Podcasts about data scientists

Gugs Mhlungu is joined by Dr Luca Pontiggia , PhD Physicist, Data Scientist and Speaker and co-founder of Universe on Stage to talk about The Black Hole Symphony Experience, the collaboration with the Johannesburg Philharmonic Orchestra and what audiences can expect from this immersive experience blending science, original music and cinematic visuals. 702 Weekend Breakfast with Gugs Mhlungu is broadcast on 702, a Johannesburg based talk radio station, on Saturdays and Sundays Gugs Mhlungu gets you ready for the weekend each Saturday and Sunday morning on 702. She is your weekend wake-up companion, with all you need to know for your weekend. The topics Gugs covers range from lifestyle, family, health, and fitness to books, motoring, cooking, culture, and what is happening on the weekend in 702land. Thank you for listening to a podcast from 702 Weekend Breakfast with Gugs Mhlungu. Listen live on Primedia+ on Saturdays and Sundays from 06:00 and 10:00 (SA Time) to Weekend Breakfast with Gugs Mhlungu broadcast on 702 https://buff.ly/gk3y0Kj For more from the show go to https://buff.ly/u3Sf7Zy or find all the catch-up podcasts here https://buff.ly/BIXS7AL Subscribe to the 702 daily and weekly newsletters https://buff.ly/v5mfetc Follow us on social media: 702 on Facebook: https://www.facebook.com/TalkRadio702 702 on TikTok: https://www.tiktok.com/@talkradio702 702 on Instagram: https://www.instagram.com/talkradio702/ 702 on X: https://x.com/Radio702 702 on YouTube: https://www.youtube.com/@radio702See omnystudio.com/listener for privacy information.

Zach Gelb is joined in studio by Sam Bruchhaus, Senior Data Scientist at Sumer Sports, to discuss the NFL landscape, who deserves OPOY, analytical perspectives on how to evaluate coaches, and more!

Duri Bonin hat Patrick Arnecke zu sich in den Podcast eingeladen, weil er verstehen will, wie KI im realen Workflow hilft. Daraus ist die Serie «Back to the Future» entstanden: eine ruhige Bestandesaufnahme nach dem Hype – was heute schon funktioniert und wie man es sauber in die Praxis bringt. In dieser Folge geht es um Audiotranskription mit KI. Ausgangslage: In der Verwaltung, der Justiz und in vielen Bereichen der öffentlichen Hand werden täglich unzählige Gespräche geführt – Einvernahmen, Befragungen, Sitzungen. All diese Gespräche müssen protokolliert werden. Das kostet Zeit, bindet Ressourcen und birgt Qualitätsrisiken. Patrick erklärt, wie KI-gestützte Transkription heute helfen kann: - Wie KI gesprochenes Audio in Text umwandelt. - Warum das Protokollieren im Kanton Zürich dank der Revision der Strafprozessordnung nun auch nachgelagert möglich ist. - Welche Chancen sich daraus ergeben: weniger Unterbrüche, mehr Qualität, bessere Arbeitsabläufe. - Und weshalb trotz KI immer ein Mensch verantwortlich bleibt Patrick erklärt das kantonale Pilotprojekt Transcribo: eine lokale, sichere Lösung, die Audioaufnahmen automatisch verschriftlicht, Sprecher trennt und einen Editor bereitstellt, in dem Protokolle nachbearbeitet werden können. Duri und er sprechen über Genauigkeit, Fehlertoleranz, Datenschutz, On-Premise-Betrieb, und darüber, warum gute Aufnahmen wichtiger sind als man denkt. Ein weiteres Thema: Was verändert sich in der Kommunikation, wenn Menschen wissen, dass jedes Wort aufgenommen und transkribiert wird? KI ist nie nur Technik – sie ist Soziotechnik. Sie verändert Zusammenarbeit, Rollen, Verantwortung und Interaktion. Genau deshalb arbeitet der Kanton mit einem breiten Team aus Juristinnen, Protokollführenden, Data Scientists und Organisationsentwicklerinnen. KI ist Teamsport. Die Folge zeigt: Audiotranskription ist kein Gimmick, sondern ein Arbeitsinstrument, das bleibt. Es spart Zeit, steigert die Treffergenauigkeit und macht vertrauliche Gesprächsprozesse einfacher, sauberer und verlässlicher – solange man KI als Assistenz versteht und Verantwortung beim Menschen bleibt. Für wen ist diese Folge spannend? Für alle, die mit Gesprächen, Protokollen und komplexen Verfahren arbeiten – Justiz, Verwaltung, Bildung, Beratung, KMU – und wissen wollen, wie man Audiotranskription mit KI sicher, sinnvoll und skalierbar einführt. In der nächsten Folge sprechen Patrick und Duri über ein weiteres zentrales KI-Thema: Sprachvereinfachung. Back to the Future: - [#739 KI nach dem Hype – Wo hilft KI heute konkret? Wie einführen?](https://www.duribonin.ch/739-ki-nach-dem-hype-wo-hilft-ki-heute-konkret-wie-einfuehren/) - [#741 KI nach dem Hype: Semantische Suche – schneller finden, besser entscheiden](https://www.duribonin.ch/741-ki-nach-dem-hype-semantische-suche-schneller-finden-besser-entscheiden/) Die Podcasts "Auf dem Weg als Anwält:in" sind unter https://www.duribonin.ch/podcast/ oder auf allen üblichen Plattformen zu hören

Morgane Dawant est Chief Data & AI Officer d'ENGIE Solutions France, acteur majeur de la transition énergétique. Arrivée il y a douze ans en tant que Data Scientist, elle pilote aujourd'hui la stratégie Data & IA de l'un des 4 grands départements du Groupe.On aborde :

This podcast episode features Dave Friesen and Tom Callahan discussing the collaboration between actuaries and data scientists. Tom is an Actuary who works extensively with data scientists, and Dave is a data scientist who works with Actuaries, making them a great combo for the topic.   Dave highlighted the operational focus of data science in improving workflows and customer experience, while Tom emphasized the financial impact focus of actuaries. They both agreed on the importance of clear communication and common terminology to bridge gaps between the two fields. They also shared examples of successful collaborations and the benefits of using modern data platforms and tools.  Contributors: Tom Callahan, FSA, MAAA; Dave Friesen; Joe Long, ASA, MAAA; Jon Forster, ASA, MAAA

Shiva Joon, Data Scientist, and Duncan Toms, Multi-Asset Strategist, look at how artificial intelligence is helping to drive the stellar performance of many US companies.Click here for appropriate Disclosures, including analyst certifications, and Disclaimers that must be viewed with this podcast: https://www.research.hsbc.com/R/101/HJJQchfStay connected and access free to view reports and videos from HSBC Global Investment Research follow us on LinkedIn https://www.linkedin.com/feed/hashtag/hsbcresearch/ or click here: https://www.gbm.hsbc.com/insights/global-research.

In dieser Episode von „Was uns bewegt“ spricht Host Wolfgang Schulz mit Patrick Zimmermann, Data Scientist und IT-Projektleiter bei der BMW Group, über die Rolle standardisierter Daten und künstlicher Intelligenz in der Hochvoltbatterieproduktion. Die beiden diskutieren über die Rolle des Industrial Internet of Things, die Vernetzung der Produktionsstätten und zukünftige Trends in der Hochvoltbatterieproduktion.

In dieser Episode sprechen wir darüber, wie es ist, im Body Leasing als externer Data Scientist direkt im Kund*innenteam zu arbeiten. Mira und Andreas teilen ihre Erfahrungen zu Rollenwechseln, Erwartungen im Projekt und dem Umgang mit Druck und neuen Teamkulturen. Wir geben praktische Tipps für Onboarding, Kommunikation und Beziehungspflege, damit die Zusammenarbeit für alle Seiten gut funktioniert. Außerdem beleuchten wir die Chancen und Risiken für Beratungen, Freelancer*innen und Auftraggeber*innen. Am Ende zeigt sich: erfolgreich wird Body Leasing vor allem über gute Beziehungen und gute Selbstorganisation.   **Zusammenfassung** Was Body Leasing bedeutet und warum es eine besondere Form der Beratung ist Erfahrungen von Mira und Andreas: Rollen, Herausforderungen und Chancen im Kund*innenteam Tipps für den Einstieg: Onboarding ernst nehmen, Erwartungen klären, Ergebnisse gut präsentieren Bedeutung von Beziehungsebene, Teamkultur und Kommunikation im täglichen Miteinander Umgang mit Druck, Bewertung und wechselnden Anforderungen Vorteile für Berater*innen: neuer Input, externe Validierung, Einblick in andere Unternehmen Chancen und Risiken für Beratungsunternehmen und Freelancer*innen Sicht der Auftraggeber*innen: schnelle Verfügbarkeit, Know-how-Gewinn, aber auch On-/Offboarding-Aufwand

On this episode of FinTech's DEI Discussions, Nadia speaks with Julia Larsson, Lead Data Scientist at Aiviq, about building inclusive cultures that empower builders, spark cross-functional collaboration, and set the trends shaping the future of work.Hear how clean data, AI fluency, diverse perspectives, and true ownership are helping teams innovate faster and more inclusively.FinTech's DEI Discussions is powered by Harrington Starr, global leaders in Financial Technology Recruitment. For more episodes or recruitment advice, please visit our website www.harringtonstarr.com

Christina Reynolds, PhD Christina Reynolds received her Ph.D. in astrophysics from University College London and a Master's degree in software engineering from Harvard University. She has been a Data Scientist with ORCATECH with a focus on developing algorithms for the analysis of ORCATECH's large and diverse data set.  Much of her research career has involved developing software algorithms used to fabricate and test the optics for the European Extremely Large Telescope and the IRIS space telescope. At ORCATECH, she focused on designing a wide variety of algorithms for deriving information about life and health patterns from ORCATECH's sensor data, including characterizing activity and sleep behaviors. _____________________ This podcast episode is sponsored by Fibion Inc. | Better Sleep, Sedentary Behaviour and Physical Activity Research with Less Hassle --- Collect, store and manage SB and PA data easily and remotely - Discover ground-breaking Fibion SENS --- SB and PA measurements, analysis, and feedback made easy.  Learn more about Fibion Research --- Learn more about Fibion Sleep and Fibion Circadian Rhythm Solutions. --- Fibion Kids - Activity tracking designed for children. --- Collect self-report physical activity data easily and cost-effectively with Mimove. --- Explore our Wearables,  Experience sampling method (ESM), Sleep,  Heart rate variability (HRV), Sedentary Behavior and Physical Activity article collections for insights on related articles. --- Refer to our article "Physical Activity and Sedentary Behavior Measurements" for an exploration of active and sedentary lifestyle assessment methods. --- Learn about actigraphy in our guide: Exploring Actigraphy in Scientific Research: A Comprehensive Guide. --- Gain foundational ESM insights with "Introduction to Experience Sampling Method (ESM)" for a comprehensive overview. --- Explore accelerometer use in health research with our article "Measuring Physical Activity and Sedentary Behavior with Accelerometers ". --- For an introduction to the fundamental aspects of HRV, consider revisiting our Ultimate Guide to Heart Rate Variability. --- Follow the podcast on Twitter https://twitter.com/PA_Researcher Follow host Dr Olli Tikkanen on Twitter https://twitter.com/ollitikkanen Follow Fibion on Twitter https://twitter.com/fibion https://www.youtube.com/@PA_Researcher

Christina Reynolds, PhD Christina Reynolds received her Ph.D. in astrophysics from University College London and a Master's degree in software engineering from Harvard University. She has been a Data Scientist with ORCATECH with a focus on developing algorithms for the analysis of ORCATECH's large and diverse data set.  Much of her research career has involved developing software algorithms used to fabricate and test the optics for the European Extremely Large Telescope and the IRIS space telescope. At ORCATECH, she focused on designing a wide variety of algorithms for deriving information about life and health patterns from ORCATECH's sensor data, including characterizing activity and sleep behaviors. _____________________ This podcast episode is sponsored by Fibion Inc. | Better Sleep, Sedentary Behaviour and Physical Activity Research with Less Hassle --- Collect, store and manage SB and PA data easily and remotely - Discover ground-breaking Fibion SENS --- SB and PA measurements, analysis, and feedback made easy.  Learn more about Fibion Research --- Learn more about Fibion Sleep and Fibion Circadian Rhythm Solutions. --- Fibion Kids - Activity tracking designed for children. --- Collect self-report physical activity data easily and cost-effectively with Mimove. --- Explore our Wearables,  Experience sampling method (ESM), Sleep,  Heart rate variability (HRV), Sedentary Behavior and Physical Activity article collections for insights on related articles. --- Refer to our article "Physical Activity and Sedentary Behavior Measurements" for an exploration of active and sedentary lifestyle assessment methods. --- Learn about actigraphy in our guide: Exploring Actigraphy in Scientific Research: A Comprehensive Guide. --- Gain foundational ESM insights with "Introduction to Experience Sampling Method (ESM)" for a comprehensive overview. --- Explore accelerometer use in health research with our article "Measuring Physical Activity and Sedentary Behavior with Accelerometers ". --- For an introduction to the fundamental aspects of HRV, consider revisiting our Ultimate Guide to Heart Rate Variability. --- Follow the podcast on Twitter https://twitter.com/PA_Researcher Follow host Dr Olli Tikkanen on Twitter https://twitter.com/ollitikkanen Follow Fibion on Twitter https://twitter.com/fibion https://www.youtube.com/@PA_Researcher    

This week on Bet the Process, Jeff and Rufus welcome football data scientist Tej Seth to discuss his insights on prediction markets and political candidates, as well as NFL related topics such as roster construction, coaching decisions, and recent deadline trades.

Christina Reynolds, PhD Christina Reynolds received her Ph.D. in astrophysics from University College London and a Master's degree in software engineering from Harvard University. She has been a Data Scientist with ORCATECH with a focus on developing algorithms for the analysis of ORCATECH's large and diverse data set.  Much of her research career has involved developing software algorithms used to fabricate and test the optics for the European Extremely Large Telescope and the IRIS space telescope. At ORCATECH, she focused on designing a wide variety of algorithms for deriving information about life and health patterns from ORCATECH's sensor data, including characterizing activity and sleep behaviors. _____________________ This podcast episode is sponsored by Fibion Inc. | Better Sleep, Sedentary Behaviour and Physical Activity Research with Less Hassle --- Learn more about Fibion Sleep and Circadian Rhythm Solutions: https://sleepmeasurements.fibion.com/ --- Collect, store and manage SB and PA data easily and remotely - Discover groundbreaking Fibion SENS: https://sens.fibion.com/ --- SB and PA measurements, analysis, and feedback made easy.  Learn more about Fibion Research : fibion.com/research --- Follow Fibion on Twitter https://twitter.com/fibion Follow host Dr Olli Tikkanen on Twitter https://twitter.com/ollitikkanen Follow the podcast on Twitter https://twitter.com/PA_Researcher

Neste episódio, recebemos o cientista de dados Paulo da Silva para uma conversa rica sobre os desafios e práticas do ML Ops no cotidiano profissional. Ele também compartilhou uma visão abrangente sobre os conceitos de DevOps e DataOps, destacando suas interseções com o mundo da ciência de dados.

Today, we're talking about building real AI products with foundation models. Not toy demos, not vibes. We'll get into the boring dashboards that save launches, evals that change your mind, and the shift from analyst to AI app builder. Our guide is Hugo Bowne-Anderson, educator, podcaster, and data scientist, who's been in the trenches from scalable Python to LLM apps. If you care about shipping LLM features without burning the house down, stick around. Episode sponsors Posit NordStellar Talk Python Courses Links from the show Hugo Bowne-Anderson: x.com Vanishing Gradients Podcast: vanishinggradients.fireside.fm Fundamentals of Dask: High Performance Data Science Course: training.talkpython.fm Building LLM Applications for Data Scientists and Software Engineers: maven.com marimo: a next-generation Python notebook: marimo.io DevDocs (Offline aggregated docs): devdocs.io Elgato Stream Deck: elgato.com Sentry's Seer: talkpython.fm The End of Programming as We Know It: oreilly.com LorikeetCX AI Concierge: lorikeetcx.ai Text to SQL & AI Query Generator: text2sql.ai Inverse relationship enthusiasm for AI and traditional projects: oreilly.com Watch this episode on YouTube: youtube.com Episode #526 deep-dive: talkpython.fm/526 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Many leaders are trapped between chasing ambitious, ill-defined AI projects and the paralysis of not knowing where to start. Dr. Randall Olson argues that the real opportunity isn't in moonshots, but in the "trillions of dollars of business value" available right now. As co-founder of Wyrd Studios, he bridges the gap between data science, AI engineering, and executive strategy to deliver a practical framework for execution. In this episode, Randy and Hugo lay out how to find and solve what might be considered "boring but valuable" problems, like an EdTech company automating 20% of its support tickets with a simple retrieval bot instead of a complex AI tutor. They discuss how to move incrementally along the "agentic spectrum" and why treating AI evaluation with the same rigor as software engineering is non-negotiable for building a disciplined, high-impact AI strategy. They talk through: How a non-technical leader can prototype a complex insurance claim classifier using just photos and a ChatGPT subscription. The agentic spectrum: Why you should start by automating meeting summaries before attempting to build fully autonomous agents. The practical first step for any executive: Building a personal knowledge base with meeting transcripts and strategy docs to get tailored AI advice. Why treating AI evaluation with the same rigor as unit testing is essential for shipping reliable products. The organizational shift required to unlock long-term AI gains, even if it means a short-term productivity dip. LINKS Randy on LinkedIn (https://www.zenml.io/llmops-database) Wyrd Studios (https://thewyrdstudios.com/) Stop Building AI Agents (https://www.decodingai.com/p/stop-building-ai-agents) Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) Watch the podcast video on YouTube (https://youtu.be/-YQjKH3wRvc)

In the Movers in Our Orbit podcast series, we speak with friends of SSPI who recently made big executive moves. We'll find out what they're doing now and what they hope to achieve in their new roles in the industry. In a rebroadcast of the first episode of season 2, we hear from Kelsey Doerksen, GeoAI Postdoctoral Researcher at Arizona State University and 2021 Promise Award Recipient. Passionate to do impactful work for Earth, in space, Kelsey Doerksen is currently pursuing her PhD at the University of Oxford in the Autonomous Intelligent Machines and Systems Centre for Doctoral Training Program, in the Oxford Applied and Theoretical Machine Learning Group under supervision of Yarin Gal. She is focusing her research on the uses of AI and Machine Learning to enable science discovery and understanding of climate-focused applications (expected graduation, 2025). Kelsey is a Research Affiliate at the NASA Jet Propulsion Lab and a part of the Machine Learning and Instrument Autonomy group, working on the Scientific Understanding from Data Science Strategic Initiative. She is also a Data Scientist with the Climate and Data Environment Unit at UNICEF, building the data pipeline infrastructure and providing analysis necessary to create the UNICEF Children's Climate Risk Index.

I podcasten TEKNOLOGIOPTIMISTENE møter du beslutningstakerne for de store IT-investeringene i bransjen, personene som leder de mest fremoverlente IT-selskapene, personene som løser de viktigste samfunnsoppdragene og menneskene i investeringsselskapene som muliggjør rask vekst hos IT-selskapene. Menneskeskapte klimaendringer er vår tids største trussel, og det grønne skiftet er avhengig av teknologioptimister. Målet vårt med podcastserien er å gi beslutningstakerne innenfor IT i energibransjen kunnskap for bedre beslutninger.Live podcast fra Energibransjens IT-konferanse 2025. Debattdeltagere: Camilla Brustad-Nilsen, Principal Consultant, IteraIngeborg Ligaarden, Head of Data Science, StatnettOla Magnus Hersvik, Founder & CEO, AlvaOle Kristian Grindbakken, Data Scientist, LedeDebattleder: Kjersti Kvile, Journalist, Europower Hosted on Acast. See acast.com/privacy for more information.

Sign up for Alex's first live cohort, about Hierarchical Model building!Get 25% off "Building AI Applications for Data Scientists and Software Engineers"Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:Why GPs still matter: Gaussian Processes remain a go-to for function estimation, active learning, and experimental design – especially when calibrated uncertainty is non-negotiable.Scaling GP inference: Variational methods with inducing points (as in GPflow) make GPs practical on larger datasets without throwing away principled Bayes.MCMC in practice: Clever parameterizations and gradient-based samplers tighten mixing and efficiency; use MCMC when you need gold-standard posteriors.Bayesian deep learning, pragmatically: Stochastic-gradient training and approximate posteriors bring Bayesian ideas to neural networks at scale.Uncertainty that ships: Monte Carlo dropout and related tricks provide fast, usable uncertainty – even if they're approximations.Model complexity ≠ model quality: Understanding capacity, priors, and inductive bias is key to getting trustworthy predictions.Deep Gaussian Processes: Layered GPs offer flexibility for complex functions, with clear trade-offs in interpretability and compute.Generative models through a Bayesian lens: GANs and friends benefit from explicit priors and uncertainty – useful for safety and downstream decisions.Tooling that matters: Frameworks like GPflow lower the friction from idea to implementation, encouraging reproducible, well-tested modeling.Where we're headed: The future of ML is uncertainty-aware by default – integrating UQ tightly into optimization, design, and deployment.Chapters:08:44 Function Estimation and Bayesian Deep Learning10:41 Understanding Deep Gaussian Processes25:17 Choosing Between Deep GPs and Neural Networks32:01 Interpretability and Practical Tools for GPs43:52 Variational Methods in Gaussian Processes54:44 Deep Neural Networks and Bayesian Inference01:06:13 The Future of Bayesian Deep Learning01:12:28 Advice for Aspiring Researchers

Every data scientist is running their own business - it's just that most of those businesses are solo operations with one client: their employer. Unfortunately, most data scientists don't realise this and too many fall into the trap of believing their employer will magically take care of their career development, putting them on the right projects and ensuring they get proper training. The reality is that while bosses usually mean well, they have their own careers to worry about.In this episode, Danny Ruspandini joins Dr. Genevieve Hayes to explore how applying a solo business mindset to your data science career can help you take control of your professional destiny, increase your value within organisations, and create opportunities that others miss.You'll learn:How to become the go-to person for specific problems within your organisation [07:11]The "secondary sale" technique that gets your projects approved even when you're not in the room [14:49]Why focusing on one shiny object at a time accelerates your career faster than juggling multiple priorities [19:06]How to find your signature service that makes you indispensable to your employer [23:00]Guest BioDanny Ruspandini is a brand strategist, business coach and director of Impact Labs Australia. He is also the creator of One Shiny Object, a program for helping solo creatives package what they do into sellable, fixed-price services.LinksConnect with Danny on LinkedInDownload the One Shiny Object frameworkConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss effective reporting and creating reports that tell a story and drive action using user stories and frameworks. You will understand why data dumping onto a stakeholder’s desk fails and how to gather precise reporting requirements immediately. You will discover powerful frameworks, including the SAINT model, that help you move from basic analysis to crucial, actionable decisions. You will gain strategies for anticipating executive questions and delivering a clear, consistent narrative throughout your entire report. You will explore innovative ways to use artificial intelligence as a thought partner to refine your analysis and structure perfect reports. Stop wasting time and start creating reports that generate real business results. Watch now! Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-how-to-create-effective-reporting.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn – 00:00 In this week’s In Ear Insights, it’s almost redundant at this point to say it’s reporting season, but as we hit quarterly ends, yearly ends, things like that, people become reflective and say, “Hey, let’s do some reports.” One of the problems that we see the most with reporting—and I was guilty of this for the majority of my career, particularly the first half—is when you’re not confident about your reporting skills, what do you do? You back the truck up and you pour data all over somebody’s desk and you hope that it overwhelms them so that they don’t ask you any questions, which is the worst possible way to do reporting. So, Katie, as a senior executive, as a leader, when someone delivers reporting to you, what do you get and what do you want to get? Katie Robbert – 00:51 Well, I would start to say reports, like the ones that you were generating, hate to see me coming. Because guess what I do, Chris, I ask a bazillion questions, starting with so what? And I think that’s really the key. As the CEO of Trust Insights, I need a report that tells me exactly what the insights and actions are so that I can do those things. And that is a user story. A user story is a simple three-part sentence: As a Persona, I want so that. If someone is giving me a report and they haven’t asked me for a user story, that’s probably step one. So, Chris, if I say, “All right, if you can pull the monthly metrics, Chris, and put it into a report, I would appreciate it.” Katie Robbert – 01:47 If I haven’t given you a user story, you need to ask me what it is, because that’s the “so what?” Why are we doing this in the first place? We have no shortage of data points. We have no shortage of information about what happened, maybe even why it happened. And that’s a problem because it doesn’t tell a story. What happens is, if you just give me all of that data back, I don’t know what to do with it. And that’s on me, and that’s on you. And so, together, one of us needs to make sure there is a user story. Ideally, I would be providing it, but if I don’t provide it, your first step is to ask for it. That is Step zero. What is the user story? Why am I pulling this report in the first place? Katie Robbert – 02:33 What is it that you, the stakeholder, expect to get out of this report? What is it you need to do with this information? That is Step zero, before you even start looking at data. Christopher S. Penn – 02:44 I love user stories, and I love them, A, for the simplicity, but B, because of that warm and comforting feeling of having covered your ass. Because if I ask you for a user story and you give me one, I build a report for that. Then you come back and say, “But this is this.” Katie Robbert – 03:03 This. Christopher S. Penn – 03:03 I’m like, “You signed off on the user. You gave me the user story, you signed off on the user story. And what you’re asking for is not in the user story.” So I think we need to recalibrate and have you give me maybe some new user stories so you can get what you want. I’m not going to tell you to go F off—not my face. But I’m also going to push back and say, “This wasn’t in the user story.” Because the reason I love user stories is because they’re the simplest but most effective form of requirements gathering. Katie Robbert – 03:36 I would agree with that. When I was a product manager, user stories saved my sanity because my job was to get all of my stakeholders aligned on a single idea. And I’ve told this before, I’d literally go to their office and camp out and get a physical signature on a piece of paper saying, “Yes, this is exactly what you’re agreeing to.” Then, when we would sit in the meeting and the development team or the design team would present the thing, the second somebody would be like, “Well, wait,” I would just hold up the piece of paper and point to their signature. It’s such an effective way to get things done. Katie Robbert – 04:23 Because what happens if you don’t have a user story to start, or any kind of requirements to start, when you’re doing reporting is exactly what you’re talking about. You end up with spreadsheets of data that doesn’t really mean anything. You end up with 60-slide PowerPoint reports with all of these visuals, and every single slide has at least four or five charts on it and some kind of a label. But there’s no story. There’s no, “Why am I looking at this?” When I think about reporting, the very first thing I want to see is—and I would say even go ahead and do this, this is sort of the pro tip— Katie Robbert – 05:00 Whatever the user story was that I gave you, put that right at the top of the report so that when I look at it, I go, “Oh, that’s what I was looking for. Great.” Because chances are, the second you walk away, I’ve already forgotten the conversation—not because it’s not important, but because a million other things have crept up. Now, when you come back to me and say, “This is what I’m delivering,” this is what I need to be reminded of. A lot of stakeholders, people in general, we’re all forgetful. Over-communicate what it is that we’re doing here in the first place. And no one’s going to be mad at that. It’s like, “Oh, now I don’t have to ask questions.” The second thing I look for is sort of that big “So what?” Katie Robbert – 05:45 We call it an executive summary. You can call it the big takeaway, whatever it is. At the very top of the report, I personally look for, “What is the big thing I need to know?” Is everything great? That’s all I need to know. Is everything terrible? I definitely need to know that. Do I need to take six big actions? Great, let me know that. Or, it’s all business as usual. Just give me the 30-second, “Here are the three bullet points that you need to know.” If you have no other time to read this report, that should be the summary at the top. I am going to, even if it’s not right then, dig into the rest of the report. But I may only in that moment be able to look at the summary. Katie Robbert – 06:33 When I see these big slide decks that people present to their executive team or to their board or to whoever they report to, it’s such a missed opportunity to not have the key takeaways right there up front. If you’re asking someone to scroll, scroll, get through it—it’s all the way at the end—they’re not going to do it, and they’re going to start picking apart everything. Even if you’ve done the work to say, “But I already summarized all of that,” it’s not right there in front of them. Do yourself a favor. Whatever it is the person you’re presenting this to needs to know, put it right in front of their face immediately. Christopher S. Penn – 07:13 Back in the day, we came up with a framework called the SAINT framework, which stands for Summary, Analysis, Insights, Next Steps, Timeline. Where I’ve seen that go wrong is people try to do too much in the summary. From Analysis, Insights, Next Steps, and Timelines, there should be one to three bullets from each that become the summary. Katie Robbert – 07:34 And that’s it? Christopher S. Penn – 07:35 Yeah, that’s it. In terms of percentages, what we generally recommend to people is that Analysis should be 10% to 15% of the report. What happened? Data Insights should be 10% to 15% of the report. Why did those things happen? We did this, and this is what happened. Or this external factor occurred, and this has happened. The remaining 50% to 60% of the report should be equally split between Next Steps—what are you going to do about it?—and Timeline—when are you going to do it? Those next steps and timeline become the decisions that you need the stakeholder to make and when they need to do it so that you get done what you need to get done. Christopher S. Penn – 08:23 That’s the part we call the three “What’s”: What happened? So what? Now what? As you progress through any measurement framework, any reporting framework, the more time you spend on “Now what,” the better a stakeholder is likely to like the report. You should absolutely, if the stakeholder wants it, provide the appendix of the data itself if they want to pour through it. But at the highest level, it should be, “Hey Katie, our website traffic was down 15% last month. The reason for it was because it was a shorter month, a lot of holidays. What we need to do is we need to spin up a small paid campaign, $500 for the next month, to boost traffic back to our key pages. I need a decision from you by October 31st. Go, no go.” Christopher S. Penn – 09:18 And that would be the short summary because that fulfills your user story of, “As a CEO, I need to know what’s going on in marketing so that I can forecast and plan for the future.” Katie Robbert – 09:31 Yep. I would say the other thing that people get wrong is trying to do too much in one report. We talk about this when we talk about dashboard development or any kind of storytelling with data. If I give you three user stories, for example, what I don’t want to see is you trying to cram everything into one report to fulfill every single user story. That’s confusing. There is nothing wrong with—because you already have all the data anyway—just giving me three different stories that fulfill the question that I’m asking. You might be like, “Well, I’m only supposed to do one monthly report. Now you’re asking me to do three monthly reports.” No, I’m not. I’m asking you to take a look at the data and answer each individual question, which you should be doing anyway. Katie Robbert – 10:29 This is the thing that drives me nuts: the lack of consistency from top to bottom. If you think of where a report starts and where it ends, I’m the person who looks at the ending and goes back through and says, “Was there a consistent thread? Am I still looking at the same information at the end that I started with at the beginning?” If you’re telling me actions about my email marketing, but you started with data about my web traffic, my eyebrows are up and I’m like, “I don’t get how we got from A to B.” That’s a big thing that I personally look for—that consistent thread throughout the entire report. If you’re giving me data on web traffic, I then expect the next steps to be about web traffic, not about a different channel. Katie Robbert – 11:20 If you have things you need to tell me about the email marketing data, start with that, because I’m going to be looking for, “Why are we talking about email marketing when our social media was where you started?” That drives me nuts to no end because then it actually puts more work on me and you: “Okay, let’s backtrack, let’s do this over again. Let’s figure out the big thing.” What I was always taught as the person executing the reports is: anticipate the questions, get to know your stakeholder. Anyone who works for me knows me, they know I’m going to ask a million questions. So one of the expectations I have of someone doing a task that I’ve delegated is know that I’m going to ask a million questions about it. Katie Robbert – 12:21 I really want you to examine and think through, “What questions would Katie ask? How do I get her off my back? How do I get her to stop being a pain in the butt and ask me a million questions?” And you’re laughing, Chris, but it’s an effective way to think through a full, well-rounded approach to any kind of a deliverable. This is what we talk about when we talk about gathering business requirements. Have you thought of what happens if we don’t do it? Have you thought of the risks? Having that full set of requirements and questions answered saves you so much time in the execution. It’s very much the same thing. Katie Robbert – 13:01 If I’m delivering something to you, Chris, the way that I’m thinking about it is, “What’s the first question Chris is going to ask me about this? Okay, can I answer that? Great. What’s the second question Chris is going to ask me about this?” And I keep going until I’m out of questions. It occurs to me that you can use generative AI to do this exercise. One of the things, Chris, that you teach in prompt engineering is the magic trick is to have the system ask you one question at a time until it has everything it needs. If you have the time and the luxury to build a synthetic version of your stakeholder, you can do that same thing. Katie Robbert – 13:48 Put together your report, give it the user story, and say, “Ask me one question at a time until there are no questions left to ask.” Christopher S. Penn – 13:57 Exactly. And if you want a scratch way to do that, one of the fastest ways is for you to take past emails or past conference call or Zoom meeting transcripts or your stakeholder’s LinkedIn profile, put that all into a single system—a GPT, a GEM, a Claude project, whatever you want to do—and say, “Behave as the stakeholder, understand what’s important to them, and then ask me one question at a time about my report until there are no questions left.” It’s super valuable, very easy way to do it. I want to go back to the thing about dashboarding and reporting because I wanted to show this. For those who are just listening, this is the cockpit of the Airbus A220, which is a popular aircraft. Christopher S. Penn – 14:42 One of the things you’ll notice: at first it looks very overwhelming, but one of the things you’ll notice is that every screen here serves one function. The altitude and course screen on the far left serves just to tell the pilot where they’re going and where the plane is right now. The navigation screen shows you where the plane is and what’s nearby. Even the controls—when you look at the controls, every lever is a different shape so that you can feel what lever your hand is on. A lot of thought has gone into this to put only the essential things that a pilot needs to get their job done. There is nothing extraneous, there is nothing wasted. Christopher S. Penn – 15:30 Because any amount of waste, any amount of confusion in a very high-stakes situation, can literally result in everyone dying. From this, we could take lessons for our reporting to say, “Does this report serve a single user story and does it do that well? Is it focused on that?” Going back to what you’re saying earlier, if there are multiple user stories, there should be multiple reports, because you can’t make everything be everything to everyone. You could not put every function on this plane in one screen. You will die! You’ll fly straight into a mountain because you’re like, “Where’s my position? What’s my GPS? Where’s the nearby? Holy crap.” By the time you figure out what’s on the screen, you’ve run into a mountain. Christopher S. Penn – 16:13 That design lesson—it really is information architecture—and design is the heart and soul of good reporting. Now, here’s the question: Why don’t we teach that? Katie Robbert – 16:27 Well, you and I teach that, but. Christopher S. Penn – 16:29 Well, yes, Trust Insights. I mean, for people who are, when you look at, for example, courses taught in business school, things we’ve both been through, that we’ve both enjoyed the lovely experience of going through a business program, a master’s degree. Katie Robbert – 16:44 Program, our own projects, all the good stuff. Christopher S. Penn – 16:47 Yeah, none of that was ever taught. Katie Robbert – 16:49 I’m speculating, but honestly, what I was about to speculate is contradictory, so that’s not helpful. No, because I was going to say, because it’s taught from the perspective of the user, the person executing it, but that would argue that, okay, that’s what they should be teaching is how to put together that kind of reporting. I actually don’t remember any kind of course or any kind of discussion about putting together some kind of data storytelling, because that’s really what we’re talking about—telling a story with the data. In business school, you get a lot of, “Here are 12 case studies about global companies and why they either succeeded or failed.” But there’s nothing about the day-to-day in terms of how they actually got to where they are. Katie Robbert – 17:54 It’s, “Henry Ford was this guy who made decisions,” or “Here’s how Wells Fargo,” or “Here’s how an international clothing company, Zara, made all their money.” That’s all really helpful to know from a big picture standpoint. I feel like a lot of what’s taught in business school is big picture unless you take stats. But stats also doesn’t teach you how to do data storytelling; it just teaches you how to analyze the data. So I actually think that it’s just a big missing component because we don’t really think about it. We think that, “Oh, it’s just a marketing function.” And even in marketing classes, you don’t really get to the data storytelling part. You get to more case studies on Facebook or “Here’s how to set up something in Google Ads.” Katie Robbert – 18:46 But then it doesn’t really tell you what to do with the data afterwards. So it’s a huge missed opportunity. I think it’s just not taught in general. I could be mistaken. It’s been a hot second since I was in business school, but my assumption is that it’s not seen as an essential part of the degree. And yet, when you get into the real world, if you can’t tell a story with the data, then you’re at a disadvantage. If you’re asking me personally as a CEO, I am open to thoughts, I’m open to ideas, I’m open to opinions. I am not open to you winging it. I’m not open to vibes. I’m not open to, “Let me just experiment in a production environment.” I’m not open to any of that. Katie Robbert – 19:36 I am open to something where you’ve done the research and you said, “I had this thought, here’s the data that backs it up, and here’s the plan moving forward.” You can use the SAINT framework for a proposal for a new idea. You can use a SAINT framework for a business plan or a business case to say, “I think we should do something different.” I’m always going to look for the data that supports your opinions. Christopher S. Penn – 20:05 Reporting is kind of a horizontal function in that it spans every department. Finance has to do reporting, and sometimes they have regulatory reasons that reporting must be in this format to be compliant with the law. HR, sales, operations—everybody has reporting. I think it’s one of those cases, like the tragedy of the commons. I don’t know if that’s the right analogy or not, but because everybody has to do it, nobody teaches it. Everybody assumes, “Oh well, that’s somebody else’s job to do that.” As a result, you end up with hot salad when it comes to the quality of reports you get. Christopher S. Penn – 20:45 When we worked at the PR agency together, the teams would put together 84-page slide decks of “Here’s what we did,” and it was never connected to results; it was never connected to stakeholders’ user stories. To your point, the simplest thing that you could do as a business professional today is to take that user story from your stakeholder and put it into generative AI with your raw data. Use Google Colab—that would be a great choice—and say, “Here’s my stakeholder’s user story of all this data. Help me understand what data is directly connected to my user story, what data is not, what data is missing that I should have, and what data is unnecessary that I can just ignore.” Christopher S. Penn – 21:34 Then, help me plan out a dashboard of the top three things that I need my stakeholder to pay attention to. That’s where you use SAINT, putting the SAINT framework as a literal knowledge block that you drop right into the chat and say, “Help me write a SAINT framework report based on this data and my user’s user story.” I guarantee if you do that, you will take your stakeholder from mildly happy to deliriously happy in one report because they’ll look at it and go, “You understand what I need to do my job.” Katie Robbert – 22:12 I would say you don’t even have to use Google Colab for something like that, especially if you’re not even really sure where to start. Chris, you’re talking about a thorough understanding of what all of the data means. If you want to even take a step back and say, “This is my stakeholder’s user story. These are the platforms that I have to work with. Can I satisfy this user story with the data that I think I have access to? What should I use? What metrics would answer this question? What am I missing?” You can do the same exercise but just keep it a little bit more high level and be like, “I have Google Analytics 4, I have HubSpot, I have Mautic. Can I answer the question being asked?” And the answer might be no. Katie Robbert – 23:03 If the generative AI says no, you can’t answer the question being asked, make sure it tells you what you need to answer that question so that you can go back to your stakeholder. Be like, “This was your user story. This is what you wanted to know. I don’t have that information. Can you get it for me? Can you help me get it? What do we need to do? Or can you adjust your expectations?” Which is probably not the way to say it to a stakeholder because they never really enjoy that. We always like to think that we know best and we know everything and that we’re never wrong, which is true 99% of the time. Christopher S. Penn – 23:41 So, to recap, use user stories, please, to get validation of your reporting requirements first. Then use any good data storytelling framework, including the SAINT framework, including the 5 Ps—use whatever you’ve got for frameworks—and use generative AI as a thought partner to say, “Can I understand what’s good, what’s bad, what’s missing, and what’s unnecessary from my data to tell the story to my stakeholder?” If you got some thoughts about how you do reporting or how you could be doing reporting better, pop by our free Slack Group. Go to Trust Insights.AI/analyticsformarketers, where you and over 4,500 marketers are asking and answering each other’s questions every single day. Wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on instead, go to Trust Insights.AI/TIPodcast. Christopher S. Penn – 24:26 You can find us at all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one. Katie Robbert – 24:38 Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology (MarTech) selection and implementation, and high-level strategic consulting. Katie Robbert – 25:42 This includes emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, Dall E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members, such as a CMO or Data Scientist, to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In Ear Insights podcast, the Inbox Insights newsletter, the So What Live Stream, webinars, and keynote speaking. What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights is adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at exploring and explaining complex concepts clearly through compelling narratives and visualizations. Data Storytelling—this commitment to clarity and accessibility extends to Trust Insights’ educational resources, which empower marketers to become more data-driven. Katie Robbert – 26:48 Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information. Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

In this conversation, Dave Prior and Hugo Bowne-Anderson discuss the evolving landscape of AI and data science, focusing on the role of AI agents in solving business problems. Hugo shares insights on how to effectively implement AI solutions, the importance of understanding the underlying data, and the need for continuous improvement in AI systems. They also touch on the skills necessary for navigating the AI landscape, the value of collaboration between technical and non-technical teams, and the importance of assessing the value of AI projects. Hugo concludes by offering a course on building AI applications, emphasizing the iterative nature of AI development. Takeaways - Hugo emphasizes the importance of data in AI applications. - AI agents can automate tasks but require human oversight. - Understanding the problem is crucial before implementing AI solutions. - Prompt engineering remains a valuable skill alongside learning about agents. - Consultants should educate clients on practical AI applications. - AI systems should be built incrementally and iteratively. - Value assessment in AI projects should focus on efficiency and cost savings. - Continuous improvement is essential for AI systems to remain effective. - Experimentation with AI tools can lead to innovative solutions. - Collaboration between technical and non-technical teams is vital for successful AI implementation. Chapters 00:00 Introduction to Data and AI Literacy 06:14 Understanding AI Agents vs. LLMs 09:18 The Role of Agents in Business Solutions 12:21 Navigating the Future of AI and Agents 15:24 Consulting and Client Education in AI 18:37 Building Incremental AI Solutions 21:29 The Future of AI Coding and Debugging 24:32 Prototyping with AI: Challenges and Solutions 25:32 Leveraging AI for User Insights and Competitive Analysis 27:29 Understanding Value in AI Development 32:05 The Role of Product Managers in AI Integration 33:00 AI as an Instrument: The Human Element 35:33 Getting Started with AI: Practical Steps for Teams 38:51 Building AI Applications: Course Overview and Insights Links from the Podcast: Stop Building AI Agents - Here's what you should build instead (Article) https://www.decodingai.com/p/stop-building-ai-agents Anthropic https://www.anthropic.com/engineering/multi-agent-research-system The Colgate Study https://www.pymc-labs.com/blog-posts/AI-based-Customer-Research Hugo's Course (Starts November 3, 2025) Building AI Applications for Data Scientists and Software Engineers (with a 25% discount) https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=drunkenpm (You can use the discount code drunkenpm to get 25% off) How To Be A Podcast Guest with Jay Hrcsko https://youtu.be/vkNbgwcolIM Contacting Hugo LinkedIn https://www.linkedin.com/in/hugo-bowne-anderson-045939a5/ Substack https://hugobowne.substack.com/ Contacting Dave Linktree: https://linktr.ee/mrsungo Dave's Classes: https://www.eventbrite.com/cc/dave-prior-classes-4758623

Vania Pecheu Bovet est Head of Global Data & AI Strategy chez Carrefour et porte notamment la stratégie IA générative du Groupe, développée en France et maintenant déployée dans 8 pays.On aborde :

In this talk, Hugo Bowne-Anderson, an independent data and AI consultant, educator, and host of the podcasts Vanishing Gradients and High Signal, shares his journey from academic research and curriculum design at DataCamp to advising teams at Netflix, Meta, and the US Air Force. Together, we explore how to build reliable, production-ready AI systems—from prompt evaluation and dataset design to embedding agents into everyday workflows.You'll learn about: How to structure teams and incentives for successful AI adoptionPractical prompting techniques for accurate timestamp and data generationBuilding and maintaining evaluation sets to avoid “prompt overfitting”- Cost-effective methods for LLM evaluation and monitoringTools and frameworks for debugging and observing AI behavior (Logfire, Braintrust, Phoenix Arise)The evolution of AI agents—from simple RAG systems to proactive, embedded assistantsHow to escape “proof of concept purgatory” and prioritize AI projects that drive business valueStep-by-step guidance for building reliable, evaluable AI agentsThis session is ideal for AI engineers, data scientists, ML product managers, and startup founders looking to move beyond experimentation into robust, scalable AI systems. Whether you're optimizing RAG pipelines, evaluating prompts, or embedding AI into products, this talk offers actionable frameworks to guide you from concept to production.LINKSEscaping POC Purgatory: Evaluation-Driven Development for AI Systems - https://www.oreilly.com/radar/escaping-poc-purgatory-evaluation-driven-development-for-ai-systems/Stop Building AI Agents - https://www.decodingai.com/p/stop-building-ai-agentsHow to Evaluate LLM Apps Before You Launch - https://www.youtube.com/watch?si=90fXJJQThSwGCaYv&v=TTr7zPLoTJI&feature=youtu.beMy Vanishing Gradients Substack - https://hugobowne.substack.com/Building LLM Applications for Data Scientists and Software Engineers https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=datatalksclubTIMECODES:00:00 Introduction and Expertise04:04 Transition to Freelance Consulting and Advising08:49 Restructuring Teams and Incentivizing AI Adoption12:22 Improving Prompting for Timestamp Generation17:38 Evaluation Sets and Failure Analysis for Reliable Software23:00 Evaluating Prompts: The Cost and Size of Gold Test Sets27:38 Software Tools for Evaluation and Monitoring33:14 Evolution of AI Tools: Proactivity and Embedded Agents40:12 The Future of AI is Not Just Chat44:38 Avoiding Proof of Concept Purgatory: Prioritizing RAG for Business Value50:19 RAG vs. Agents: Complexity and Power Trade-Offs56:21 Recommended Steps for Building Agents59:57 Defining Memory in Multi-Turn ConversationsConnect with HugoTwitter - https://x.com/hugobowneLinkedin - https://www.linkedin.com/in/hugo-bowne-anderson-045939a5/Github - https://github.com/hugobowneWebsite - https://hugobowne.github.io/Connect with DataTalks.Club:Join the community - https://datatalks.club/slack.htmlSubscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQCheck other upcoming events - https://lu.ma/dtc-eventsGitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

I recently sat down for a podcast chat with the teacher Caroline Keep about our careers, about all things disability and awareness, how it feels to get a diagnosis and how you can use your disability or condition as a super power!

In this week's MBA Admissions podcast we began by discussing the current state of the MBA admissions season, with interview invites continuing to roll out. This week, John's Hopkins / Carey has its Round 1 deadline, UPenn / Wharton is scheduled to release its Round 1 interview invites and UVA / Darden and Johns Hopkins / Carey are scheduled to release their Early Action Round decisions. Graham highlighted several upcoming events being hosted by Clear Admit that begin this week, including a Real Humans series and a series focused on MBA programs in different regions of the United States. Signups for all these events are here, https://www.clearadmit.com/events Graham also highlighted our next livestream AMA, which is now scheduled for Monday, October 27; here's the link to Clear Admit's YouTube channel: https://bit.ly/cayoutubelive. Graham recognized Stanford's 100-year anniversary by quizzing Alex on some of the history of the MBA Program degree and business schools in general. Graham then noted several recently published admissions tips which focus interview preparation, as well as an admissions tip that focuses on assessment days that are offered by a few top MBA programs. Graham highlighted a Real Humans piece that focuses on MBA students at Columbia Business School, and also reviewed Yale SOM's Class of 2027 profile, which appears to be very impressive. For this week, for the candidate profile review portion of the show, Alex selected three ApplyWire entries. This week's first MBA admissions candidate has a remarkably high salary, as a software engineer at a FANG company. We hope they will consider retaking the GRE. This week's second MBA applicant has a very high GMAT score of 705. They want to be in Austin Texas, post MBA. They are also very concerned with gaining scholarship to help defray costs. The final MBA candidate is a data scientist and is debating their post MBA goals. They want to do investment banking but worry how that would appear for adcom. This episode was recorded in Paris, France and Cornwall, England. It was produced and engineered by the fabulous Dennis Crowley in Philadelphia, USA. Thanks to all of you who've been joining us and please remember to rate and review this show wherever you listen!

Most AI teams find their multi-agent systems devolving into chaos, but ML Engineer Alex Strick van Linschoten argues they are ignoring the production reality. In this episode, he draws on insights from the LLM Ops Database (750+ real-world deployments then; now nearly 1,000!) to systematically measure and engineer constraint, turning unreliable prototypes into robust, enterprise-ready AI. Drawing from his work at Zen ML, Alex details why success requires scaling down and enforcing MLOps discipline to navigate the unpredictable "Agent Reliability Cliff". He provides the essential architectural shifts, evaluation hygiene techniques, and practical steps needed to move beyond guesswork and build scalable, trustworthy AI products. We talk through: - Why "shoving a thousand agents" into an app is the fastest route to unmanageable chaos - The essential MLOps hygiene (tracing and continuous evals) that most teams skip - The optimal (and very low) limit for the number of tools an agent can reliably use - How to use human-in-the-loop strategies to manage the risk of autonomous failure in high-sensitivity domains - The principle of using simple Python/RegEx before resorting to costly LLM judges LINKS The LLMOps Database: 925 entries as of today....submit a use case to help it get to 1K! (https://www.zenml.io/llmops-database) Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) Watch the podcast video on YouTube (https://youtu.be/-YQjKH3wRvc)

Voices is a new mini-series from Humanitarian AI Today. In daily five-minute flashpods we pass the mic to humanitarian experts and technology pioneers, to hear about new projects, events, and perspectives on critical topics. In this flashpod, Annie Brown, a Data Scientist with Humane Intelligence, talks about her team's Bias Bounty program and how to get involved in an interview with Brent Phillips, Producer of Humanitarian AI Today. They discuss Humane Intelligence's work focusing on collaboratively designing and running rigorous evaluations that make AI systems more accountable, responsible, and fair, their bias bounty program and the strategy behind it as well as touch on how volunteers can get involved and launch their research. Substack notes: https://humanitarianaitoday.substack.com/p/annie-brown-from-humane-intelligence

We react to conference and a recent video a data scientist did about the Book of Mormon.

Is your AI built on quicksand? Learn how bad data, poisoned datasets, and deep fakes threaten your AI systems, and what to do about it.In this episode of CXOTalk (#896), AI luminaries Dr. David Bray and Dr. Anthony Scriffignano reveal the hidden dangers lurking in your AI foundations. They share practical strategies for building trustworthy AI systems and escaping the "AI quicksand" that traps countless organizations.

My former classmate, Anthony Scriffignano, is a computer science and data science veteran with over 40 years of experience, from chief data scientist at Dunn & Bradstreet to an inventor who holds more than 100 patents in fraud detection and geospatial tech. This is an amazing episode where he even advises us to watch closely:- The actual progression of AI versus the hype - What investors should invest in now...and more!-Axel***Watch this episode on YouTube https://youtu.be/-KvxRMggDiMSubscribe for more investing, age of abundance content on YouTube: @idealwealthgrowerEpisode deep dive + Simple action plan to get you ahead of the 95% https://tinyurl.com/ep-chris-n***Start taking action right NOW! 

در این قسمت، افتخار داشتیم با سیاوش حکیم‌الهی، دانشمند ارشد داده(Senior Principal Data Scientist)در شرکتAutodeskگفتگو کنیم. او با تکیه بر پیش‌زمینه‌ی قوی خود در داده‌ساینس، هوش مصنوعی و مهندسی، به شرکتاتودسک در ادغام هوش مصنوعی در نرم‌افزارهایی مانندAutodesk Revit، Autodesk Forma، Autodesk Fusionو سایر محصولات خود کمک می کند.https://www.linkedin.com/in/siavash-hakim-elahi/Data science mentor and AI enthusiast with over 10 years of solid experience in artificial intelligence, generative AI, stochastic model calibration, optimization, data-driven modeling, hybrid modeling, physics-based modeling, time-series forecasting and analysis, and anomaly detection.In this episode, we had the privilege of speaking with Siavash Hakim Elahi, Senior Principal Data Scientist at Autodesk. With his background in data science, artificial intelligence, and engineering, he is helping Autodesk integrate AI into its software platforms, including Autodesk Revit, Autodesk Forma, Autodesk Fusion, and others.

The Doers Nepal – Nepal's Longest Running Business Podcast Most people think success means staying where everyone dreams to be. But what if walking away from your dream job is the first step toward real leadership? In this episode, Rosha Pokharel, Founder of SolvDat and former Lead Data Scientist at IBM Watson, shares her journey from being a math lover in Nepal to leading global AI projects and then leaving it all to create a new chapter for women in leadership. In this conversation, Rosha reveals: Why being a “good girl” has cost women more opportunities than lack of talent ever did How she scaled from Data Scientist at IBM to Director of AI in a Fortune 25 company The 4 pillars every AI project must follow to avoid the 90% failure rate The most important skill women must develop to thrive in leadership Why women should stop settling in seats built for men and start building their own Whether you are a student, professional, or dreamer curious about AI, leadership, and empowerment, this episode will challenge how you think about success, courage, and breaking barriers.

Most AI teams find "evals" frustrating, but ML Engineer Hamel Husain argues they're just using the wrong playbook. In this episode, he lays out a data-centric approach to systematically measure and improve AI, turning unreliable prototypes into robust, production-ready systems. Drawing from his experience getting countless teams unstuck, Hamel explains why the solution requires a "revenge of the data scientists." He details the essential mindset shifts, error analysis techniques, and practical steps needed to move beyond guesswork and build AI products you can actually trust. We talk through: The 10(+1) critical mistakes that cause teams to waste time on evals Why "hallucination scores" are a waste of time (and what to measure instead) The manual review process that finds major issues in hours, not weeks A step-by-step method for building LLM judges you can actually trust How to use domain experts without getting stuck in endless review committees Guest Bryan Bischof's "Failure as a Funnel" for debugging complex AI agents If you're tired of ambiguous "vibe checks" and want a clear process that delivers real improvement, this episode provides the definitive roadmap. LINKS Hamel's website and blog (https://hamel.dev/) Hugo speaks with Philip Carter (Honeycomb) about aligning your LLM-as-a-judge with your domain expertise (https://vanishinggradients.fireside.fm/51) Hamel Husain on Lenny's pocast, which includes a live demo of error analysis (https://www.lennysnewsletter.com/p/why-ai-evals-are-the-hottest-new-skill) The episode of VG in which Hamel and Hugo talk about Hamel's "data consulting in Vegas" era (https://vanishinggradients.fireside.fm/9) Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) Watch the podcast video on YouTube (https://youtube.com/live/QEk-XwrkqhI?feature=share) Hamel's AI evals course, which he teaches with Shreya Shankar (UC Berkeley): starts Oct 6 and this link gives 35% off! (https://maven.com/parlance-labs/evals?promoCode=GOHUGORGOHOME) https://maven.com/parlance-labs/evals?promoCode=GOHUGORGOHOME

This episode is a little different because Alun turns the microphone toward me. After 456 episodes, it feels both strange and exciting to be the “guest” on my own show. Together, we reflect on the journey so far and then dive into a topic close to both our hearts: the human skills that make statisticians and quantitative scientists truly effective. We talk about leadership as helping others accomplish something, how to influence people across functions (not just departments), why being known inside your organization matters, and how presentation skills can make or break your impact. We wrap up with three actions you can start applying right away.

SumerSports data scientist Sam Bruchhaus joins the show to discuss his takeaways from what he's seen of the Vikings over the first three weeks of the season. The Purple Insider podcast is brought to you by FanDuel. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, I chat with the wonderful and very sweet Jules Vasquez (Brain Body by Jules) about how to keep yours and your children's brains healthy in today's modern world. We dive into the science of stress and inflammation, and how they destroy the brain. The importance of sleep and dreaming, blue light, and even the role that technology (like ChatGPT) might play in shaping our memory and cognitive health (decline). I soooo enjoyed this conversation and was blown away by Jules and her knowledge, including how she delivers information. I think you will too! ✨ What you'll learn in this episode: Stress and Inflammations impact on the brain How the amygdala, the brain's emotional processing center, plays a critical role in the stress the "fight-or-flight" or "rest-and-digest" reaction The prefrontal cortex's part in rational logical thinking How blue light and poor sleep impair brain performance Why you need to dream for memory Natural and nutritional approaches to supporting your brain Is ChatGPT destroying our brain performance and ability to think for ourselves And LOTS more! If you've ever wondered how to support your brain against the challenges of modern life and aging, this conversation will give you both clarity and practical tools.

In our September 2025 Community Chat, and with youth members of the Caribbean tech community, Data Scientist and Researcher, Julie Koon Koon of Trinidad and Tobago, and the Co-CEO of Orbtronics and Rifbid, Keeghan Patrick, of Saint Lucia, the panel discusses:   *  whether the use of AI for digital transformation and innovation is evident in the Caribbean region;   *  women's empowerment in ICTs; and   *  the changing job market due to AI and the implications for new and imminent graduates.    The episode, show notes and links to some of the things mentioned during the episode can be found on the ICT Pulse Podcast Page (www.ict-pulse.com/category/podcast/)       Enjoyed the episode?  Do rate the show and leave us a review!       Also, connect with us on: Facebook – https://www.facebook.com/ICTPulse/   Instagram –  https://www.instagram.com/ictpulse/   Twitter –  https://twitter.com/ICTPulse   LinkedIn –  https://www.linkedin.com/company/3745954/admin/   Join our mailing list: http://eepurl.com/qnUtj    Music credit: The Last Word (Oui Ma Chérie), by Andy Narrell Podcast editing support:  Mayra Bonilla Lopez   ---------------

John Berryman (Arcturus Labs; early GitHub Copilot engineer; co-author of Relevant Search and Prompt Engineering for LLMs) has spent years figuring out what makes AI applications actually work in production. In this episode, he shares the “seven deadly sins” of LLM development — and the practical fixes that keep projects from stalling. From context management to retrieval debugging, John explains the patterns he's seen succeed, the mistakes to avoid, and why it helps to think of an LLM as an “AI intern” rather than an all-knowing oracle. We talk through: - Why chasing perfect accuracy is a dead end - How to use agents without losing control - Context engineering: fitting the right information in the window - Starting simple instead of over-orchestrating - Separating retrieval from generation in RAG - Splitting complex extractions into smaller checks - Knowing when frameworks help — and when they slow you down A practical guide to avoiding the common traps of LLM development and building systems that actually hold up in production. LINKS: Context Engineering for AI Agents, a free, upcoming lightning lesson from John and Hugo (https://maven.com/p/4485aa/context-engineering-for-ai-agents) The Hidden Simplicity of GenAI Systems, a previous lightning lesson from John and Hugo (https://maven.com/p/a8195d/the-hidden-simplicity-of-gen-ai-systems) Roaming RAG – RAG without the Vector Database, by John (https://arcturus-labs.com/blog/2024/11/21/roaming-rag--rag-without-the-vector-database/) Cut the Chit-Chat with Artifacts, by John (https://arcturus-labs.com/blog/2024/11/11/cut-the-chit-chat-with-artifacts/) Prompt Engineering for LLMs by John and Albert Ziegler (https://amzn.to/4gChsFf) Relevant Search by John and Doug Turnbull (https://amzn.to/3TXmDHk) Arcturus Labs (https://arcturus-labs.com/) Watch the podcast on YouTube (https://youtu.be/mKTQGKIUq8M) Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)

Most data scientists have never heard of decision science, yet this discipline - which dates back to WWII - may hold the key to solving one of data science's biggest problems: the 87% project failure rate. While data scientists excel at building models that predict outcomes, decision scientists focus on modelling the actual business decisions that need to be made - a subtle but crucial difference that dramatically improves success rates.In this episode, Prof Jeff Camm joins Dr. Genevieve Hayes to explore how decision science approaches problems differently from data science, why decision science approaches lead to higher success rates, and how data scientists can integrate these techniques into their own work.This episode reveals:The fundamental difference between modelling data and modelling decisions [04:12]Why decision science projects have historically had higher success rates than current data science efforts [10:42]How to avoid the "ill-defined problem" trap that kills most data science projects [21:12]The medical doctor approach to understanding what business problems really need solving [22:28]Guest BioProf Jeff Camm is a decision scientist and the Inmar Presidential Chair in Analytics at the Wake Forest University School of Business. His research has been featured in top-ranking academic journals and he is the co-author of ten books on business statistics, management science, data visualisation and business analytics.LinksConnect with Jeff on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

While most conversations about generative AI focus on chatbots, Thomas Wiecki (PyMC Labs, PyMC) has been building systems that help companies make actual business decisions. In this episode, he shares how Bayesian modeling and synthetic consumers can be combined with LLMs to simulate customer reactions, guide marketing spend, and support strategy. Drawing from his work with Colgate and others, Thomas explains how to scale survey methods with AI, where agents fit into analytics workflows, and what it takes to make these systems reliable. We talk through: Using LLMs as “synthetic consumers” to simulate surveys and test product ideas How Bayesian modeling and causal graphs enable transparent, trustworthy decision-making Building closed-loop systems where AI generates and critiques ideas Guardrails for multi-agent workflows in marketing mix modeling Where generative AI breaks (and how to detect failure modes) The balance between useful models and “correct” models If you've ever wondered how to move from flashy prototypes to AI systems that actually inform business strategy, this episode shows what it takes. LINKS: The AI MMM Agent, An AI-Powered Shortcut to Bayesian Marketing Mix Insights (https://www.pymc-labs.com/blog-posts/the-ai-mmm-agent) AI-Powered Decision Making Under Uncertainty Workshop w/ Allen Downey & Chris Fonnesbeck (PyMC Labs) (https://youtube.com/live/2Auc57lxgeU) The Podcast livestream on YouTube (https://youtube.com/live/so4AzEbgSjw?feature=share) Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)

Too often, AI breaks in the wild. Why? CXOTalk 890 dissects the adversarial economy with Steven C. Daffron (fintech private equity leader) and Anthony Scriffignano (distinguished data scientist), hosted by Michael Krigsman. Discover the challenges of **ai implementation** and the strategies needed to navigate the **future of work** in an AI-driven world. Stay informed with expert insights on CXOTalk. What you'll learn:How AI enables and masks adversarial behaviorMisaligned incentives, data/model drift, and biasGovernance vs. regulation; resilient metrics and KPIsInvestor/CFO implications and talent/education needs 

Welcome to English Plus Podcast's deep dive into "Living in the Age of AI"! This week, we tackle one of the most pressing questions of our time: "What are the top current and future jobs related to AI, and what can you do to be qualified to do them?" Join us as we demystify the professional landscape of Artificial Intelligence, moving beyond the headlines to reveal the concrete opportunities available to you, not just as a user, but as a shaper of this extraordinary era. We dissect high-demand roles like Machine Learning Engineers, Data Scientists, AI Ethicists, and AI Product Managers, outlining the foundational skills and educational pathways required to excel. But we don't stop there. We also cast our gaze to the horizon, exploring emerging roles such as Prompt Engineers, AI Integration Specialists, and Human-AI Teaming Specialists – positions that will define the next wave of AI innovation. We provide actionable advice on cultivating a growth mindset, mastering essential technical skills (like Python and data literacy), and strategically choosing your educational journey, whether through traditional degrees, online courses, or intensive bootcamps. Crucially, we emphasize the importance of building a robust project portfolio, developing invaluable domain expertise, and honing critical soft skills like communication, ethical reasoning, and adaptability. This episode is your comprehensive blueprint for navigating the AI career revolution, designed to empower you with foresight and practical steps. Remember, this episode serves as a powerful introduction. True mastery in the Age of AI demands sustained inquiry, diligent research, and a commitment to lifelong learning. Consider this your essential guide to not just surviving, but thriving and making a meaningful impact in the world of Artificial Intelligence. To unlock full access to all our episodes, consider becoming a premium subscriber on Apple Podcasts or Patreon. And don't forget to visit englishpluspodcast.com for even more content, including articles, in-depth studies, and our brand-new audio series and courses now available in our Patreon Shop!

While many people talk about “agents,” Shreya Shankar (UC Berkeley) has been building the systems that make them reliable. In this episode, she shares how AI agents and LLM judges can be used to process millions of documents accurately and cheaply. Drawing from work on projects ranging from databases of police misconduct reports to large-scale customer transcripts, Shreya explains the frameworks, error analysis, and guardrails needed to turn flaky LLM outputs into trustworthy pipelines. We talk through: - Treating LLM workflows as ETL pipelines for unstructured text - Error analysis: why you need humans reviewing the first 50–100 traces - Guardrails like retries, validators, and “gleaning” - How LLM judges work — rubrics, pairwise comparisons, and cost trade-offs - Cheap vs. expensive models: when to swap for savings - Where agents fit in (and where they don't) If you've ever wondered how to move beyond unreliable demos, this episode shows how to scale LLMs to millions of documents — without breaking the bank. LINKS Shreya's website (https://www.sh-reya.com/) DocETL, A system for LLM-powered data processing (https://www.docetl.org/) Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) Watch the podcast video on YouTube (https://youtu.be/3r_Hsjy85nk) Shreya's AI evals course, which she teaches with Hamel "Evals" Husain (https://maven.com/parlance-labs/evals?promoCode=GOHUGORGOHOME)

This week on The Audit Podcast, Rafael Kon, Chief Auditor at Mitsubishi Power, talks about how internal audit can stay connected to a company's strategy while keeping controls strong. He shares how he uses automation and data to improve processes, why shorter and more practical audit reports can be more effective, and how his thinking on audit co-sourcing has changed over time. Rafael also gives his take on Vision 2035 from the IIA and how he measures success in internal audit. Be sure to connect with Rafael on LinkedIn.   Also, be sure to follow us on our social media accounts on LinkedIn, Instagram, and TikTok.   Also be sure to sign up for The Audit Podcast newsletter and to check the full video interview on The Audit Podcast YouTube channel.   Timecodes:   1:40 – What's in Rafael's ChatGPT History 4:51 – J-SOX 9:00 – Connecting Internal Audit to Strategic Goals 17:14 – Bringing in a Data Scientist 22:33 – Building Data Literacy Within the Team 31:37 – Vision 2035 36:25 – Plan vs. Reality 41:45 – Final Thoughts   *   This podcast is brought to you by Greenskies Analytics, the services firm that helps auditors leap-frog up the analytics maturity model. Their approach for launching audit analytics programs with a series of proven quick-win analytics will guarantee the results worthy of the analytics hype.  Whether your audit team needs a data strategy, methodology, governance, literacy, or anything else related to audit and analytics, schedule time with Greenskies Analytics.

One organization turns to a game to get employees to debate and decide together what health care they most value. Guests:Paul Fronstin, Ph.D., Director, Health Benefits Research, Employee Benefits Research Institute Jeanette Janota, Senior Research Associate, American Speech-Language-Hearing AssociationTavril Saint Jean, Senior Research Associate, American Speech-Language-Hearing AssociationJanet McNichol, Chief Human Resources Officer, American Speech-Language-Hearing AssociationEvan Reid, Senior Director of Analytics, American Speech-Language-Hearing AssociationJulia Reilly-Edwards, Data Scientist, American Speech-Language-Hearing AssociationLearn more and read a full transcript on our website.Want more Tradeoffs? Sign up for our free weekly newsletter featuring the latest health policy research and news.Support this type of journalism today, with a gift. Hosted on Acast. See acast.com/privacy for more information.

In this week's MBA Admissions podcast we began by discussing a couple of webinars that are in the works. Graham and Alex will host an AMA-style webinar, as the new admissions season gets underway on August 26. More details to follow, but it will be livestreamed on YouTube! Graham also highlighted the September series of admissions events, where Clear Admit will host the majority of the top MBA programs to discuss Round 2 application strategy. Sign ups for this series are here: https://bit.ly/cainsidemba Graham then noted a few new publications on the Clear Admit site. We have a post that covers all the top MBA programs' in-person admissions event activities for the month of August. We also cover all the early and Round 1 application deadlines for the top MBA programs in a useful guide, and have a timely admissions tip on how to best prepare recommenders. We continue our series of Adcom Q&As; this week we hear from CMU / Tepper. For this week, for the candidate profile review portion of the show, Alex selected three ApplyWire entries: This week's first MBA admissions candidate has a career in undergraduate admissions, and a side-career in comedy. They want to use the MBA to pivot into the entertainment industry. This week's second MBA candidate is a data scientist who also plays a rock guitar. They have a 695 GMAT. The final MBA candidate is a reapplicant. They have a low GPA of 2.75 but have now completed MBA Math. They do have a super GRE score of 331. This episode was recorded in Philadelphia, USA and Cornwall, England. It was produced and engineered by the fabulous Dennis Crowley in Philadelphia, USA. Thanks to all of you who've been joining us and please remember to rate and review this show wherever you listen!

Go to www.LearningLeader.com for full show notes This is brought to you by Insight Global. If you need to hire 1 person, hire a team of people, or transform your business through Talent or Technical Services, Insight Global's team of 30,000 people around the world have the hustle and grit to deliver. www.InsightGlobal.com/LearningLeader Guest: Nick Maggiulli is the Chief Operating Officer and Data Scientist at Ritholtz Wealth Management. He is the best-selling author of Just Keep Buying: Proven Ways to Save Money and Build Your Wealth, and his latest book is called The Wealth Ladder. Nick is also the author of OfDollarsAndData.com, a blog focused on the intersection of data and personal finance. Notes: Money works as an enhancer, not a solution: Like salt enhances food flavors, money amplifies existing life experiences but has little value by itself without relationships, health, and purpose. "Money by itself is useless... without friends, family, without your health, it doesn't add much... it enhances all the other parts of life." Nick beat his dad's friends at chess when he was 5 years old because he practiced more than they did. He got more reps. He did the work. It's not that he was a chess prodigy. He just worked harder than his opponents did. And he still does that today. Practice creates expertise beyond intelligence: At five years old, Maggiulli could beat adults at chess not because he was smarter, but because he had more practice. Consistent effort over time can outcompete raw talent. "I could beat them, not because I was smarter than them, only because I had practiced something... In this very specific realm, I could beat them." Consistent writing builds compound advantages: Writing 10 hours every weekend for nine years created opportunities including book deals and career advancement. The discipline of regular practice compounds over time. "I've been writing for nine years... I spend 10 hours a week every single week for almost a decade now, and that helps over time." The most expensive thing people own is their ego. How do you add value when you're in a job that doesn't have a clear scoreboard (like sales)? Think... What gets accomplished that otherwise wouldn't have without you? Add value through time savings and efficiency: In roles where impact isn't immediately measurable, focus on how much time and effort you save others. Create systems that make your colleagues more efficient. "How do I save our operations team time? How do I save our compliance team time... I'm designing better oars that'll give us 10% more efficiency." Money amplifies existing happiness: Research shows that if you're already happy, more money will make you happier. But if you're unhappy and not poor, more money won't solve your problems. "If you're happy already, more money will make you happier... but if you aren't poor and you aren't happy, more money's not gonna do a thing." Ego is the most expensive thing people own: Trying to appear wealthier than you are prevents actual wealth building. Focus on substance over status symbols. "People in level three that wanna look like people in level four end up spending so much money to keep up with the Joneses." Follow your interests for long-term success: Passion sustains you through inevitable obstacles and rejection. Maggiulli wrote for three years without earning money because he genuinely enjoyed it. "Follow your interest because when you follow your interest, you're more likely to keep going when you face obstacles." The "Die with Zero" philosophy, advocated by Bill Perkins, encourages people to prioritize experiences and fulfillment over accumulating maximum wealth, suggesting spending money strategically to maximize lifetime enjoyment. Nick defines six levels of wealth based on net worth, ranging from $0 to over $100 million. These levels are: Level 1: $0-$10,000 (paycheck-to-paycheck), Level 2: $10,000-$100,000 (grocery freedom), Level 3: $100,000-$1 million (restaurant freedom), Level 4: $1 million-$10 million (travel freedom), Level 5: $10 million-$100 million (house freedom), and Level 6: $100 million+ (philanthropic freedom).  Nick also notes a shift in asset allocation as one progresses through the levels. In the lower levels, a larger portion of wealth is tied up in non-income-producing assets like cars, while higher levels see a greater emphasis on income-producing assets like stocks and real estate. Wealth strategies must evolve by level: The approach that gets you to level four ($1M-$10M) won't get you to level five ($10M-$100M). Higher wealth levels typically require entrepreneurship or equity ownership. "The strategy that you use to get into level four is not going to be the strategy that gets you out." Know when "enough" is enough: Level four wealth ($1M-$10M) may be sufficient for most people. The sacrifices required to reach higher levels often aren't worth the marginal benefits. "The rational response for an American household once they get into level four is... maybe I take my foot off the gas and just enjoy life more." As a data scientist, Nick leverages data to provide business intelligence insights at Ritholtz Wealth Management, where he also serves as Chief Operating Officer. His work involves analyzing data to answer business questions, identify trends, and build predictive models. For example, he might analyze lead conversion rates, client attrition, or investment patterns to inform business decisions. Financial independence requires separate identities: Maintain individual financial accounts within marriage for independence and easier asset division. Pool resources for shared expenses while preserving autonomy. "Everyone needs to have their own accounts. They need to have their own money... especially important for women." Nick and his wife have a joint + separate bank account(s). Here's how it works: All of your income and your partner's income flows into this joint account. That income is used to pay for all shared expenses. Any excess left in the account (above a certain threshold) can either be left in the account or distributed equally between you and your partner (to your separate accounts). Apply to be part of my Learning Leader Circle  

Today on the podcast we welcome back Nick Maggiulli. He's the author of a new book called The Wealth Ladder: Proven Strategies for Every Step of Your Financial Life. His first book was called Just Keep Buying. In addition, Nick writes a wonderful blog called Of Dollars and Data, which is focused on the intersection between data and personal finance. In his day job, Nick is the Chief Operating Officer and Data Scientist at Ritholtz Wealth Management. He received his bachelor's degree in economics from Stanford University. Nick, welcome back to The Long View.BackgroundBioOf Dollars and DataThe Wealth Ladder: Proven Strategies for Every Step of Your Financial LifeJust Keep Buying: Proven Ways to Save Money and Build Your WealthTopics Discussed“How to Make More Without Working More,” by Nick Maggiulli, ofdollarsanddata.com, July 7, 2025.“How Much House Is Too Much?” by Nick Maggiulli, ofdollarsanddata.com, Oct. 22, 2024.“Rich vs Wealthy: Summarizing the Differences,” by Nick Maggiulli, ofdollarsanddata.com, April 18, 2023.“What Is Liquid Net Worth? [And Why It's So Important],” by Nick Maggiulli, ofdollarsanddata.com, Dec. 5, 2023.“Do You Need Alternatives to Get Rich?” by Nick Maggiulli, ofdollarsanddata.com, May 28, 2024.“Concentration Is Not Your Friend,” by Nick Maggiulli, ofdollarsanddata.com, March 14, 2023.Other“Nick Maggiulli: ‘The Biggest Lie in Personal Finance,'” The Long View, Morningstar.com, April 12, 2022.Federal Reserve Survey of Consumer Finances“High Income Improves Evaluation of Life But Not Emotional Well-Being,” by Daniel Kahneman and Angus Deaton, Princeton.edu, Aug. 4, 2010.“Experienced Well-Being Rises With Income, Even Above $75,000 Per Year,” by Matthew Killingsworth, pnas.org, Nov. 14, 2020.“Income and Emotional Well-Being: A Conflict Resolved,” by Matthew Killingsworth, Daniel Kahneman, and Barbara Mellers, pnas.org, Nov. 29, 2022.Of Dollars and Data Popular Posts“Even God Couldn't Beat Dollar-Cost Averaging,” by Nick Maggiulli, ofdollarsanddata.com, Feb. 5, 2019.Get Good With Money, by Tiffany AlicheThe Millionaire Fastlane, by MJ DeMarcoThe Intelligent Asset Allocator, by William BernsteinHow to Retire, by Christine Benz

A view from the intersection of AI and creators. Rich Lumelleau and Data Scientist Hilary Mason discuss: - How her company Hidden Door uses generative AI to turn any work of fiction into an online social roleplaying game. - Whether Napster is a fair comparison. - What the future of storytelling could look like. Host: Rich Lumelleau Guests: Hilary Mason Engineer: Dan Boyd Advertisements are sponsored content and provided for informational purposes only. The Motley Fool and its affiliates (collectively, "TMF") do not endorse, recommend, or verify the accuracy or completeness of the statements made within advertisements. TMF is not involved in the offer, sale, or solicitation of any securities advertised herein and makes no representations regarding the suitability, or risks associated with any investment opportunity presented. Investors should conduct their own due diligence and consult with legal, tax, and financial advisors before making any investment decisions. TMF assumes no responsibility for any losses or damages arising from this advertisement. Learn more about your ad choices. Visit megaphone.fm/adchoices