed and accuracy aren't just nice to have - getting it wrong has real consequences.In this episode, Peter and Dave talk with Tommy about what it actually looks like to build data products responsibly in a fast-moving AI environment. They get into where humans still need to be in the loop, how compliance has become a competitive signal, and why being nimble matters more than picking the perfect architecture from day one.Three things to take away from this conversation:Self-agency is real now. If you have a strong conviction about a product or problem, the barrier to building something has never been lower. That's a genuine shift from even five years ago.Security and compliance are no longer just internal concerns. In a world where AI startups spin up overnight, having invested in SOC2 or GDPR signals to customers that you're a legitimate, trustworthy operation. It's a market differentiator.Humans still belong in the system. Not everywhere, but in the right places. For low-risk, deterministic processes, let AI run. For anything client-facing or accuracy-critical, keep a human in the loop. Knowing the difference is the skill.If this conversation sparked something for you, send us your thoughts at feedback@definitelymaybeagile.com. And if you haven't already, hit subscribe so you don't miss the next one.

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.

Every organization relies on secure digital connections across suppliers, partners, and platforms. Yet many of the technologies that protect those connections were built for a world before quantum computing.  While practical quantum capabilities may still seem years away, the risks associated with them are already prompting concern, particularly as encrypted data collected today could potentially be decrypted in the future. For supply chain leaders, that creates a unique challenge: preparing for a technological shift that is still emerging while protecting information that remains valuable far into the future. In this episode of Supply Chain Now, hosts Scott W. Luton and Karin Bursa sit down with Akhilesh Agarwal, President of P2P Solutions and Technology at apexanalytix, and William McNeill, Vice President of Market Intelligence, for a conversation on what the quantum era could mean for supply chains. Together, they unpack the growing conversation around quantum computing, the implications of "harvest now, decrypt later" strategies, and why supply chain ecosystems may be particularly vulnerable due to the vast amounts of supplier, financial, and contractual data that move across them every day. As digital transformation continues to accelerate, they discuss why understanding emerging risks today may be just as important as preparing for the opportunities quantum technologies could unlock tomorrow. Jump into the conversation: (00:00) Intro (00:42) Quantum risks supply chain leaders must know (02:13) Meet apexanalytix quantum risk experts (03:35) Space exploration lessons for innovation (07:03) Apexanalytix protects supplier data at scale (10:16) Why they wrote The Quantum Paradox (10:46) Harvest now decrypt later threat (15:46) Where to start with quantum readiness (21:38) Four major supply chain impacts (24:39) Supplier risk extends beyond tier one (26:27) Why supplier collaboration matters (27:42) Building a three-to-five-year quantum plan (33:20) Audit technology stack and supplier data (48:47) White paper resources and next steps (51:37) Gartner recognition and key takeaway (54:48) Act now on quantum readiness   Additional Links & Resources: Connect with Akhilesh Agarwal: https://www.linkedin.com/in/akhilesh78/ Connect with William McNeill: https://www.linkedin.com/in/wimcneill/ Learn more about apexanalytix: https://www.apexanalytix.com/ Learn more about Qbiton: https://www.qbiton.com/ Learn more about our hosts: https://supplychainnow.com/about Learn more about Supply Chain Now: https://supplychainnow.com Watch and listen to more Supply Chain Now episodes here: https://supplychainnow.com/program/supply-chain-now Subscribe to Supply Chain Now on your favorite platform: https://supplychainnow.com/join Work with us! Download Supply Chain Now's NEW Media Kit: https://supplychainnow.com/media-kit/ WEBINAR- Amazon Supply Chain 101: Enabling efficiency and growth for businesses everywhere–and everywhere they sell: https://bit.ly/49r8N7D WEBINAR- The Expanding Role of Supply Chain Optimization Teams in Driving Business Impact: https://bit.ly/3PHRAAf WEBINAR- AI that moves at velocity: Cut through latency with agentic workflows: https://bit.ly/4x4626t This episode was hosted by Scott Luton and produced by Trisha Cordes, Joshua Miranda, and Amanda Luton. For additional information, please visit our dedicated show page at: https://supplychainnow.com/leader-briefing-quantum-risk-to-practical-action-1596 The content in this episode, including all audio, videos, visuals, and graphics, is the property of Supply Chain Now and is protected by copyright law. Unauthorized use, reproduction, distribution, modification, or re-uploading of this content in any form is strictly prohibited without explicit written permission from Supply Chain Now.For licensing inquiries or permissions, please contact us at production@supplychainnow.com© 2026 Supply Chain Now. All rights reserved. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Recorded live at EIC 2026 in Berlin, Jeff and Jim sit down with Thomas Zarnhofer, IAM Architect at SPAR-ICS, the IT unit of the SPAR Austria Group, which operates roughly 3,000 retail stores and 32 shopping centers across Central Europe. Thomas shares his experience leading a full IGA transformation from a decade-old on-premise system to a modern cloud-based platform. The conversation covers the shift from a contract-based to a person-based identity model, the importance of cleaning data before migration begins, a three-phase framework of Foundation, Migration, and Adoption, lessons learned from running two systems in parallel, and a look at how AI could make IGA predictive. The episode ends with Thomas's tips for visiting Austria.Connect with Thomas: https://www.linkedin.com/in/tzarnhofer/Connect with us on LinkedIn:Jim McDonald: https://www.linkedin.com/in/jimmcdonaldpmp/Jeff Steadman: https://www.linkedin.com/in/jeffsteadman/Visit the show on the web at http://idacpodcast.comTimestamps00:00 Introduction and EIC 2026 Setting02:00 Thomas's Identity Origin Story03:38 What Is SPAR-ICS?04:21 The Catalyst for IGA Modernization07:43 Contract-Based vs Person-Based Identity Models09:22 Consolidating Master Data Sources11:39 Data Quality and Attribute Ownership13:34 Partnering with HR for Clean Data16:43 Data Analysis: Why They Chose Excel Over AI17:53 Clean Your Data Before You Migrate18:23 The Three Phases: Foundation, Migration, Adoption20:12 Driving Adoption Across the Organization21:10 Running Two Systems in Parallel22:47 Challenge Everything vs Lift and Shift27:23 Surprises in the Cloud IGA Journey29:02 Testing Requirements in the Cloud29:51 AI and the Future of IGA32:25 AI Chatbots and Role Discovery35:30 Scoping Business Role Visibility36:06 Life Outside IAM: Travel and Austria TipsIAM, IGA, Identity Governance, IGA Migration, On-Premises to Cloud, Identity Model, Contract-Based Identity, Person-Based Identity, Master Data, Data Quality, HR Integration, Joiner Mover Leaver, Cloud IGA, SPAR-ICS, Retail IAM, EIC 2026, AI in IGA, Predictive IGA, Role Management, Access Governance, IDAC, Identity at the Center, Jeff Steadman, Jim McDonald, Thomas Zarnhofer

On this episode of The Buzz, Scott Luton is joined by special co-host Dr. Muddassir Ahmed and special guest Anthony Reeves, Vice President of Global Brand & Creative at Kohler and author of Eat the Donkey: Why Great Companies Embrace Discomfort. Together, they explore the realities of AI adoption, decision-making optimization, innovation, leadership, and what separates organizations that thrive from those that struggle to keep pace. As supply chains continue to evolve in the age of AI, organizations face critical decisions about technology adoption, data quality, change management, and leadership. Scott, Muddassir, and Anthony examine why many AI initiatives fail, what companies can learn from both successes and setbacks, and why strong decision-making remains one of the most valuable competitive advantages. The conversation also explores the growing importance of human connection, brand differentiation, organizational culture, and the willingness to embrace discomfort in pursuit of long-term growth. Drawing on experiences from Amazon, Kohler, Starbucks, and other global brands, Anthony shares powerful lessons on innovation, leadership, and staying true to what makes an organization unique. Key Takeaways: AI success depends as much on adoption, change management, and leadership as it does on technology. High-quality, contextualized data remains the foundation for effective AI implementation. Organizations must learn from failed initiatives just as much as successful ones. Soft skills, emotional intelligence, and human connection will become increasingly valuable as AI handles more routine work. Strong brands remain differentiated by purpose, customer experience, and authenticity—not technology alone. Great leaders make difficult decisions early rather than delaying action until opportunities have passed. Whether you're leading a supply chain transformation, evaluating AI investments, or building a stronger organization, this episode offers practical insights from leaders who have navigated innovation at the highest levels. You'll walk away with actionable advice on decision-making, change management, leadership, and creating organizations that can thrive amid constant disruption. Additional Links & Resources: Guest LinkedIn Profile: https://www.linkedin.com/in/anthonyreeves/ Guest Instagram Handle: @anthony.j.reeves Guest Company Website: anthonyreeves.co APL Logistics: https://www.apllogistics.com/ With That Said: https://bit.ly/WTS-7JUN2026 The Corner Market: https://bit.ly/The-Corner-Market Exclusive: Starbucks scraps AI inventory tool across North America: https://reut.rs/4vuPSkR 4 Supply Chain and AI Predictions for 2026: https://bit.ly/AI-Predictions-2026 AI Strategy Takes A Data Foundation That Cleansing Can't Provide: https://bit.ly/Paul-Noble-Gartner2026-Takeaways 5 Signs Your Supply Chain Has Outgrown How It's Managed Today: https://bit.ly/5-signs-your-SC-has-outgrown-mgmt Eat the Donkey: https://www.amazon.com/dp/B0G97CHK9F When Safety Technologies Backfire and How Managers Can Prevent It: https://bit.ly/When-Safety-Tech-Backfires Upcoming Live Programming:  https://supplychainnow.com/upcoming-live-programming/ Supply Chain Now Resource Hub: https://supplychainnow.com/resource-hub/ Connect with Anthony on LinkedIn: https://www.linkedin.com/in/anthonyreeves/ SCMDOJO: https://sensei.scmdojo.com/ Connect with Muddassir on LinkedIn: https://www.linkedin.com/in/muddassirism/ Follow Scott on LinkedIn: https://www.linkedin.com/in/scottwindonluton/ WEBINAR- Amazon Supply Chain 101: Enabling efficiency and growth for businesses everywhere–and everywhere they sell: https://bit.ly/49r8N7D WEBINAR- The Expanding Role of Supply Chain Optimization Teams in Driving Business Impact: https://bit.ly/3PHRAAf WEBINAR- AI that moves at velocity: Cut through latency with agentic workflows: https://bit.ly/4x4626t This episode was hosted by Scott Luton and Dr. Mudassir Ahmed. For additional information, please visit our dedicated show page at: https://supplychainnow.com/buzz-ai-adoption-brand-differentiation-embracing-comfort-1595 The content in this episode, including all audio, videos, visuals, and graphics, is the property of Supply Chain Now and is protected by copyright law. Unauthorized use, reproduction, distribution, modification, or re-uploading of this content in any form is strictly prohibited without explicit written permission from Supply Chain Now.For licensing inquiries or permissions, please contact us at production@supplychainnow.com© 2026 Supply Chain Now. All rights reserved. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Scott McKinley, Founder & CEO of Truthset, discusses the state of data quality, identity, and measurement in digital advertising. Scott shares why the industry continues to prioritize scale over accuracy, how data quality deteriorates throughout the supply chain, and why advertisers need to rethink legacy metrics like reach and CPMs. The conversation also explores identity, walled gardens, authentication, and the future of the open internet. Takeaways Data accuracy often declines significantly as data moves through the ad tech supply chain. Scale is frequently prioritized over quality, leading to inefficient advertising spend. Advertisers should focus on precision and outcomes rather than reach alone. Authentication is critical to improving identity and publisher monetization. Independent measurement remains essential for trust and accountability in advertising. Walled gardens continue to outperform because of durable identity systems. IP addresses are an unreliable long-term replacement for cookies. The open internet must improve identity infrastructure to remain competitive. Chapters 00:00 Introduction to Scott McKinley and Truthset 01:05 From Olympic cyclist to ad tech entrepreneur 03:01 The trust crisis in advertising and lessons from sports 05:25 Why advertising lacks accountability and regulation 07:00 Nielsen's role in independent measurement 09:00 Why Scott founded Truthset 11:17 Common misconceptions about data accuracy 14:20 The industry's obsession with scale over quality 17:53 Why reach is becoming an outdated metric 19:13 Signal loss, walled gardens, and measurement challenges 23:16 The future of identity in advertising 25:34 Why authentication is the path forward 25:51 The biggest misconception about IP addresses 26:43 What the open internet must do next 28:05 Closing thoughts Guests: AdTechGod Learn more about your ad choices. Visit megaphone.fm/adchoices

Send us Fan MailEpisode 79 of the "Everything Except The Law" podcast has arrived! This time we're speaking with Ben Glass, the founder of Great Legal Marketing, LLC.Ben Glass has been practicing law for over three decades — and for 21 of those years, he's also been running Great Legal Marketing, one of the most trusted organizations in the legal industry for helping attorneys build firms that actually work for their lives.In this episode of Everything Except the Law, host Nick Werker sits down with Ben to talk about how GLM started, why the fundamentals of marketing haven't changed, and what the most important topics are inside GLM's mastermind rooms right now: people, AI, and digital marketing.But this conversation goes deeper than marketing. Ben and Nick trade stories about CrossFit, triple bypass surgery, refereeing youth soccer, and the personal journaling practice that helped Nick transform his own life. Ben opens up about what it really means to build a law firm around happiness, and why the client isn't actually at the top of the priority list.If you've ever wondered whether it's possible to enjoy running a law firm, this episode is for you.Chapters:0:00 Intro & Nick's CrossFit Origin Story2:05 Ben's Triple Bypass & CrossFit Comeback3:37 Soccer Refereeing, Running 4 Miles a Game & Protecting Young Refs5:23 How Great Legal Marketing Got Started 21 Years Ago8:33 Why Law Firms Are an Easy Target for Vendors11:08 How GLM Helps Lawyers Evaluate & Hold Vendors Accountable13:44 The Right Question to Ask Before Spending on Google Ads15:22 The Top 3 Topics at GLM: People, AI & Digital Marketing19:58 Data Quality, Call Tracking & The 83% Referral Rule21:38 The Best First Step for a New Law Firm: Build Your Relationships22:01 Direct Mail, Newsletters & Putting Others First27:31 "Is Someone Wandering Into Someone Else's Office Right Now?"31:36 The Fish Rots at the Head: Culture Starts With the Owner33:12 How to Increase Your Happiness as a Law Firm Owner36:28 Nick's Journaling Journey & Personal Transformation39:06 Mastermind Groups, Accountability & Bragging Rights42:32 How to Join Great Legal Marketing + Renegade Lawyer Marketing BookGuestBen Glass — Founder, Great Legal Marketing | Managing Attorney, Ben Glass Lawgreatlegalmarketing.com | glmsummit.com | benglasslaw.comTopics Covered-How Great Legal Marketing started 21 years ago — and why the appetite was already there-Why the fundamentals of marketing haven't changed and never will (it's all human psychology)-GLM's model: vendor-agnostic education, not a product pitch-The top three mastermind topics right now: people/culture, AI, and digital marketing-Why 83% of Ben Glass Law's revenue comes from human referrals — and what that means for your firm-The best first move for a new lawyer: direct mail, newsletters, and genuine curiosity about others-Why the right question isn't 'how much should I spend on Google Ads?'-Ben's framework for building a firm that fits your life: vision, permission, and people-The four-quadrant exercise for eliminating the things you hate-Why the client is third on Ben's priority list — not first-Mastermind groups: accountability, bragging rights, and seeing that everyone's a little broken-Ben's soccer referee work and his fight to restore sanity to youth sports-Ben's triple bypass surgery and his return to CrossFit-Nick's personal transformation story and the TikTok journal that started it allPeople & Resources MentionedBen Glass — Ben Glass Law (benglasslaw.com) / Great Legal Marketing (greatlegalmarketing.com)Brian Glass — Ben's son and co-leader at Ben Glass Law and Great Legal MarketingGyi Tsakalakis — AttorneySync; presenter at GLM's upcoming boot campConrad Saam — Mockingbird Marketing; presenter at GLM's upcoming boot campDan Kennedy — marketing legend; coined "the money is in the list"Samy Chong — mindset coach (Toronto); Ben's coach for ~12 yearsNick Werker — host, Everything Except the Law PodcastResources:Renegade Lawyer Marketing — Ben Glass's book (available on Amazon)greatlegalmarketing.com — join the email list, find upcoming eventsglmsummit.com — Great Legal Marketing Summit (annual, October)Ben Glass Law — benglasslaw.com (ERISA long-term disability & personal injury, Northern Virginia)Everything Except The Law is a part of the Answering Legal Podcast Network. Learn more about the show here: https://tinyurl.com/4xjerw3w  Interested in learning more about Answering Legal? Book an appointment to speak with us here: https://tinyurl.com/4c9h8xb8 You can also give us a call at 631-212-1899.This podcast is produced and edited by Joe Galotti. You can reach Joe via email at joe@answeringlegal.com.

In this episode of Treasury Leaders, Host Philip Costa Hibberd, Founder of Automation Boutique, talks with Mariam (Petrosyan) Halfhide, Principal Consultant, Data & AI Strategy at Xebia, to explore how AI strategy, data governance, and organisational readiness are reshaping the future of finance and treasury.Mariam shares practical insights on why many organisations struggle to move beyond AI experimentation, the importance of building strong data foundations, and how finance leaders can bridge the gap between technology and business decision-making. She also discusses the growing role of AI in forecasting, operational efficiency, and strategic planning, while highlighting why human judgment and communication remain essential.Whether you're a treasury professional, finance leader, or simply interested in AI transformation, this episode offers valuable lessons on how businesses can adopt AI more effectively and create long-term value.What You'll Learn in This Episode:AI Strategy & Business Alignment: Why successful AI adoption starts with understanding business problems, not just implementing technology.Data Foundations Matter: How poor data quality and fragmented systems limit the effectiveness of AI initiatives.The Human Side of AI: Why communication, collaboration, and organisational readiness are critical for successful transformation.AI in Finance & Treasury: How AI can support forecasting, analytics, automation, and decision-making across finance functions.From Experimentation to Execution: Why many companies remain stuck in pilot phases and what is needed to scale AI successfully.Episode Breakdown with Timestamps:[00:00] – Introduction[01:40] – Mariam's Background in Data & AI Strategy[04:15] – Why AI Adoption Often Fails in Organisations[08:22] – The Importance of Data Quality and Governance[12:35] – Aligning AI with Business Objectives[17:10] – AI Use Cases in Finance and Treasury[22:48] – Moving Beyond AI Experimentation[27:55] – Organisational Readiness and Change Management[32:20] – Human Judgment vs AI Decision-Making[36:45] – The Future of AI in Treasury and Finance[40:10] – Final Advice for Finance LeadersFollow Our Guest: LinkedIn: https://www.linkedin.com/in/mpetrosyan/Xebia: https://www.linkedin.com/company/xebia/Follow Treasury Leaders:Website: https://corporate-treasury-101.com/LinkedIn: https://www.linkedin.com/company/treasury-leaders/Follow Our Hosts:Hussam Ali on LinkedIn: https://www.linkedin.com/in/hussam-r-ali/Guillaume Jouvencel on LinkedIn: https://www.linkedin.com/in/guillaume-jouvencel/Jan-Willem Attevelt on LinkedIn: https://www.linkedin.com/in/attevelt/Philip Costa Hibberd on LinkedIn: https://www.linkedin.com/in/philip-costa-hibberd/GHA Marketing Website: https://ghapodcast.com/Automation Boutique Website: https://automationboutique.com/ -----------------------------------------------------------------------Get $100 off any AFP product, including their CTP Exam Prep Platform, using our discount code! Find this and More on our partner's pagehttps://corporate-treasury-101.com/partners-page/

In this episode of Future Finance, Paul Barnhurst and Glenn Hopper speak with Ali Hussain, CEO and co-founder of Tabs, an AI-powered platform revolutionizing finance workflows. Ali discusses the limitations of legacy finance systems, the challenges of modern revenue models, and how AI is automating the full contract-to-cash cycle for finance teams.Ali Hussain is the CEO and co-founder of Tabs, where he leads the development of AI-native solutions designed to automate the billing, collections, and revenue recognition process. With a background spanning product leadership at Google, strategy consulting at BCG, and public policy, Ali is at the forefront of the next wave of finance technology.In this episode, you will discover:How automation is transforming the finance sector, particularly revenue managementThe importance of structured data for effective financial solutionsWhy traditional finance systems struggle with modern revenue modelsHow Tabs is automating billing, collections, and revenue recognitionThis episode provides insights into how automation is reshaping finance, helping businesses manage revenue models and streamline financial tasks. Ali Hussain shares his perspective on simplifying finance workflows and the direction the industry is heading. Follow Ali:Website: https://www.tabs.comLinkedin: https://www.linkedin.com/in/ali-hussain786/ YouTube: https://www.youtube.com/@tabsplatform 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:[02:00] – Ali's Background and Tabs' Growth[03:00] – Automation in Finance[05:00] – Agents in Finance Workflows[08:00] – Data Quality in Finance[10:00] – Limitations of LLMs in Finance[12:00] – Agents vs Automation[15:00] – The Future of Finance[17:00] – Managing Usage-Based Pricing[21:00] – Closing Thoughts

In this episode, we dive into how smart pricing helps e-commerce brands boost profits and scale growth. Felix Hoffmann, co-founder and CEO of 7Learnings, shares how his predictive pricing models help businesses move beyond simple rules and gut feelings to find the perfect price for every product. He also reveals strategies for managing marketplace complexity, reducing overstock, and using financial goals to steer automated decision-making. Topics discussed in this episode:  How rule-based pricing creates unmanaged business complexity. Why matching competitor prices leads to a market race to bottom. What predictive pricing does using unlimited cloud compute. Why tracking transaction-level costs is vital for profit. How AI identifies different price elasticities across channels. What role weather and attribute data play in predictions. Why high-quality data is the gatekeeper for AI success. How A/B testing proves profit uplifts of over 100 percent. What strategic trade-offs exist between growth and margin. Why AI pricing is now a requirement for market survival. Links & ResourcesWebsite: https://7learnings.com/LinkedIn: : https://www.linkedin.com/in/felix-hoffmann-7learnings/ LinkedIn: https://www.linkedin.com/company/7learnings/Get access to more free resources by visiting the show notes at https://tinyurl.com/us6eab7kI'd love your feedback. Tap the the link to send me a text.______________________________________________________LOVE THE SHOW? HERE ARE THE NEXT STEPS!Follow the podcast to get every bonus episode. Tap follow now and don't miss out!   Rate & Review: Help others discover the show by rating the show on Apple Podcasts at https://tinyurl.com/ecb-apple-podcasts   Join our Free Newsletter: https://newsletter.ecommercecoffeebreak.com/   Support The Show On Patreon: https://www.patreon.com/EcommerceCoffeeBreak   Partner with us: https://ecommercecoffeebreak.com/partner-with-us/

In this episode of The Responsive Lab, Carly and Scott sit down with Erin Stender, CMO at Omatic. With nearly a decade at mission-driven tech companies like Classy and Neon One, Erin brings a unique perspective from both the technology side and the nonprofit board member lens. You'll hear about:* Why nonprofits feel both excitement and trepidation about AI, and how both responses are completely valid* How incomplete or inaccurate data creates costly missed opportunities, from major donors not receiving year-end appeals to lapsed donors getting acquisition-level messaging* Why your CRM serves as the beating heart of your tech ecosystem and how integrations make it more powerful* How to start cleaning up your data without feeling overwhelmed by beginning with one specific decision you're trying to make* Using AI to identify blind spots by asking what you don't know rather than just automating what you do know* Why the mindset shift around AI is often underestimated and how teams can adopt it togetherLinks from the episode:* Connect with Erin on LinkedIn: https://www.linkedin.com/in/erin-hall-stender-2644264/* Learn more about Omatic: https://omaticsoftware.com/Looking for technology that helps you build deeper donor relationships with less work from your team? Learn more at virtuous.org.

Send us Fan MailAI in nonprofit fundraising strategy is transforming how organizations operate—but using it incorrectly can damage donor relationships and trust. In this conversation, Katie Gaston of Bloomerang opens the box with practical guidance on how to use AI effectively while avoiding the most common pitfalls.Nonprofit professionals are increasingly turning to AI tools for donor research, reporting, and communications. The opportunity is clear: faster workflows, better insights, and increased capacity. But as Katie explains, AI is not a replacement for human judgment—it's a tool to enhance it. “AI should be a supportive arm… but it should never replace your judgment as a fundraiser.”From donor asks to personalized stewardship, the human connection remains at the core of successful fundraising. AI can prepare you for meetings, surface insights, and even recommend strategies—but it cannot replicate the emotional intelligence required in critical moments.This episode also addresses key operational risks. Sending AI-generated content without review, relying too heavily on automated insights, and failing to maintain clean data can all create serious challenges. As Katie reminds us, “The quality of your data is what AI will know—garbage in, garbage out.”You'll also learn how AI can dramatically improve efficiency—reducing hours of reporting work to minutes—while freeing your team to focus on relationship-building and strategic thinking.The takeaway? AI isn't replacing fundraising—it's redefining how effective fundraisers work. 00:00:00 Introduction to AI in Fundraising00:03:10 Meet Penny: AI Fundraising Assistant00:06:00 Why AI Should NOT Make Donor Asks00:09:00 Reviewing AI Output to Avoid Risk00:11:30 AI vs. Human Donor Knowledge00:14:30 Data Quality and CRM Accuracy00:17:30 Protecting Your Nonprofit Voice00:22:00 Personalization vs. Automation in Donor Care00:25:45 Using AI to Save Time and Increase Capacity00:27:00 How Fast Should Nonprofits Adopt AI?00:30:00 Final Thoughts on AI Strategy#TheNonprofitShow #NonprofitEfficiency #FundraisingStrategyFind us Live daily on YouTube!Find us  Live daily on LinkedIn!Find us Live daily on X: @Nonprofit_ShowOur national co-hosts and amazing guests discuss management, money and missions of nonprofits!  12:30pm ET   11:30am CT  10:30am MT  9:30am PTSend us your ideas for Show Guests or Topics: HelpDesk@AmericanNonprofitAcademy.comVisit us on the web:The Nonprofit Show

Charlotte Ledoux est une experte Data & AI Gouvernance, elle accompagne de très belles boîtes comme Pernod Ricard, Disney ou Printemps. 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 :

As sanctions risk intensifies and regulatory expectations grow more complex, financial institutions are under increasing pressure to strengthen and defend their compliance programs. In this episode of Ahead of the Curve, we sit down with Greg Pinn of Abrigo and Sarabjeet Singh, Founder and CEO of RZOLUT, to explore how data quality, risk visibility, and AI are reshaping financial crime compliance.Listen in to learn how expectations around data have evolved, why a unified view across sanctions, PEPs, and watchlists is critical, and how banks and credit unions can responsibly adopt AI while maintaining trust, transparency, and defensibility.About the guests:Sarabjeet Singh is the Founder, CEO, and Chief Product Architect of RZOLUT, bringing more than three decades of global experience in financial crimes compliance. With a strong Big 4 background, including leadership roles at KPMG and EY, his expertise spans AML, KYC, sanctions, and regulatory transformation. Sarabjeet has designed and operated large compliance Centers of Excellence for global banks and FinTechs, including multi-year programs for Top Wall Street banks and global payment providers, combining operational scale, machine learning, and automation. His teams have built and maintained global AML datasets for more than a decade. Through RZOLUT, he enables institutions to leverage high-quality data and adopt enterprise-grade screening and due diligence aligned with modern regulatory expectations.Greg Pinn has spent over 20 years building software products and data solutions to solve AML and financial crime challenges for global financial institutions. His work has included developing sanctions, watchlist, and PEP screening solutions, cryptocurrency compliance tools, adverse media offerings, and advanced risk data products. At Abrigo, Greg leads the development of innovative scan solutions that help banks and credit unions confidently navigate regulatory and operational challenges.Helpful links:Rzolut | Compliance, ConnectedWhat happens when sanctions screening failsThe new sanctions reality: Why community financial institutions need enterprise-grade screening Modernizing sanctions screening for U.S. community financial institutions - Abrigo

Recorded live at Shoptalk Spring 2026 by host Isaac Morey, the conversation features Amera Khalil, Director of Strategic Account Management at Commerce — the parent brand behind Feedonomics, BigCommerce, and MakeSwift. Together, they cover what brands actually need to do to stay visible as AI-powered discovery takes over consumer behavior. It's a practical, no-fluff conversation that's worth your time whether you're running a lean SMB operation or managing enterprise-level feeds.Key TakeawaysData quality is the foundation. Before any AI strategy works, your product feeds need accurate titles, descriptions, sizes, and attributes.Enriched data isn't one-size-fits-all. Enrichment requirements vary by brand, category, and channel — and they need to match today's conversational search queries.Agentic commerce is already operational. Commerce built a fully functional agentic checkout experience for PacSun on Perplexity in under 30 days.Don't deploy AI for AI's sake. Without a clear business objective, AI implementation creates confusion rather than results.SMBs need to act now. LLM visibility isn't exclusive to enterprise brands — smaller businesses can start preparing today.Human oversight isn't optional. Quality assurance guardrails protect brand integrity and keep AI-generated content on point.Have AI conversations out loud. Brands strategizing behind closed doors miss partners who may already have the solutions they need.Episode SummaryAmera opened by describing Commerce's three-brand structure. Feedonomics handles intelligent product feed management, BigCommerce powers flexible e-commerce experiences, and MakeSwift enables agile front-end design. She described Feedonomics' core value simply: taking complex data, making it clean and structured, and distributing it intelligently across every relevant channel.From there, Isaac asked the big question: how is AI changing e-commerce? Amera's answer was direct. The traditional marketing funnel — performance ads, tracking, attribution models — is collapsing. Consumers are now using Perplexity, ChatGPT, and Claude not just for research but for actual purchase decisions. "The biggest change in e-commerce," she noted, "is the preparation to be visible on these LLMs while maintaining the quality of your data."She broke agentic commerce readiness into clear layers. First, brands need solid foundational data — accurate product titles, descriptions, brand names, sizing. Without that, nothing else works. On top of that, brands need enriched data that responds to how people actually search today. Nobody's typing "suitcase" anymore. They're saying something like, "I'm going on a trip, I want something light, and I tend to overpack." Product data has to meet that kind of specificity.Interestingly, she was candid about the complexity of getting onto LLM channels: "Just because you're a brand doesn't mean that your feed is going to be accepted." Approval processes are real hurdles, and the backend requirements — syncing inventory, enabling checkout, integrating payments — go well beyond what most marketing teams expect.The PacSun case study was the episode's standout moment. Commerce built a complete agentic checkout experience for PacSun on Perplexity in under 30 days, during the holiday season. Shoppers could find PacSun jeans, select their size, and check out via PayPal — receiving a confirmation email from PacSun directly. "This is thrilling," Amera said, "because it's changed the way that we are looking at our expectations as consumers."On AI risks, she stressed quality assurance. Feedonomics uses internal benchmarking systems that flag AI-generated content not meeting brand standards before it goes live. She also flagged a generational nuance: Gen Z consumers can detect cold, scripted AI content, and they don't respond well to it. Adjusting content based on audience expectations isn't a nice-to-have — it's essential.For SMBs, her advice was to start with a data audit. Centralize your assets, identify missing fields, and find a feed partner who can submit requests to LLMs on your behalf. As she put it, "even if you're small, medium, or you're the big kahunas in the industry, you have to be present and you certainly have to be visible."Final ThoughtsIn this new era, AI agents act on behalf of shoppers — searching, comparing, and even checking out across multiple channels, often without ever visiting a merchant's website. These AI-driven experiences are seamless, contextual, and increasingly the default for how consumers interact with commerce online.  Amera's message throughout this episode is clear: preparation beats hesitation every time.So here's the question worth sitting with — if your brand's data isn't ready for an agent to read it, how feed-y is your commerce strategy for what's already here?This has been produced in cooperation with Content Cucumberhttps://www.contentcucumber.com/Chapters00:00:00 — Introducing Amera Khalil and Commerce00:00:22 — What Feedonomics Actually Does00:00:46 — BigCommerce and MakeSwift Explained00:01:09 — How AI Is Changing E-Commerce00:01:59 — How Consumers Are Using LLMs to Shop00:02:38 — The Rise of the Brand Agent as Consumer00:03:13 — How Brands Can Prepare for Agentic Commerce00:03:50 — Why Data Quality Is the Foundation00:04:28 — What Data Enrichment Actually Means00:05:15 — Matching Long-Tail Search Queries with Enriched Feeds00:05:52 — AI Risks and the Humans-in-the-Loop Element00:06:25 — Quality Assurance and Guardrails for AI Content00:07:15 — How Brands Are Adapting — Real Customer Journeys00:08:05 — Enterprise Brands Going Live on LLMs00:09:33 — The PacSun Agentic Checkout Story00:12:58 — Advice for Small and Medium-Sized Businesses00:14:54 — Why Every Brand Needs to Be Visible on LLMs by 202800:15:45 — The Feedonomics Framework for Readiness00:16:02 — Amera's Hot Take from Shoptalk00:16:56 — Final Thoughts and What's Getting Exciting Again

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, Wendy Gonzalez, CEO of Sama, breaks down why high-quality data, human-in-the-loop systems, and clear evaluation standards are essential for building AI that actually works at scale.Wendy shares how enterprises train, validate, and improve AI models in the real world, from autonomous vehicles to e-commerce recommendations and generative AI. She also explains why dirty data, edge cases, and weak quality standards can quietly kill AI performance, trust, and adoption.You'll also hear a sharp conversation on responsible AI, model bias, regulation, language inclusion, and why founders and innovation leaders need to define what “good” looks like before shipping AI products.If you are building, buying, or leading AI initiatives, this episode offers practical insight on AI deployment, trustworthy AI, training data, model accuracy, and the human systems behind production-grade machine learning.Key topics

Send me a messageWhat if the real reason transformation stalls isn't the tech, but the fact that everyone is making decisions with a different rubric?And what happens when you start training AI on processes built 30 years ago?In this episode of the Resilient Supply Chain Podcast, I'm joined by Don Mahoney, Global Head of Products and Innovation at SNP Group. Don has had a ringside seat to some of the world's largest enterprise transformations, and he brings a sharp perspective on what actually drives supply chain resilience, business agility, and better decision-making when the pressure is on.We get into why transformation is no longer a one-off event, but an ongoing capability, and why so many firms still get trapped between “lift-and-shift” modernisation that delivers weak ROI and greenfield ambitions that exceed what the business can absorb. You'll hear how Don thinks about the sweet spot in the middle, why organisational change is often the real constraint, and why “your plan, my plan, our plan” matters far more than most people admit.You might be surprised to learn that 80-something percent of enterprise data sits outside ERP systems, much of it unstructured, which makes data quality, visibility, and trust far more strategic than they look on a slide. We also break down one of my favourite lines in the episode: the shift from running a transaction machine to building a decision machine. That's where the real value is.

We're living in an age where new technology promises to improve everything with faster decisions, smarter workflows, and better outcomes. But behind that promise lies a quieter reality, and that is many organisations have that ambition, but readiness often lags behind. In this episode of Don't Panic! It's Just Data, host Christina Stathopoulos, Founder of Dare to Data, speaks with Pascal Bensoussan, Chief Product Officer at Ivalua.In this episode, they look at the growing excitement around AI and the reality many organisations face when trying to use it. While ambition is high, readiness often falls short. Focusing on procurement, the conversation explores why many AI initiatives struggle to move beyond early stages and what's needed to turn that ambition into real, measurable value.Data: The Backbone of AISuccessful AI depends on high-quality, unified data. Fragmented sources, unclean data, and siloed systems make it difficult to build reliable AI applications. As Bensoussan explains: “Fix your data foundation. Without that, you can't get started with AI. Don't jump into an AI frenzy hoping it will sort itself out. First, you need a unified transactional and master data model that captures relationships, ensures semantic coherence, and creates a system of truth you can trust.”A unified data model enables AI to work effectively, increasing both its success rate and depth. Organisations should start with use cases that provide tangible value rather than trying to do everything at once. Governance frameworks, monitoring, and maintenance are critical to ensure reliability, security, and meaningful outcomes. Employee trust is another key factor. Users need confidence in AI outputs, and organisations must address scepticism about how AI might impact roles. Building that trust often requires broader cultural change, which can be one of the hardest barriers. Many teams are used to traditional methods and resist adopting new technologies. By combining solid data foundations with practical, focused use cases and a clear strategy, companies can guide teams through this change, ensuring AI initiatives don't stall and deliver measurable results.Understanding AI Ambition vs. AI ReadinessAmbition and readiness are not the same. AI ambition refers to the enthusiasm organisations have for integrating AI into operations, driven by the promise of efficiency and insight. AI readiness, on the other hand, measures whether an organisation can actually deploy AI effectively at scale.According to MIT research, 95 per cent of enterprise AI projects fail to move from proof of concept to production. Bensoussan calls this the “GenAI divide”: “The ambition is there because the promise is incredible, but the readiness is often missing because often the foundation is cracked.”Without a clear strategy or roadmap, even organisations with abundant resources can struggle to implement AI successfully. Starting with targeted, achievable use cases helps teams gain confidence, build trust, and generate measurable results before scaling more widely.AI in ProcurementProcurement provides a unique lens for understanding AI adoption. Positioned at the intersection of data, compliance, risk, and finance, it offers significant opportunities but also considerable complexity. One major challenge is that unstructured data like contracts, risk assessments, and supplier communications must be integrated with transactional records, a process that is often time-consuming and difficult. Fragmented systems only add to the challenge, limiting AI's ability to deliver meaningful, actionable insights.Bensoussan emphasises that seeing the entire process from supplier discovery to payment is essential. A comprehensive view ensures that AI-driven insights are reliable, actionable, and fully traceable, allowing organisations to understand why specific decisions are made and to make more strategic choices.AI in procurement is not about replacing humans; it is about augmenting them. By automating mundane tasks like data retrieval and report generation, professionals can focus on higher-value work, strategic thinking, and deeper evaluation. AI also enables richer insights, helping teams develop more effective strategies and make informed decisions. By addressing data challenges, building trust, and starting with targeted use cases, organisations can turn AI ambition into measurable value. With the right preparation and focus, AI can strengthen procurement operations, enhance decision-making, and unlock new levels of efficiency.For more information, visit www.ivalua.comTakeawaysAI ambition vs. readiness in organisationsBarriers to AI adoption: culture, strategy, data, trust, governanceImportance of unified data models for AI effectivenessPractical AI applications in procurement: sourcing, contracts, invoicingHuman-AI collaboration and the future of work in procurementChapters00:00 AI Ambition vs. Readiness05:02 The Procurement Landscape and AI Adoption09:10 Data Foundations for AI Success13:03 Unified Data Models in Procurement16:43 The Human Element in AI Integration25:57 Real-World Applications of AI Agents32:22 Key Takeaways for Leaders in AI Adoption

In this HIMSS26 recap episode, Tony Schueth is joined by Brian Bamberger, Vanessa Candelora, and Brian Dwyer to unpack what they heard, saw, and debated after a week on the ground in Las Vegas. Rather than focusing on announcements or product launches, the conversation centers on the signals emerging across sessions, client meetings, and hallway conversations and what those signals suggest about where health IT is headed. The discussion opens with reflections on a keynote from former Tesla president Jon McNeill, which challenged attendees to rethink entrenched healthcare processes. While initial skepticism about an outsider perspective was high, the panel agrees the message resonated. Meaningful progress may require stripping workflows down to their fundamentals and rebuilding them with simplicity in mind. That theme carries throughout the episode, particularly as the group connects it to persistent challenges like prior authorization and administrative burden. From there, the conversation shifts to the dominant presence of AI at HIMSS26. Unlike prior years, where AI often felt theoretical, the panel notes a clear shift toward practical applications embedded directly into workflows. Examples like prior authorization automation and clinical summarization highlight real efficiency gains, but the group is quick to point out that AI is only as good as the data behind it. Concerns around data quality, bias, and trust are no longer side conversations. They are central to whether AI can scale in meaningful ways. As one theme emerges repeatedly, it is that the industry may have rushed ahead with AI excitement before fully solving for foundational data challenges. That leads into a deeper discussion on interoperability. The panel describes a noticeable transition from “interoperability as a vision” to “interoperability as infrastructure.” Organizations are no longer asking what connected data exchange could look like. They are now actively building the components required to support it. This includes identity frameworks, consent models, trust networks, and governance structures. While progress is real, the work is also proving to be more complex than anticipated, with many stakeholders still grappling with how these pieces fit together at scale.  The conversation also explores how these shifts are playing out across different stakeholders. From a payer and vendor perspective, Dwyer highlights that many organizations have moved firmly into execution mode, particularly with regulatory deadlines like CMS-0057 on the horizon. However, there is still uncertainty about what comes next, especially when it comes to scaling beyond compliance into true business transformation. For life sciences, Bamberger notes that strategy is largely set, but execution remains uneven. Efforts are increasingly focused on improving data capture within EHRs, enabling more efficient prior authorization, and addressing complex use cases like rare disease diagnosis, where fragmented data can significantly delay care. Several moments in the discussion bring the conversation back to foundational issues that continue to slow progress. Patient identity, data quality, and structured versus unstructured data all emerge as persistent barriers. The group emphasizes that without resolving these challenges, even the most advanced AI tools will fall short. Initiatives like FHIR accelerators and broader industry collaborations are seen as critical to closing these gaps, but there is still work to be done to move from standards development to consistent, real-world implementation. The panel also spends time on emerging areas of focus, including price transparency and rural health transformation. Candelora shares observations from her HIMSS presentation, noting growing engagement and more nuanced questions from stakeholders, signaling that the industry is beginning to take these efforts more seriously. Meanwhile, rural health funding is creating both opportunity and urgency, with stakeholders recognizing that interoperability and data sharing will be essential to making those investments impactful within tight timelines. One of the more unexpected themes to surface is the human side of all this change. Despite the heavy focus on technology, many of the most meaningful conversations at HIMSS centered on workforce impact, trust, and the role of humans in an AI-enabled future. The panel reflects on the need for thoughtful change management, noting that adoption is not just about deploying new tools but building confidence in how they are used. There is a shared recognition that while AI will shift certain types of work, it will also require new roles, new skills, and a more intentional approach to integrating technology into care delivery. As the episode wraps, each participant highlights a key signal to watch over the next 12 to 18 months. Prior authorization is widely seen as approaching an inflection point, with tangible progress finally within reach, though not fully complete. At the same time, the convergence of interoperability, AI, and policy is identified as a broader, more transformative trend. This trend will shape how data flows, how workflows are designed, and ultimately how care is delivered. The takeaway is not that the industry has solved its biggest challenges, but that it is entering a new phase. The foundational pieces are being built, expectations are rising, and the focus is shifting from possibility to execution. The next chapter will depend less on vision and more on whether stakeholders can align, operationalize, and follow through on the work already in motion.

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

What if the real advantage in AI lies not in having more data, but in having less?In this episode of the Don't Panic, It's Just Data podcast, host Shubhangi Dua, Podcast Producer and B2B Tech Journalist at EM360Tech, sits down with Herb Blecher, Research Director of Data and Analytics at Enterprise Management Associates (EMA). This conversation challenges a common belief in enterprise tech – that gathering everything ensures insight. Blecher, alluding to the modern-day AI craze, cautions the enterprise audience that just because you can access vast amounts of unstructured data doesn't mean you should.What is the AI Gold Rush & Why It's Risky?Unstructured data now fills the enterprise tech space — voice calls, financial documents, customer chats, images, logs, and emails. “With AI and machine learning, we've finally figured out how to access and organise it.”However, Blecher offers a stark reality check. AI doesn't just increase insight; it increases error. When machines transition from calculating numbers to interpreting tone, images, and incomplete context, the chances for mistakes rise significantly. A blurry comma in a financial document, a misread abbreviation, a misplaced decimal. In low-stakes situations, this is inconvenient. In finance or healthcare, it can be disastrous.The danger lies not just in faulty outputs, but in confidently flawed outputs. AI doesn't hesitate as humans do. It doesn't say, “This seems off.” It fills in gaps, often convincingly. That confidence, Blecher argues, makes governance essential.The real issue companies face isn't a lack of data; it's a lack of careful thought.Also Read: AI is Making “As-Code” InevitableWhy Human-in-the-Loop is Imperative?Governance over hype is the key takeaway from the conversation. AI generating and using data at the same time creates a new situation. In the past, including financial troubles that Blecher experienced directly, human judgment acted as the final protection. Now, companies risk losing that safeguard in their rush to automate.Dua puts it simply – humans are leaders; AI is the helper.The enterprises that succeed with unstructured data aren't the fastest; they are the most thoughtful. They clearly define their questions first, build feedback loops, monitor continuously, and foster a culture of scepticism.What are the failures? They often look like ambitious automation without safeguards—from flawed document scanning to high-profile AI rollouts like McDonald's testing automated drive-through ordering, where conversational nuance proved more challenging than anticipated.Tone, ambiguity, and context remain distinguishing human areas.What Happens Five Years From Now?Will AI solve data quality issues? No, it will not. However, Blecher believes that data quality problems are here to stay. “What will change is the range of questions we try to answer. As AI develops, companies won't stop dealing with edge cases; they'll broaden the edge.”The future doesn't promise easy automation. It promises increased capability, increased capacity, along with increased responsibility.For CFOs and IT leaders investing in AI-driven data strategies, EMA's Research Director of Data and Analytics has a final message:Don't confuse volume with value.Don't replace governance with optimism.Don't give up scepticism in a gold rush.AI's potential is huge. But more data doesn't always mean better data. In a world eager to gather everything, restraint could be the most radical strategy of all.Key Takeaways More data doesn't guarantee better insights — clarity of purpose matters more than volume.AI doesn't just scale intelligence; it scales errors if governance is weak.Unstructured data is powerful, but without context and oversight, it becomes a liability.Human judgment remains essential — especially in high-stakes domains like finance and healthcare.The most successful organisations move deliberately, not impulsively, in the AI gold rush.Chapters00:00 Introduction to Data Quality and Its Importance02:43 The Rise of Unstructured Data05:42 Challenges in Ensuring Data Quality08:46 AI's Role in Data Quality Management11:30 Human Oversight in AI and Data Quality14:47 Opportunities in Data Quality17:32 Governance and Regulation in AI20:25 Real-World Applications and Case Studies23:27 Future of Data Quality and AI26:18 Key Takeaways for LeadersAbout Herb BlecherHerb leads EMA's Data and Analytics practice. He brings more than two decades of experience building solutions across financial services, data product development, and enterprise analytics.His perspective is shaped by leading national data initiatives for U.S. mortgage servicers and government agencies, as well as driving product innovation and strategy in fast-moving technology environments. Herb's research spans enterprise data and analytics, including data architecture and platform modernisation, analytics and integration, governance, and AI/ML platforms.#AI #DataAnalytics #TechPodcast #B2BTech #DataQuality #UnstructuredData #AIGoldRush #HumanInTheLoop #AICorporate #HerbBlecher #EMAPartners #CFOs #ITLeaders #DataStrategy #DontPanicItsJustData #EM360Tech #PodcastClips #DataInsights

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

Oil and gas companies generate enormous volumes of operational, geological, and production data. Despite this abundance, much of that data remains fragmented, inconsistent, and difficult to trust. Teams often spend a significant portion of their time preparing datasets rather than analyzing them. The result is delayed decision-making, inflated costs, and reduced operational agility. The core complication lies in data quality, data governance, and data readiness. Duplicate records, null values, drift, and structural inconsistencies make it difficult to move quickly from raw data to actionable insight. Asset teams frequently work semi-independently, each rebuilding transformation processes from scratch. Without reliable data foundations, scaling analytics, automation, or advanced modelling becomes difficult and costly.  In this episode, I'm in conversation with Shravan Gunda, CEO of Kaarvi, to discuss how a structured approach to data ingestion, anomaly detection, ETL transformation, and data lineage can reduce time-to-insight from weeks to hours. He outlines how upstream teams can standardize workflows, support governance requirements such as SOC 2, and deploy platforms either on-premises or via SaaS. Clean, trusted data is a prerequisite for accelerating analytics and enabling more advanced digital capabilities.

Economist Peter St. Onge comes back to the show to discuss the recent spate of encouraging economic numbers coming out of the US and what they mean in the context of Trump's attempt to turn the US economy around in the face of titanic political opposition.Show Notes:Peter on XPeter's WebsiteTom on XGGnG on Patreon

In this episode, Matt Paige and Rowan Stone, CEO of Sapien, discuss the critical importance of data quality and provenance in AI.Stone, who has experience with on-chain products at Coinbase, introduces Sapien's innovative approach to building a decentralized data protocol that emphasizes 'don't trust, verify' principles.They explore avenues such as incentives, validation methods, and the peer review process used by Sapien to create high-quality datasets.The discussion touches on the implications of bad data, the role of synthetic data, the complexities of achieving accurate AI outputs, and the parallels between the AI and crypto worlds.Key insights are shared on how to ensure models perform safely, the hurdles in the industry, and the trajectory of AI development.Additionally, Stone provides a glimpse into Sapien's efforts to demystify data validation and enhance the transparency and trustworthiness of AI applications.--Key Moments:01:04 The Importance of Data Quality in AI03:32 Challenges and Risks in AI Development07:08 Sapien's Approach to Data Validation08:35 Incentives and Trust in AI Systems13:30 Building a Decentralized Data Protocol23:22 Consensus and Collaboration in AI and Crypto30:55 The Role of Synthetic Data36:17 Future of AI Models and Open Source--Key Links:SapienConnect with Rowan on LinkedInMentioned in this episode:Free report from HatchWorks AI — State of AI 2026What's real in AI this year, what's hype, and what leaders should prioritize — including production lessons, designing for agents, and governance. https://hatchworks.com/state-of-ai-2026/AI Opportunity FinderFeeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you'll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action.

The Institute of Internal Auditors Presents: All Things Internal Audit    In this episode, Adam Ross is joined by Filipe Ribeiro and Julien Perreault to discuss how supply chain risk has evolved into an interconnected, enterprisewide challenge. They discuss where organizations underestimate exposure, how risks quietly accumulate across the value chain, and why internal audit is uniquely positioned to identify blind spots before disruptions escalate. The conversation spans real-world examples from agriculture and highly regulated industries, third-party risk, continuous monitoring, and the growing impact of automation and AI on supply chains.   HOST: Adam Ross, CIA, CISA Partner, Grant Thornton Advisors LLC GUEST: Filipe Ribeiro, CIA, CRMA, CFE Group Internal Audit Manager, Aldar Julien Perreault, CPIM, MBA Experienced Manager, Sourcing and Supply Chain Advisory, Grant Thornton Advisors LLC   KEY POINTS: Introduction to Modern Supply Chain Risk [00:00:02–00:01:22] From Operational Inconvenience to Strategic Risk [00:01:22–00:02:24] Why Supply Chain Risk Is Now Systemic and Enterprisewide [00:02:32–00:03:11] Where Organizations Commonly Underestimate Exposure [00:03:22–00:04:33] When "Green Dashboards" Mask Emerging Risk [00:03:33–00:05:08] How Informal Workarounds Quietly Accumulate Enterprise Risk [00:05:08–00:05:47] Agricultural Case Study: How Small Upstream Delays Become Major Downstream Failures [00:05:53–00:07:52] Using Continuous Monitoring to Detect Hidden Timing and Dependency Risks [00:07:57–00:12:26] Supply Chain Risk in Remote, Capital-Intensive, and Highly Regulated Environments [00:12:54–00:15:25] Balancing Regulatory Compliance and Operational Efficiency [00:15:42–00:18:55] Procure-to-Pay Risk and the Rise of Operational "Noise" [00:19:14–00:21:01] When Exceptions Become the Normal Operating Model [00:21:01–00:23:15] Third-Party Risk as a Business Resilience Issue [00:25:15–00:27:12] Governance, Speed of Business, and Supplier Ecosystems [00:27:12–00:30:25] Managing Supplier Concentration Risk Without Sacrificing Resilience [00:31:20–00:35:04] Geographic and Cultural Complexity as an Underestimated Risk Driver [00:35:36–00:37:27] How Internal Audit Can Add Value Without Compromising Independence [00:38:35–00:41:29] Emerging Risks: Automation, AI, Data Quality, and Governance Lag [00:41:47–00:46:18] Final Thoughts on the Future of Supply Chain Risk [00:46:29–00:47:05]   Visit The IIA's website or YouTube channel for related topics and more.   IIA RELATED CONTENT:  Interested in this topic? Visit the links below for more resources: Global Internal Audit Standards Third-Party Topical Requirement Continuous Auditing and Monitoring, 3rd Edition Boardroom: Breaks in the Chain GAM 2026   Follow All Things Internal Audit: Apple Podcasts Spotify Libsyn Deezer

In this episode of Future Finance, Paul Barnhurst and Glenn Hopper explore how AI tools like Claude, Copilot, and Excel agents are transforming financial workflows in 2026. As the finance industry continues to evolve, AI is playing an increasingly crucial role in automating routine tasks, improving accuracy, and boosting efficiency.Paul and Glenn discuss their firsthand experiences with AI-driven tools, from tracking expenses and managing journal entries to building financial models. They also dive into the ways small and medium businesses are utilizing AI-powered Excel agents to streamline their financial processes without the need for expensive, traditional planning tools.In this episode, you will discover:The impact of AI agents in finance and how they simplify workflows.Personal experiences with Claude and Copilot in real-world finance tasks.How to integrate AI into your finance team's daily tasks.The benefits and challenges of AI-powered automation in finance.Paul and Glenn emphasize the importance of embracing AI in finance, highlighting how tools like Claude, Copilot, and Excel agents are transforming everyday workflows. They encourage finance professionals to experiment with AI, even if it means starting small.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] – AI's Role in Finance[03:47] – AI Agents in Excel[06:12] – Claude vs. Copilot[09:27] – Automating Expenses & Journal Entries[15:56] – AI for Financial Models[21:21] – Future of AI in Small Business Finance[26:26] – Workflow Automation in Finance[35:28] – AI & Human Collaboration[39:31] – Experimenting with AI in Finance[41:45] – Data Quality & Governance[43:07] – Key Takeaway

A huge THANK YOU to our Patrons: Michael Devries, irvin ruiz, Hoshi 127, and Nora Klimek, who are supporting us on the “credited” level. www.patreon.com/bdckrThanks to the following for providing fodder for our Q&A: @nevinusa7164 (USA)@sugmadig00  (tracking challenges)@fleetingsydney (Joker v. Joker)@chrislegend382 (Data Quality)Public Mobile referral code: VPM35Z

In this episode, Adam Torres ⁠interviews Alex Kangoun, CEO of Athena Solutions, Inc., about why data readiness—not AI models—is the real driver of success in the AI era. Alex shares how companies can overcome data management debt, build trust through incremental wins, and turn data into a competitive advantage. About ⁠Alex Kangoun Alex has over 20 years of data warehousing and business intelligence consulting experience across multiple industries. His experience includes solution delivery for BI and data quality initiatives. His core strengths include managing global projects involving teams across multiple locations. Prior to Athena Solutions Alex was responsible for BI solutions at Pitney Bowes and was Director of Business Intelligence and Data Quality at Monster.com. His other clients included Price Waterhouse Coopers, PTC, Fidelity, Teradyne, EMC, Citizens Bank, and others. Alex has MBA from Boston College and MS from Kiev National University of Construction and Architecture. Alex is certified Project Manager Professional (PMP) from PMI. About Athena Solutions Athena Solutions offers strategic data management and business intelligence consulting that empowers businesses to access and use their data to make better business decisions. The experts at Athena Solutions have over twenty years of business intelligence experience, having worked on over 100 successful projects in various industries such as financial services, healthcare, consumer product goods, retail, telecom and high tech. Watch Full Episode on ⁠Youtube⁠. --- Follow Adam on Instagram at ⁠https://www.instagram.com/askadamtorres/⁠ for up to date information on book releases and tour schedule. Apply to be a guest on our podcast: ⁠https://missionmatters.lpages.co/podcastguest/⁠ Visit our website: ⁠https://missionmatters.com/⁠ More FREE content from Mission Matters here: ⁠https://linktr.ee/missionmattersmedia Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this episode, ⁠Adam Torres ⁠⁠interviews ⁠Alex Kangoun⁠, CEO of Athena Solutions, Inc., about why data readiness—not AI models—is the real driver of success in the AI era. Alex shares how companies can overcome data management debt, build trust through incremental wins, and turn data into a competitive advantage. About⁠ ⁠⁠Alex Kangoun Alex has over 20 years of data warehousing and business intelligence consulting experience across multiple industries. His experience includes solution delivery for BI and data quality initiatives. His core strengths include managing global projects involving teams across multiple locations. Prior to Athena Solutions Alex was responsible for BI solutions at Pitney Bowes and was Director of Business Intelligence and Data Quality at Monster.com. His other clients included Price Waterhouse Coopers, PTC, Fidelity, Teradyne, EMC, Citizens Bank, and others. Alex has MBA from Boston College and MS from Kiev National University of Construction and Architecture. Alex is certified Project Manager Professional (PMP) from PMI. About⁠ ⁠⁠Athena Solutions⁠ Athena Solutions offers strategic data management and business intelligence consulting that empowers businesses to access and use their data to make better business decisions. The experts at Athena Solutions have over twenty years of business intelligence experience, having worked on over 100 successful projects in various industries such as financial services, healthcare, consumer product goods, retail, telecom and high tech. Watch Full Episode on⁠ ⁠Youtube⁠⁠. --- Follow Adam on Instagram at⁠ ⁠https://www.instagram.com/askadamtorres/⁠⁠ for up to date information on book releases and tour schedule. Apply to be a guest on our podcast: ⁠⁠https://missionmatters.lpages.co/podcastguest/⁠⁠ Visit our website: ⁠⁠https://missionmatters.com/⁠⁠ More FREE content from Mission Matters here:⁠ ⁠https://linktr.ee/missionmattersmedia⁠ Learn more about your ad choices. Visit podcastchoices.com/adchoices

This week, I've been thinking about something slightly uncomfortable. Last weekend, I was reviewing one of my older architecture diagrams from five years ago. A cloud-native migration plan I was deeply proud of at the time. It was clean. Structured. Scalable. And then I asked myself: If I were to rebuild this today in the era of generative AI… Would I build it the same way? The honest answer? No. Not because it was wrong. But because our assumptions have changed. Two years ago, AI was a feature. Today, AI is shaping architecture decisions. We're not just designing systems anymore. We're designing systems that design, generate, predict, and automate. And here's the tension I keep seeing in enterprise conversations: Everyone wants AI. But very few are asking: "What technical debt are we creating while chasing it?" That's why today's conversation matters. Today, I'm joined by Maxim Salav, based in Australia, someone who works deeply in enterprise architecture and technical debt remediation. And this episode is not about hype. It's about responsibility. Because AI doesn't remove architectural complexity. In many cases, it amplifies it. Let's get into it. Chapters 00:00 Introduction to Technical Debt and Architecture 01:34 The Impact of AI on Technical Debt 04:12 Generative AI and Architectural Challenges 08:40 Adopting AI in Organizations 12:26 Building AI Strategies and Governance 17:33 Data Quality and AI Integration 22:43 Guardrails for AI Adoption Episode # 181 Today's Guest: Maxim Silaev, Technology Advisor and Enterprise Architect He is a technology advisor and enterprise architect with more than two decades of experience working with high-growth companies, complex systems, and business-critical platforms. Website: Arch-Experts What Listeners Will Learn: What technical debt really means in the AI era How generative AI can unintentionally increase hidden system risk Why architecture remains critical despite AI coding tools The importance of governance and verification layers in AI systems How large enterprises are cautiously integrating AI Why strategy must precede AI deployment The evolving role of enterprise architects in AI-native environments Resources: Arch-Experts

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.

At the 2025 Medical Innovation Olympics, a powerful all-star expert panel moderated by Melissa Norcross (Vice President, Corporate Strategy, Hyland Software) featuring Eddie Power (CEO, Empower Medical, former Global Medical Affairs Leader at Pfizer), Vivek Mukhatyar (Senior Director, Medical AI Team Lead, Pfizer), and Ravi Kiran Koppichetti (Senior Analyst, Manufacturing Technology, Vertex; former Lead IT Data Engineer, Novo Nordisk) cut through the hype and delivered a practical playbook for leaders in healthcare: 1) Fall in love with the problem, not the tool; 2) Think in systems, not silos; and 3) Train your people, not just your models.Timeline00:00 Highlight 1: Why AI Innovation Fails When the Problem Is Mis-framed01:20 Highlight 2: Probable vs Precise Decisions: Where AI Helps vs Where Governance Must Lead03:38 Highlight 3: Falling in Love with the Problem, Not the Solution04:38 Highlight 4: Non-Patient AI Use Cases: Process, Partnership & Proof06:00 Leadership in the Age of AI: Framing the Right Questions08:52 Systems Thinking in Healthcare Innovation (Hepatitis C Case Study)11:35 Constraints in Medical Affairs: Where Humans Must Stay in the Loop13:19 AI as “Intelligence on Tap” vs Clinical Decision Authority17:53 Defining Target Conditions and What “Done” Really Means20:15 Systems Failures in Real-World Healthcare Environments22:50 How Providers, Payers, and Pharma Are Using AI Today25:47 Who Decides: Human vs AI Agents in Regulated Healthcare27:18 Industry 4.0 Explained: Integrating OT and IT in Pharma Manufacturing30:33 Data Quality, Trust, and Why Most Organizational Data Is Unstructured32:03 Probabilistic AI vs Precision Decisions: A Leadership Framework34:35 Trust, Evaluations, and Human-in-the-Loop AI Design39:11 Why 95% of AI Pilots Fail — and the Role of AI Ambassadors43:08 Closing Reflections: Systems Thinking, Learning Loops, and Fearless Curiosity

In this episode, Shobha Phansalkar, PhD, FAMIA, Vice President of Client Solutions and Innovation for Wolters Kluwer Health Language, discusses where data quality can make or break prior authorization accuracy.

Discover all of the podcasts in our network, search for specific episodes, get the Optimal Living Daily workbook, and learn more at: OLDPodcast.com. Episode 1917: Sherice Jacob breaks down the misunderstood concept of data quality, emphasizing that perfection isn't the goal, relevance, accuracy, and consistency are. Through clear steps like data profiling, error management, and adherence to key quality dimensions, she offers a practical roadmap for businesses to improve decisions, customer experience, and ROI. Read along with the original article(s) here: https://neilpatel.com/blog/data-quality/ Quotes to ponder: "Data quality is very much a delicate balancing act, juggling and judging accuracy and completeness." "The first step toward successful integration is seeing where the data is and then combining that data in a way that's consistent." "Taking the time now to map out what data quality means to your company or organization can create a ripple-effect of improved customer service, a better customer experience, a higher conversion rate and longer customer retention." Learn more about your ad choices. Visit megaphone.fm/adchoices

Discover all of the podcasts in our network, search for specific episodes, get the Optimal Living Daily workbook, and learn more at: OLDPodcast.com. Episode 1917: Sherice Jacob breaks down the misunderstood concept of data quality, emphasizing that perfection isn't the goal, relevance, accuracy, and consistency are. Through clear steps like data profiling, error management, and adherence to key quality dimensions, she offers a practical roadmap for businesses to improve decisions, customer experience, and ROI. Read along with the original article(s) here: https://neilpatel.com/blog/data-quality/ Quotes to ponder: "Data quality is very much a delicate balancing act, juggling and judging accuracy and completeness." "The first step toward successful integration is seeing where the data is and then combining that data in a way that's consistent." "Taking the time now to map out what data quality means to your company or organization can create a ripple-effect of improved customer service, a better customer experience, a higher conversion rate and longer customer retention."  

In this episode, Dipak Kalra, President of the European Institute for Innovation through Health Data, joins Faces of Digital Health to break down the real progress (and real gaps) in European health data, from legacy “hybrid” paper/digital workflows to the underused potential of clinical decision support that depends on structured data. We explore what EHDS changes—especially the promise of a standardized, downloadable patient dataset—and what it could unlock for patient-facing apps, analytics, and more active self-management. We also tackle the hard questions: how to protect citizens from misuse and scams, how opt-out choices might create bias in research and AI, why “beating clinicians with a stick” won't fix data quality, and why delays aren't just bureaucratic—they can translate into avoidable harm. 02:00 The State of Healthcare Data in Europe 07:59 Challenges in Data Interoperability 12:31 The Role of Patients in Data Management 16:37 AI and Data Privacy Concerns 22:01 Patient Consent and Data Usage 28:00 Optimism for the Future of Health Data 31:03 Optimistic Futures for EAGDS 33:02 Preparing for EHDs: Readiness and Challenges 35:48 Data Quality and Workforce Challenges 37:58 Delays and Future Discussions on EHDs 39:53 The Urgency of Health Data Readiness 42:38 The Evolving Role of Patients in Healthcare 50:19 Building Trust Among Healthcare Stakeholders 57:58 The Future of Healthcare Data Discussions

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.

Edwin Chen is the founder and CEO of Surge AI, the data infrastructure company behind nearly every major frontier model. Surge works with OpenAI, Anthropic, Meta, and Google, providing the high-quality data and evaluation infrastructure that powers their models.  Edwin reveals why optimizing for popular benchmarks like LMArena is "basically optimizing for clickbait," how one frontier lab's models regressed for 6-12 months without anyone knowing, and why the industry's approach to measurement is fundamentally broken. Jacob and Edwin discuss what actually makes elite AI evaluators, why "there's never going to be a one size fits all solution" for AI models, and how frontier labs are taking surprisingly divergent paths to AGI. (0:00) Intro(0:56) The Pitfalls of Optimizing for LMArena(4:34) Issues with Data Quality and Measurement(9:44) The Importance of Human Evaluations(13:40) The Rise of RL Environments(17:21) Challenges and Lessons in Model Training(19:59) Silicon Valley's Pivot Culture(23:06) Technology-Driven Approach(24:18) Quality Beyond Credentials(27:51) Impact of Scale Acquisition(28:35) Hiring for Research Culture(30:48) Divergence in AI Training Paradigms(34:16) Future of AI Models(39:32) Multimodal AI and Quality(43:44) Quickfire With your co-hosts: @jacobeffron - Partner at Redpoint, Former PM Flatiron Health @patrickachase - Partner at Redpoint, Former ML Engineer LinkedIn @ericabrescia - Former COO Github, Founder Bitnami (acq'd by VMWare) @jordan_segall - Partner at Redpoint

Join us for an insightful conversation with Fernanda Michels, MSc, PhD, ODS-C, Program Manager of Data Quality and Integration at the North American Association of Central Cancer Registries (NAACCR). In this episode, Dr. Michels explains the critical role state cancer registries play in cancer surveillance and why data quality and accuracy matter for meaningful outcomes. Listen now to gain expert insights into the backbone of cancer data collection.

Send me a messageWhat if the biggest risk in your supply chain isn't geopolitical shocks or new regulations, but the data you trust every single day?This week, I'm joined by Andy Kohm, co-founder and CEO of SCIP, a supply chain intelligence platform built to clean, connect, and operationalise data across ERPs, PLMs, control towers, and the spreadsheets nobody admits to using. Andy has spent more than a decade wrestling with the messy reality of supply chain data, and his insights couldn't be more relevant as volatility rises and digital transformation hits its limits.In this conversation, you'll hear how bad data quietly drives bad decisions - from inflated lead times to unnecessary expedites to risk scores that collapse under scrutiny. We break down why most organisations can't agree on something as simple as the “source of truth,” and how that single failure cascades into higher emissions, higher costs, and planners who simply stop believing the system.You might be surprised to learn how often companies pay 10x for components they could have sourced at the normal price - simply because the underlying data was wrong. And we dig into where AI can genuinely help today (contract intelligence, grunt-work automation) and where it's still pure theatre without clean inputs.

In this episode, Kortney Harmon and Chris Hesson join Benjamin Mena on The Elite Recruiter Podcast to examine how AI agents are reshaping the recruiting workflow and challenging the traditional KPIs many teams still rely on. They explore why sourcing is shifting, why volume-based activity metrics no longer reflect real performance, and how automation is redefining the recruiter's day-to-day responsibilities.Kortney and Chris break down how AI agents now support sourcing, research, data cleanup, enrichment, and workflow execution—returning hours each week while exposing the widening disconnect between legacy activity tracking and modern recruiter impact. They also discuss how measuring calls, emails, and task volume can unintentionally penalize recruiters who leverage automation effectively, and why leaders must adopt outcome-based metrics grounded in influence, advisory work, and relationship-building. The conversation highlights how living resumes, real-time data enrichment, and agent-driven workflows inside the ATS can unlock value long buried in existing databases.Listen in to explore how automation changes the work—and how humans elevate the results.________________Follow Benjamin Mena LinkedIn: LinkedIn: BenjaminBenjamin Mena with Select Source Solutions: hereThe Elite Recruiter Podcast Instagram: https://www.instagram.com/theeliterecruiter/Follow Crelate on LinkedIn: CrelateWant to learn more about Crelate? Book a demo hereSubscribe to our newsletter: The Full Desk Experience

The promise of agentic AI has been massive, autonomous systems that act, reason, and make business decisions, but most enterprises are still struggling to see results.In this episode, host Chris Brandt sits down with Sumeet Arora, Chief Product Officer at Teradata, to unpack why the gap exists between AI hype and actual impact, and what it takes to make AI scale, explainable, and ROI-driven.From the shift toward “AI with ROI” to the new era of human + AI systems and data quality challenges, Sumeet shares how leading enterprises are moving from flashy demos to measurable value and trust in the next phase of AI. CHAPTER MARKERS00:00 The AI Hackathon Era03:10 Hype vs Reality in Agentic AI06:05 Redesigning the Human AI Interface09:15 From Demos to Real Economic Outcomes12:20 Why Scaling AI Still Fails15:05 The Importance of AI Ready Knowledge18:10 Data Quality and the Biggest Bottleneck20:46 Building the Customer 360 Knowledge Layer23:35 Push vs Pull Systems in Modern AI26:15 Rethinking Enterprise Workflows29:20 AI Agents and Outcome Driven Design32:45 Where Agentic AI Works Today36:10 What Enterprises Still Get Wrong39:30 How AI Changes Engineering Priorities55:49 The Future of GPUs and Efficiency Challenges -- 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.

The robotics industry is on the cusp of its own “GPT” moment, catalyzed by transformative research advances. Enter Memo, the first general-intelligence personal robot, focused on taking on your chores to give back your time. Sarah Guo sits down with Tony Zhao and Cheng Chi, co-founders of Sunday Robotics, to discuss the state of AI robotics. Tony and Cheng speak to the challenges they faced while developing their technology, the innovative glove system employed to scale real-world data collection, and the impact of diffusion policy and imitation learning. Plus, they talk about their 2026 in-home beta program and why personal robots are only a handful of years away from mass deployment. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @tonyzzhao | @chichengcc | @sundayrobotics Chapters: 00:00 – Tony Zhao and Cheng Chi Introduction 00:56 – State of AI Robotics 02:11 – Deploying a Robot Pre-AI 03:13 – Impact of Diffusion Policy  04:29 – Role of ACT and ALOHA 07:02 – Imitation Learning - Enter UMI 10:38 – Introducing Sunday 11:57 – Sunday's Robot Design Philosophy 15:05 – Sunday's Shipping Timeline 19:02 – Scale of Sunday's Training Data 23:58 – Importance of Data Quality at Scale 24:56 – Technical Challenges 27:59 – When Will People Have Home Robots? 30:48 – Failures of Past Demos 32:34 – Sunday's Demos 36:53 – What Sunday's Hiring For 39:10 – Conclusion

Join us on the latest episode, hosted by Jared S. Taylor!Our Guests: Kenneth Young, CEO at Medecision and Mike Green, Managing Partner at Excell Healthcare Advisors.What you'll get out of this episode:Strategic Union for Scalable Impact: Medecision's acquisition of Excell aims to merge technology and consulting to unlock ROI and operational change.Data Quality as the Foundation: Leaders emphasize that without clean, integrated data, AI initiatives risk failure.Enabling Clinicians to Work Top of License: AI is used to minimize administrative burden and maximize patient-focused care.AI with Purpose, Not Hype: Real-world applications, not buzzwords, are driving conversations about AI's role in healthcare transformation.Rehumanizing Healthcare: Combining AI, data, and clinical insight to ensure the right care is delivered at the right time.To learn more about:Medecision Website https://www.medecision.com/ Medecision Linkedin https://www.linkedin.com/company/medecision/ Excell Healthcare Advisors Website https://www.excellha.com/ Excell Healthcare Advisors Linkedin https://www.linkedin.com/company/excellhealthcareadvisors/ Our sponsors for this episode are:Sage Growth Partners https://www.sage-growth.com/Quantum Health https://www.quantum-health.com/Show and Host's Socials:Slice of HealthcareLinkedIn: https://www.linkedin.com/company/sliceofhealthcare/Jared S TaylorLinkedIn: https://www.linkedin.com/in/jaredstaylor/WHAT IS SLICE OF HEALTHCARE?The go-to site for digital health executive/provider interviews, technology updates, and industry news. Listed to in 65+ countries.

In this episode of Crazy Wisdom, host Stewart Alsop talks with Jessica Talisman, founder of Contextually and creator of the Ontology Pipeline, about the deep connections between knowledge management, library science, and the emerging world of AI systems. Together they explore how controlled vocabularies, ontologies, and metadata shape meaning for both humans and machines, why librarianship has lessons for modern tech, and how cultural context influences what we call “knowledge.” Jessica also discusses the rise of AI librarians, the problem of “AI slop,” and the need for collaborative, human-centered knowledge ecosystems. You can learn more about her work at Ontology Pipeline and find her writing and talks on LinkedIn.Check out this GPT we trained on the conversationTimestamps00:00 Stewart Alsop welcomes Jessica Talisman to discuss Contextually, ontologies, and how controlled vocabularies ground scalable systems.05:00 They compare philosophy's ontology with information science, linking meaning, categorization, and sense-making for humans and machines.10:00 Jessica explains why SQL and Postgres can't capture knowledge complexity and how neuro-symbolic systems add context and interoperability.15:00 The talk turns to library science's split from big data in the 1990s, metadata schemas, and the FAIR principles of findability and reuse.20:00 They discuss neutrality, bias in corporate vocabularies, and why “touching grass” matters for reconciling internal and external meanings.25:00 Conversation shifts to interpretability, cultural context, and how Western categorical thinking differs from China's contextual knowledge.30:00 Jessica introduces process knowledge, documentation habits, and the danger of outsourcing how-to understanding.35:00 They explore knowledge as habit, the tension between break-things culture and library design thinking, and early AI experiments.40:00 Libraries' strategic use of AI, metadata precision, and the emerging role of AI librarians take focus.45:00 Stewart connects data labeling, Surge AI, and the economics of good data with Jessica's call for better knowledge architectures.50:00 They unpack content lifecycle, provenance, and user context as the backbone of knowledge ecosystems.55:00 The talk closes on automation limits, human-in-the-loop design, and Jessica's vision for collaborative consulting through Contextually.Key InsightsOntology is about meaning, not just data structure. Jessica Talisman reframes ontology from a philosophical abstraction into a practical tool for knowledge management—defining how things relate and what they mean within systems. She explains that without clear categories and shared definitions, organizations can't scale or communicate effectively, either with people or with machines.Controlled vocabularies are the foundation of AI literacy. Jessica emphasizes that building a controlled vocabulary is the simplest and most powerful way to disambiguate meaning for AI. Machines, like people, need context to interpret language, and consistent terminology prevents the “hallucinations” that occur when systems lack semantic grounding.Library science predicted today's knowledge crisis. Stewart and Jessica trace how, in the 1990s, tech went down the path of “big data” while librarians quietly built systems of metadata, ontologies, and standards like schema.org. Today's AI challenges—interoperability, reliability, and information overload—mirror problems library science has been solving for decades.Knowledge is culturally shaped. Drawing from Patrick Lambe's work, Jessica notes that Western knowledge systems are category-driven, while Chinese systems emphasize context. This cultural distinction explains why global AI models often miss nuance or moral voice when trained on limited datasets.Process knowledge is disappearing. The West has outsourced its “how-to” knowledge—what Jessica calls process knowledge—to other countries. Without documentation habits, we risk losing the embodied know-how that underpins manufacturing, engineering, and even creative work.Automation cannot replace critical thinking. Jessica warns against treating AI as “room service.” Automation can support, but not substitute, human judgment. Her own experience with a contract error generated by an AI tool underscores the importance of review, reflection, and accountability in human–machine collaboration.Collaborative consulting builds knowledge resilience. Through her consultancy, Contextually, Jessica advocates for “teaching through doing”—helping teams build their own ontologies and vocabularies rather than outsourcing them. Sustainable knowledge systems, she argues, depend on shared understanding, not just good technology.

In this Marketing Over Coffee: Learn about clean data, AI strategy and more with Katie Robbert, CEO of Trust Insights! Direct Link to File AI Ready Data Quality Audit 6Cs of Data Quality Sora 2 video – New channel from Tim Street! Comedy Gum Dolly is still bringing it Insta360 GO Ultra is now available! […] The post Do You Have A Data Quality Problem? Foog Da Boot It! appeared first on Marketing Over Coffee Marketing Podcast.

On this week's episode of The Horizon Podcast, John Chang explores how artificial intelligence is beginning to reshape commercial real estate investing. After meeting with top institutional investors, John dives into how firms are using AI to identify high-performing trade areas, prune underperforming assets, and accelerate decision-making. He examines the strengths and pitfalls of data quality, from census and CoStar metrics to migration tracking through Placer AI, and discusses how automation could fundamentally transform investment strategy by filtering deals with higher accuracy and efficiency. John closes by connecting this evolution to his core theme—anticipating where the market will be five to ten years down the road. This is a limited time offer, so head over to aspenfunds.us/bestever to download the investor deck—or grab their quick-start guide if you're brand new to oil and gas investing. Get 50% Off Monarch Money, the all-in-one financial tool at www.monarchmoney.com with code BESTEVER Join the Best Ever Community  The Best Ever Community is live and growing - and we want serious commercial real estate investors like you inside. It's free to join, but you must apply and meet the criteria.  Connect with top operators, LPs, GPs, and more, get real insights, and be part of a curated network built to help you grow. Apply now at ⁠www.bestevercommunity.com⁠ Podcast production done by ⁠Outlier Audio⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices