In this discussion, I sit down with data veterans Remco Broekmans and Marco Wobben to explore why so many data projects fail. They argue that the problem isn't the technology, but a fundamental misunderstanding of communication, culture, and long-term strategy.The conversation goes deep into the critical shift from being a "hardcore techie" to focusing on translating business needs into data models. They use the classic "involved party" data modeling pattern as a prime example of how abstract IT jargon creates a massive disconnect with the business.Marco shares a fascinating (and surprising) case study of the Dutch Railroad organization, which has been engaged in an 18-year information modeling "program" - not a project - to manage its immense complexity. This sparks a deep dive into the cultural and work-ethic differences between the US and Europe, contrasting the American short-term, ROI-driven "project" mindset with the European capacity for long-term, foundational "programs".Finally, they tackle the role of AI. Is it a silver bullet or just the latest shiny object? They conclude that AI's best use is as an "intern" or "assistant", a tool to brainstorm, ask questions, and handle initial prototyping, but never as a replacement for the deep, human-centric work of understanding a business.Timestamps:00:00 - Introduction01:09 - Marco Wobben introduces his 25-year journey in information modeling.01:56 - Remco Broekmans reintroduces himself and his focus on the communication aspect of data.03:22 - The progression from hardcore techie to focusing on communication over technology.08:16 - Why is communication in data and IT projects so difficult? 09:49 - The "Involved Party" Problem: A perfect example of where IT communication goes wrong with the business.13:35 - The essence of IT is automating the communication that happens on the business side.18:39 - Discussing a client with 20,000 distinct business terms in their information model.21:55 - The story of the Dutch Railroad's 18-year information modeling program that reduced incident response from 4 hours to 2 seconds.27:25 - Project vs. Program: A key mindset difference between the US and Europe.34:18 - The danger of chasing shiny new tools like AI without getting the fundamentals right first.39:55 - Where does AI fit into the world of data modeling? 43:34 - Why you can't trust AI to be the expert, especially with specialized business jargon.47:18 - The role of risk in trusting AI, using a self-driving car analogy.53:27 - Cultural differences in work pressure and ethics between the US and the Netherlands.59:29 - Why personality and communication skills are more important than a PhD for data modelers.01:03:38 - What is the purpose of an AI-run company with no human benefit? 01:11:21 - Using AI as an instructive tool to improve your own skills, not just to get an answer.01:14:12 - How AI can be used as a "sidekick" to ask dumb questions and help you think.01:18:00 - Where to find Marco and Remco online

Pulumi is the Infrastructure as Code platform that boosts your team’s capacity using familiar programming languages, helping you ship faster, scale confidently, and enable secure developer self-service. Built on 8+ years of infrastructure intelligence, Pulumi combines proven expertise with AI that works at your pace, under your control.

Plain-English agents, not hype: plan, use tools, add guardrails—then dry-run a calendar + inbox agent that saves hours. You’ve heard “AI agent” everywhere, but definitions vary. In this episode, Mukundan explains—in plain, practical language—what a real agent is and when to use one. We’ll contrast agents with chatbots and simple automations, walk through the five pillars (goal, plan, tools, memory, feedback), and strip the tooling of buzzwords. Then we run a live dry-run: two 90-minute deep-work blocks, one 45-minute admin sweep, and a 30-minute workout that avoids your existing commitments. We finish by triaging sample emails into reply/delegate/archive/read-later and drafting five concise replies with one clear next step each. Safety first: drafts-only, tentative calendar holds, approval gates, and fallbacks if tools fail. Copy the templates from the show notes and ship your v0.5 tonight. Lightning Round: True/False (answers in the episode) An AI agent is just a chatbot.Remove a tool and it still makes progress → probably a real agent.Clear success criteria matter less than good prompts.Chatbots reply; agents execute across steps.Fixed automations are best when inputs rarely change.If a tool disconnect breaks everything, it’s a brittle macro.Links & Resources AI Agent Copy-Paste Templates (run these in ChatGPT): Get your free copyRecording Partner: Riverside → Sign up here (affiliate)Host Your Podcast: RSS.com (affiliate )Research Tools: Sider.ai (affiliate)Join the Newsletter: Free Email Newsletter to receive practical AI tools weekly.Join the Discussion (comments hub): https://mukundansankar.substack.com/notes🔗 Connect with Me:Website: Data & AI with MukundanTwitter/X: @sankarmukund475LinkedIn: Mukundan SankarYouTube: Subscribe

In this episode, I sat down with tech humanist Kate O’Neill to explore how organizations can balance human-centered design in a time when everyone is racing to find ways to leverage AI in their businesses. Kate introduced her “Now–Next Continuum,” a framework that distinguishes digital transformation (catching up) from true innovation (looking ahead). We dug into real-world challenges and tensions of moving fast vs. creating impact with AI, how ethics fits into decision making, and the role of data in making informed decisions. 

Kate stressed the importance of organizations having clear purpose statements and values from the outset, proxy metrics she uses to gauge human-friendliness, and applying a “harms of action vs. harms of inaction” lens for ethical decisions. Her key point: human-centered approaches to AI and technology creation aren’t slow; they create intentional structures that speed up smart choices while avoiding costly missteps.

Highlights/ Skip to:

How Kate approaches discussions with executives about moving fast, but also moving in a human-centered way when building out AI solutions (1:03) Exploring the lack of technical backgrounds among many CEOs and how this shapes the way organizations make big decisions around technical solutions (3:58)  FOMO and the “Solution in Search of a Problem” problem in Data (5:18)  Why ongoing ethnographic research and direct exposure to users are essential for true innovation (11:21)  Balancing organizational purpose and human-centered tech decisions, and why a defined purpose must precede these decisions (18:09) How organizations can define, measure, operationalize, and act on ethical considerations in AI and data products (35:57) Risk management vs. strategic optimism: balancing risk reduction with embracing the art of the possible when building AI solutions (43:54)

Quotes from Today’s Episode "I think the ethics and the governance and all those kinds of discussions [about the implications of digital transformation] are all very big word - kind of jargon-y kinds of discussions - that are easy to think aren't important, but what they all tend to come down to is that alignment between what the business is trying to do and what the person on the other side of the business is trying to do." –Kate O’Neill

" I've often heard the term digital transformation used almost interchangeably with the term innovation. And I think that that's a grave disservice that we do to those two concepts because they're very different. Digital transformation, to me, seems as if it sits much more comfortably on the earlier side of the Now-Next Continuum. So, it's about moving the past to the present… Innovation is about standing in the present and looking to the future and thinking about the art of the possible, like you said. What could we do? What could we extract from this unstructured data (this mess of stuff that’s something new and different) that could actually move us into green space, into territory that no one’s doing yet? And those are two very different sets of questions. And in most organizations, they need to be happening simultaneously." –Kate O’Neill

"The reason I chose human-friendly [as a term] over human-centered partly because I wanted to be very honest about the goal and not fall back into, you know, jargony kinds of language that, you know, you and I and the folks listening probably all understand in a certain way, but the CEOs and the folks that I'm necessarily trying to get reading this book and make their decisions in a different way based on it." –Kate O’Neill

“We love coming up with new names for different things. Like whether something is “cloud,” or whether it’s like, you know, “SaaS,” or all these different terms that we’ve come up with over the years… After spending so long working in tech, it is kind of fun to laugh at it. But it’s nice that there’s a real earnestness [to it]. That’s sort of evergreen [laugh]. People are always trying to genuinely solve human problems, which is what I try to tap into these days, with the work that I do, is really trying to help businesses—business leaders, mostly, but a lot of those are non-tech leaders, and I think that’s where this really sticks is that you get a lot of people who have ascended into CEO or other C-suite roles who don’t come from a technology background.” 

–Kate O’Neill

"My feeling is that if you're not regularly doing ethnographic research and having a lot of exposure time directly to customers, you’re doomed. The people—the makers—have to be exposed to the users and stakeholders.  There has to be ongoing work in this space; it can't just be about defining project requirements and then disappearing. However, I don't see a lot of data teams and AI teams that have non-technical research going on where they're regularly spending time with end users or customers such that they could even imagine what the art of the possible could be.”

–Brian T. O’Neill

Links

KO Insights: https://www.koinsights.com/ LinkedIn for Kate O’Neill: https://www.linkedin.com/in/kateoneill/ Kate O’Neill Book: What Matters Next: A Leader's Guide to Making Human-Friendly Tech Decisions in a World That's Moving Too Fast

If you didn't have a visceral reaction to the title for this episode, then you are almost certainly not in our target audience. There are few more certain ways to get a room full of analytics folk fired up than to raise the topic of dashboards. Are they where data goes to die, or are they the essential key to unlocking self-service access to actionable insights? Are they both? Is the question irrelevant, because, if they exist to inform business users, aren't they soon going to be replaced by an AI-powered chatbot, anyway? We thought a great way to dig into the topic (and, BTW, we were right) would be to have someone on the show who has co-penned multiple books on the topic. As luck would have it, Andy Cotgreave, one of the co-authors of both 2017's The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios and the imminently releasing  Dashboards That Deliver: How to Design, Develop, and Deploy Dashboards That Work agreed to join us for a lively chat on the topic! This episode's Measurement Bite from show sponsor Recast is a quick explanation of power analysis from Michael Kaminsky! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Anu Srivastava, Sr. Staff Technical Marketing Manager, NVIDIA - Anu leads open model marketing at NVIDIA, working with partners like Meta, Google DeepMind, and Microsoft to grow the public AI ecosystem.

Denise Bowen, Founder & CEO, DB Connect | UNODC Cybercrime Advisor - A global policy leader shaping international cybercrime policy for the United Nations and scaling socially conscious ventures.

Financial institutions are racing to harness the power of AI, but the path to implementation is filled with challenges. From feature engineering to model deployment, the technical complexities of AI adoption in finance require careful navigation of both technological and regulatory landscapes. How do you build AI systems that satisfy strict compliance requirements while still delivering business value? What skills should teams prioritize as AI tools become more accessible through natural language interfaces? With the pressure to reduce model development time from months to days, how can organizations maintain proper governance while still moving at the speed modern business demands? Vijay is a seasoned analytics, product, and technology executive. As EVP of Global Solutions & Analytics at Experian, he runs the department that creates Experian's Ascend financial AI platform. Promoted multiple times in eight years, Vijay now leads a team of more than 70 at Experian. He is one of the youngest execs at Experian, believing strongly in understanding and accepting risk. He has built and run data, engineering, and IT teams, and created market-leading products. In the episode, Richie and Vijay explore the impact of generative AI on the finance industry, the development of Experian's Ascend platform, the challenges of fraud prevention, education and compliance in AI deployment, and much more. Links Mentioned in the Show: ExperianExperian AscendConnect with VijayCourse: Implementing AI Solutions in BusinessRelated Episode: How Generative AI is Transforming Finance with Andrew Reiskind, CDO at MastercardRewatch RADAR AI 

New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business

The debate builds over the resilience of the expansion, the health of the labor market, and the durability of the consumers. At the same time, questions arise over growth without jobs and the Fed’s responsibility to it.

Speakers:

Bruce Kasman

Joseph Lupton

This podcast was recorded on 12 September 2025.

This communication is provided for information purposes only. Institutional clients please visit www.jpmm.com/research/disclosures for important disclosures. © 2025 JPMorgan Chase & Co. All rights reserved. This material or any portion hereof may not be reprinted, sold or redistributed without the written consent of J.P. Morgan. It is strictly prohibited to use or share without prior written consent from J.P. Morgan any research material received from J.P. Morgan or an authorized third-party (“J.P. Morgan Data”) in any third-party artificial intelligence (“AI”) systems or models when such J.P. Morgan Data is accessible by a third-party. It is permissible to use J.P. Morgan Data for internal business purposes only in an AI system or model that protects the confidentiality of J.P. Morgan Data so as to prevent any and all access to or use of such J.P. Morgan Data by any third-party.

Mark and Cris are joined by Matt Colyar to break down the latest CPI inflation report, while Jared Franz from the Capital Group explores how artificial intelligence is reshaping the American economy and labor market. We examine the opportunities and challenges of the AI revolution and what it means for workers, businesses, and investors in this rapidly changing economic landscape. Jared Franz is an economist at Capital Group, responsible for covering the United States. He has 19 years of investment industry experience and has been with Capital Group for 10 years. Prior to joining Capital, Jared was head of international macroeconomic research at Hartford Investment Management Company. Before that, he was an international and U.S. economist at T. Rowe Price. He holds a PhD in economics from the University of Illinois at Chicago, a bachelor’s degree in mathematics from Northwestern University and attended the U.S. Naval Academy. He is also a member of the Forecasters Club of New York, an elected member of the Conference of Business Economists and a member of the Pacific Council. Jared is based in Los Angeles. Explore more insights from Capital Group’s Jared Franz in the articles below: 4 charts on why the U.S. economy could stay resilient | Capital Group Benjamin Button’s clues for the US economy   Explore the risks and realities shaping the economy in our new webinar, now streaming for free. U.S. Economic Outlook: Under Unprecedented Uncertainty Watch here: https://events.moodys.com/mc68453-wbn-2025-mau25777-us-macro-outlook-precipice-recession?mkt_tok=OT… Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, and Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X' and BlueSky @MarkZandi, Cris deRitis on LinkedIn, and Marisa DiNatale on LinkedIn

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

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At Berlin Buzzwords, industry voices highlighted how search is evolving with AI and LLMs.

• Kacper Łukawski (Qdrant) stressed hybrid search (semantic + keyword) as core for RAG systems and promoted efficient embedding models for smaller-scale use.
• Manish Gill (ClickHouse) discussed auto-scaling OLAP databases on Kubernetes, combining infrastructure and database knowledge.
• André Charton (Kleinanzeigen) reflected on scaling search for millions of classifieds, moving from Solr/Elasticsearch toward vector search, while returning to a hands-on technical role.
• Filip Makraduli (Superlinked) introduced a vector-first framework that fuses multiple encoders into one representation for nuanced e-commerce and recommendation search.
• Brian Goldin (Voyager Search) emphasized spatial context in retrieval, combining geospatial data with AI enrichment to add the “where” to search.
• Atita Arora (Voyager Search) highlighted geospatial AI models, the renewed importance of retrieval in RAG, and the cautious but promising rise of AI agents.

Together, their perspectives show a common thread: search is regaining center stage in AI—scaling, hybridization, multimodality, and domain-specific enrichment are shaping the next generation of retrieval systems.

Kacper Łukawski Senior Developer Advocate at Qdrant, he educates users on vector and hybrid search. He highlighted Qdrant’s support for dense and sparse vectors, the role of search with LLMs, and his interest in cost-effective models like static embeddings for smaller companies and edge apps. Connect: https://www.linkedin.com/in/kacperlukawski/

Manish Gill
Engineering Manager at ClickHouse, he spoke about running ClickHouse on Kubernetes, tackling auto-scaling and stateful sets. His team focuses on making ClickHouse scale automatically in the cloud. He credited its speed to careful engineering and reflected on the shift from IC to manager.
Connect: https://www.linkedin.com/in/manishgill/

André Charton
Head of Search at Kleinanzeigen, he discussed shaping the company’s search tech—moving from Solr to Elasticsearch and now vector search with Vespa. Kleinanzeigen handles 60M items, 1M new listings daily, and 50k requests/sec. André explained his career shift back to hands-on engineering.
Connect: https://www.linkedin.com/in/andrecharton/

Filip Makraduli
Founding ML DevRel engineer at Superlinked, an open-source framework for AI search and recommendations. Its vector-first approach fuses multiple encoders (text, images, structured fields) into composite vectors for single-shot retrieval. His Berlin Buzzwords demo showed e-commerce search with natural-language queries and filters.
Connect: https://www.linkedin.com/in/filipmakraduli/

Brian Goldin
Founder and CEO of Voyager Search, which began with geospatial search and expanded into documents and metadata enrichment. Voyager indexes spatial data and enriches pipelines with NLP, OCR, and AI models to detect entities like oil spills or windmills. He stressed adding spatial context (“the where”) as critical for search and highlighted Voyager’s 12 years of enterprise experience.
Connect: https://www.linkedin.com/in/brian-goldin-04170a1/

Atita Arora
Director of AI at Voyager Search, with nearly 20 years in retrieval systems, now focused on geospatial AI for Earth observation data. At Berlin Buzzwords she hosted sessions, attended talks on Lucene, GPUs, and Solr, and emphasized retrieval quality in RAG systems. She is cautiously optimistic about AI agents and values the event as both learning hub and professional reunion.
Connect: https://www.linkedin.com/in/atitaarora/

Chris Mohr, President of the Software & Information Industry Association (SIIA), joins us to unpack the legal and policy challenges shaping the future of data, AI, and digital information. Discover how companies, policymakers, and innovators can prepare for an era where AI regulation, copyright liability, and privacy standards are evolving faster than ever. If you’re a CIO, CTO, or business leader navigating decentralized data, compliance, and digital transformation, this episode will give you the insights you need to stay ahead of the curve. Be sure to check out Chris's podcast The Business of Information: https://www.siia.net/the-business-of-information/ You can find out more about Chris and SIIA by visiting their website: https://www.siia.net/ Cyberpunk by jiglr | https://soundcloud.com/jiglrmusic Music promoted by https://www.free-stock-music.com Creative Commons Attribution 3.0 Unported License https://creativecommons.org/licenses/by/3.0/deed.en_US

AI #ArtificialIntelligence #Copyright #DataPrivacy #AIRegulation #TechnologyPolicy #DigitalTransformation #Section230 #DataUnchained #TechPodcast #CloudData #DecentralizedData #CIO #CTO #SIIA

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Hoe Centraal Beheer AI inzet om organisatiebreed waardevolle toepassingen te realiseren — met concrete voorbeelden van use cases, technische bouwblokken en adoptie-aanpak.

AI heeft de potentie om chirurgen te ondersteunen bij complexe beslissingen, maar de weg van modelontwikkeling naar klinische implementatie is uitdagend. In deze presentatie laat ik zien hoe het PERISCOPE-model stap voor stap richting de zorgpraktijk gaat, welke technische, klinische en regelgevende obstakels daarbij spelen, en waarom multidisciplinaire samenwerking cruciaal is voor succes.

Generative AI and AI tools are democratizing data and blurring lines between business, data, and IT, making traditional operating models obsolete. Zilveren Kruis, the largest health insurer in the Netherlands, is modernizing by enabling self-service, implementing AI productivity suites, fostering collaboration across departments, and redefining data roles to drive rapid, compliant innovation.

Iedereen praat over AI en data, maar hoe kom je van ambitie tot resultaat? Tom Pots laat zien hoe je datagestuurd werken en AI écht in beweging krijgt. Een inspirerend verhaal met successen, fouten en doorbraken vanuit de Gemeente Zaanstad – en vooral: wat jij morgen anders kunt doen.

Hoe bouw je als regio aan digitale autonomie, met aandacht voor publieke waarden? DataFryslân deelt het transitieverhaal van strategie naar praktijk. Met concrete lessen over het inzetten van generatieve AI voor datarapportages en het borgen van ethiek via een onafhankelijke regionale commissie. Wat werkt, wat niet – en hoe je koers houdt in een complexe omgeving.

Hoe bouw je een carrière in tech – én een community – zonder traditionele start? Femke Cornelissen laat zien hoe personal branding, AI en de kracht van community haar reis vormden. Vanuit haar rol als Chief Copilot inspireert ze anderen om zichtbaar te zijn, impact te maken en mensen te verbinden. Een energieke sessie vol praktische tips over hoe je jouw verhaal deelt, een sterk netwerk bouwt en AI inzet als versneller van je missie.

Hoe ver zijn we in Nederland met AI in marketing? Laat je meenemen in de nieuwste inzichten uit het DDMO 2025: hét jaarlijkse onderzoek van de DDMA naar datagedreven marketing. Met een scherpe analyse van waar de sector staat en waar kansen liggen om te versnellen. In deze sessie ontdek je waarom de kloof tussen voorlopers en achterblijvers groeit. En hoe je voorkomt dat jouw organisatie de aansluiting mist.