DataFramed

DataCamp

Welcome to DataFramed, a weekly podcast exploring how artificial intelligence and data are changing the world around us. On this show, we invite data & AI leaders at the forefront of the data revolution to share their insights and experiences into how they lead the charge in this era of AI. Whether you're a beginner looking to gain insights into a career in data & AI, a practitioner needing to stay up-to-date on the latest tools and trends, or a leader looking to transform how your organization uses data & AI, there's something here for everyone.

Join co-hosts Adel Nehme and Richie Cotton as they delve into the stories and ideas that are shaping the future of data. Subscribe to the show and tune in to the latest episode on the feed below.

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21 episodes
Updated Yesterday

Episodes21

Newest first

Yesterday · 39 min
#371 The Real Reason Your Product Team Needs a Feedback Loop with Todd Olson, CEO at Pendo
Software teams are shipping faster than ever, but speed hasn't solved the oldest problem in the industry: most software still isn't very good. AI coding tools have lowered the barrier to building something, yet they haven't lowered the barrier to building something worth using. As more people who aren't trained software creators start shipping products, a new question is forming across product, design, and engineering teams: if AI can build almost anything, how do you make sure it builds the right thing, and builds it well? Todd Olson is co-founder and CEO of Pendo, the product experience platform he started in 2013. Before that, he held product and engineering roles at Rally Software, Red Hat, Cisco, and Google. He's the author of The Product-Led Organization and has led Pendo through raising over $356M in venture funding while growing to 2,300+ customers. In the episode, Richie and Todd explore why bad software still gets built, how much context AI coding agents need before they can be trusted, using behavioral data and "rage prompts" to catch what's actually frustrating users, the shift toward headless and agentic software design, how product, design, and engineering roles are splitting apart, managing one-way-door risk during AI transformation, and much more. Links Mentioned in the Show: Jeff Bezos’s one-way door / two-way door decision framework: 2015 Amazon shareholder letter HubSpot’s 2024 terms-of-service backlash Ramp Stripe Fin (Intercom’s AI agent), recently announced to be acquired by Salesforce Anthropic / Claude Code Connect with Todd AI-Native Course: Intro to AI for Work Related Episode: The Data Team’s Agentic Future with Ketan Karkhanis, CEO at ThoughtSpot New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business
July 27 · 44 min
#370 Failure is Data (and Other Career Advice) | Todd Dewett, Leadership Author & Speaker
As AI takes over more technical and routine work, the skills that set data and AI professionals apart are shifting. Raw technical ability and a high IQ still matter, but they are becoming table stakes as tools get more capable and teams get smarter. What increasingly separates people is harder to automate: communication, self-awareness, authenticity, and the ability to keep learning through failure. For anyone building a career in this space, that raises real questions. Which skills are actually worth investing in now? What holds up as AI advances? And how do you keep growing once you have already had some success? Dr. Todd Dewett is one of the world's most-watched leadership voices — an authenticity expert, bestselling author, and top LinkedIn Learning instructor whose courses have reached more than 25 million people across 100+ countries. After beginning his career at Andersen Consulting and Ernst & Young, he earned a PhD in organizational behavior at Texas A&M and spent a decade as an award-winning professor before going solo. He is a five-time TEDx speaker and the author of Show Your Ink. In the episode, Richie and Todd explore why fear quietly limits careers, treating failure as data rather than a verdict, the people skills that outlast raw IQ, learnable self-awareness, authenticity at work, using AI without losing your voice, getting better at speaking and writing, building habits, escaping the success trap, and much more. Links Mentioned in the Show: • Todd's LinkedIn newsletter (writing + his "Creswall" comic) — https://www.linkedin.com/in/drdewett/ • Todd Dewett on LinkedIn Learning — https://www.linkedin.com/learning/instructors/todd-dewett • Free LinkedIn Learning access via your public library — https://www.linkedin.com/learning • Gemma Leigh Roberts, chartered psychologist — https://www.linkedin.com/in/gemmaleighroberts/ • Erin Shrimpton, chartered organisational psychologist — https://ie.linkedin.com/in/erinshrimpton • Connect with Todd: https://www.linkedin.com/in/drdewett/ • AI-Native Course: Intro to AI for Work • Related Episode: How to Have a Machine Learning Career in 2026 with Marina Wyss New to DataCamp? Learn on the go using the DataCamp mobile app
July 20 · 49 min
#369 How to Become a Top Business Intelligence Analyst | Helen Wall, Founder at Helen Data Design & Microsoft Influencer
Business intelligence has never been only about building charts and writing queries. Most of the work that makes a report trustworthy happens below the surface — in the data models, documentation, and stakeholder conversations that users never see. For analysts, technical skill is just the starting point; understanding what the business actually needs, and why a number exists, matters just as much. So what really separates a competent analyst from a great one? How do you build something that answers the right question, not just any question? And which skills are worth investing in first? Helen Wall is the founder of Helen Data Design and a Microsoft-recognized business intelligence expert and LinkedIn Learning instructor. A former actuary, she has worked across financial reporting, weather data, and consulting projects, and has maintained a running list of monthly Power BI updates for close to five years. She studied math and economics at the University of Washington, and focuses on where data analytics meets design. In the episode, Richie and Helen explore what separates a great business intelligence analyst from an average one, the iceberg model of analytics work, building and using semantic layers, taking over messy legacy projects, documenting for both humans and AI agents, how Power BI has changed over five years, keeping AI outputs consistent and cost-effective, accountability in the age of agents, and much more. Links Mentioned in the Show: • Connect with Helen • Microsoft AI for Good Lab • Power BI monthly feature updates • SQL Server Analysis Services • Power BI Q&A visual • DAX (Data Analysis Expressions) • AI-Native Course: Intro to AI for Work • Related Episode: The Data Team's Agentic Future with Ketan Karkhanis, CEO at ThoughtSpot New to DataCamp? • Learn on the go using the DataCamp mobile app • Empower your business with world-class data and AI skills with DataCamp for business
July 13 · 50 min
#368 AI Agents Are Now Your Database's Main User | Reynold Xin, Co-Founder at Databricks
For forty years, the rule held that transactional and analytical databases had to be separate systems, connected by fragile pipelines that move data from one to the other. That assumption is now being questioned. As AI agents start generating the majority of database activity, the old architecture is being redesigned around speed, scale, and a single copy of governed data. For anyone who works with data day to day, this raises practical questions. Do you still need separate systems for live and historical data? What happens to the pipelines you maintain? And how does your stack change when agents, not people, write most of the queries? Reynold Xin is co-founder and Chief Architect of Databricks. He is one of the original creators of Apache Spark, where he led the design of GraphX, Project Tungsten, and Structured Streaming, co-designed DataFrames, and served as release manager for Spark 2.0. He holds a PhD in Computer Science from UC Berkeley's AMPLab and a degree in Engineering Science from the University of Toronto. In the episode, Richie and Reynold explore self-service analytics with Genie, the ontology layer that grounds AI in enterprise data, handling hallucinations, governance and permissions for AI agents, merging transactional and analytical databases with Lakebase and LTAP, real-time analytics, controlling cost through autoscaling, the future of Spark and classic machine learning, and much more. Links Mentioned in the Show: • Connect with Reynold: https://www.linkedin.com/in/rxin • Genie (Databricks data agent): https://www.databricks.com/product/genie • Genie Ontology / Genie One: https://www.databricks.com/blog/introducing-genie-one-genie-ontology-and-genie-agents • LTAP (Lake Transactional/Analytical Processing): https://www.databricks.com/company/newsroom/press-releases/databricks-launches-ltap-first-lake-transactionalanalytical • Lakehouse//RT: https://www.databricks.com/blog/introducing-lakehousert-real-time-performance-unified-lakehouse • Lakebase: https://www.databricks.com/product/lakebase • Apache Spark: https://spark.apache.org • AI-Native Course: Intro to AI for Work - https://www.datacamp.com/courses/introduction-to-ai-for-work • Related Episode: AI's Impact on Databases - https://www.datacamp.com/podcast/ais-impact-on-databases New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business
July 6 · 51 min
#367 Don't Build on Jell-O: How to Make Agentic AI Reliable with Dan Klein, CTO at Scaled Cognition
Across the AI industry, capability has exploded while trustworthiness has lagged badly behind. The same technology that writes fluent prose can invent a refund policy that was never real, and most of those errors are subtle enough that no one notices. As more teams hand high-stakes work to AI — in banking, healthcare, customer service — the cost of confident mistakes adds up fast. So how common are hallucinations, really? Can chaining models together or adding humans to the loop fix it? And is reliability something you can design into a system from the start? Dan Klein is the CTO and co-founder of Scaled Cognition and a professor of computer science at UC Berkeley, where he leads the Berkeley NLP Group within the Berkeley AI Research (BAIR) Lab. He previously co-founded Semantic Machines, a conversational AI company acquired by Microsoft in 2018. At Scaled Cognition he built APT (Agentic Pretrained Transformer), a frontier model designed from the ground up for reliable, policy-adherent agentic AI. In the episode, Richie and Dan explore why AI reliability has lagged behind capability, how hallucinations hide in plain sight, the limits of humans-in-the-loop and LLM-as-judge, building reliability into model architecture, agentic systems and verifiable actions, test-driven agent development, the skills that stay valuable, digital literacy, and much more. Links Mentioned in the Show: • Connect with Dan: https://www.linkedin.com/in/dan-klein/ • Scaled Cognition: https://www.scaledcognition.com/ • Berkeley NLP Group: https://nlp.cs.berkeley.edu/ • Code smells (Martin Fowler): https://martinfowler.com/bliki/CodeSmell.html • Refactoring, by Martin Fowler: https://martinfowler.com/books/refactoring.html • "Now you have two problems" (Jamie Zawinski quote): https://regex.info/blog/2006-09-15/247 • Lean theorem prover: https://lean-lang.org/ • AI-Native Course: Intro to AI for Work - https://www.datacamp.com/courses/introduction-to-ai-for-work • Related Episode: How to Build AI Your Users Can Trust with David Colwell, VP of AI & ML at Tricentis - https://www.datacamp.com/podcast/how-to-build-ai-your-users-can-trust New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business
June 29 · 48 min
#366 Can AI Agents Outperform a Data Scientist? | James Zou, Professor at Stanford University
AI agents are no longer limited to automating routine tasks like customer support or report generation. Research labs and pharmaceutical companies are beginning to deploy teams of specialist AI agents capable of designing experiments, analyzing data, and proposing new hypotheses — in some cases producing results that outperform human experts. For data scientists and researchers, this raises urgent questions: Where do AI agents excel in scientific workflows today, and where do they fall short? How do you build an agent that can genuinely innovate rather than just replicate what's already been done? And what does it take to scale a single model into a fully functioning virtual research team? James Zou is an Associate Professor of Biomedical Data Science, and by courtesy of Computer Science and Electrical Engineering, at Stanford University. He leads the Stanford AI for Science Lab and is affiliated with Together AI. His research focuses on building AI agents for scientific discovery and data science, making AI more reliable and statistically rigorous. He has received a Sloan Fellowship, NSF CAREER Award, two Chan-Zuckerberg Investigator Awards, and faculty awards from Google, Amazon, and Adobe. In the episode, Richie and James explore how AI scientist agents are already outperforming human experts in scientific discovery, the Virtual Lab framework for building teams of specialist AI agents that conduct real research, teaching models to innovate not just imitate through a new training paradigm called "learning to discover," DS Gym for self-improving data science agents, scaling agentic systems from a single model to a Virtual Biotech with tens of thousands of agents, Einstein Arena as the first competition platform built exclusively for AI agents, converting scientific papers into agent-native MCPs through Paper to Agent, and much more. Links Mentioned in the Show: • Virtual Lab (Nature paper) • Einstein Arena • DS Gym • Paper2Agent • Together AI • AlphaFold 2 / Nobel Prize 2024 • Connect with James • AI-Native Course: Intro to AI for Work • Related Episode: #358 How AI Agents Will Work While You Sleep | Ruslan Salakhutdinov New to DataCamp? • Learn on the go using the DataCamp mobile app • Empower your business with world-class data and AI skills with DataCamp for business
June 22 · 53 min
#365 Your 90 Day Blueprint for AI Success with Charlene Li, Author of Winning with AI
Most organizations know AI matters, but few have turned that conviction into a written plan. Ambition and hope are everywhere; a clear roadmap tied to business strategy is rare. For teams on the ground, this gap shows up as scattered initiatives, tools nobody fully uses, and a lot of activity that never adds up to real value. So where do you actually start? How do you move from a long list of use cases to a focused plan you can execute? And who in the organization should own the job of turning AI into business results? Charlene Li is a New York Times bestselling author and strategic advisor who has spent more than two decades helping leaders navigate disruptive change. She founded Altimeter Group, has advised 49 of the Fortune 100, and is the co-author of Winning with AI: The 90-Day Blueprint for Success (with Dr. Katia Walsh). In the episode, Richie and Charlene explore how to get your organization AI-ready in 90 days, why you don't need a separate AI strategy, appointing an AI value owner, creating value beyond efficiency, building AI fluency, Goldilocks governance, why you should kill your AI pilots, and much more. Links Mentioned in the Show: Winning with AI: The 90-Day Blueprint for Success Dr. Katia Walsh (co-author) Moderna Konecta IKEA Andrej Karpathy's LLM Wiki Connect with Charlene: LinkedIn AI-Native Course: Intro to AI for Work Related Episode: Our Data Trends & Predictions for 2026 with Jonathan Cornelissen & Martijn Theuwissen New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business
June 15 · 43 min
#364 How to Enable Agentic Commerce with Nell Thomas, VP of Data at Shopify
AI agents are starting to handle parts of the shopping journey that used to require human judgment — discovery, comparison, checkout. But behind every agent recommendation is a massive, invisible layer of data infrastructure. Product catalogs need to be structured, inventory synced in real time, pricing accurate, and quality signals clear. For data engineers and teams building at companies like Shopify, this shift means rethinking how data flows through systems and what "good enough" quality actually means. How do you ensure data is ready for AI? And how is this reshaping what data teams actually do? Nell Thomas is the VP of Data at Shopify, where she leads a team of approximately 400–500 people across data infrastructure, ML platforms, data engineering, and data science. Her career spans multiple industries including social media (Facebook), e-commerce (Etsy), politics (Hillary for America, Democratic National Committee), and now commerce. She holds an A.B. in Psychology from Harvard University and an M.A. in History & Sociology of Science from the University of Pennsylvania. In the episode, Richie and Nell explore agentic commerce and how AI agents are transforming shopping, the role of data in enabling AI-driven commerce, Shopify's Catalog and Universal Commerce Protocol, data quality requirements for agentic systems, how the data team function is evolving at Shopify, changing skill requirements for data professionals, and Nell's unconventional career path from politics to tech. Links Mentioned in the Show: - Agentic Commerce on Shopify - Universal Commerce Protocol (UCP) vs Agentic Commerce Protocol (ACP) - Shopify Catalog Documentation - Agentic Storefronts — Shopify Sales Channel - ChatGPT — OpenAI's Conversational AI - How Shopify Built Data Infrastructure at Scale Related Scaling Data Quality in the Age of Generative AI New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business
June 8 · 53 min
#363 Build Your Personal Brand at Work | Dorie Clark, Executive Education Faculty at Columbia Business School
Technical skills are being commoditized faster than ever. As AI takes on more of the work that used to define a junior knowledge worker, the things that once made someone valuable are becoming table stakes. What compounds in this environment is reputation — what colleagues, clients, and decision-makers think about you when your name comes up. That puts new pressure on visibility. People doing great work in silence are increasingly the ones getting passed over for promotions and external opportunities. So how do you build a reputation without becoming an influencer? What does AI-era credibility actually look like? And how do you start small? Dorie Clark teaches Executive Education at Columbia Business School and is the Wall Street Journal and USA Today bestselling author of The Long Game, Entrepreneurial You, Reinventing You, and Stand Out. She has been named four times as one of the Top 50 business thinkers in the world by Thinkers50, recognized as the #1 Communication Coach in the world by the Marshall Goldsmith Leading Global Coaches Awards, and is a frequent contributor to the Harvard Business Review. In the episode, Richie and Dorie explore why AI fluency is the new Excel skill, tinkering with AI's jagged frontier, the security risks of agentic AI, what personal branding really means in an AI-disrupted job market, the recognized expert formula, the ladder strategy for credibility, networking with "no asks for a year," running better meetings, and much more. Links Mentioned in the Show: • The Jagged Frontier (HBS Working Paper) • Agentic Misalignment: How LLMs could be insider threats (Anthropic) • AI-powered coding tool wiped out a software company's database (Fortune) • Reinventing You by Dorie Clark • The Long Game by Dorie Clark • Superteams by Ron Friedman • Connect with Dorie on LinkedIn • AI-Native Course: Intro to AI for Work • Related Episode: #341 Our Data Trends & Predictions for 2026 New to DataCamp? Learn on the go using the DataCamp mobile app. Empower your business with world-class data and AI skills with DataCamp for business.
June 1 · 47 min
#362 How to Have a Machine Learning Career in 2026 | Marina Wyss, Senior Applied Scientist at Twitch
The role of the machine learning engineer is being rewritten in real time. AI coding assistants are absorbing parts of the day-to-day, planning and evaluation are eating up more of the week, and the lines between machine learning engineer, AI engineer, and data scientist are blurrier than ever. For anyone working in data and AI — or trying to break in — this shift changes what skills are worth investing in, what employers actually screen for, and how interviews are run. What's still worth learning? What does a competitive portfolio look like? And how do you stand out when a thousand applicants are using bots to apply? Marina Wyss is a Senior Applied Scientist at Twitch (an Amazon company), where she builds production AI and machine learning systems across content understanding, recommendations, and forecasting. She came into the field from a non-traditional background — a political science undergrad and a Master's in social data science in Berlin — and has held machine learning roles at Coursera and a Berlin-based statistical consultancy along the way. Outside her day job, Marina runs a popular AI/ML YouTube channel and weekly newsletter, and coaches people transitioning into machine learning from non-traditional careers. In this episode, Richie and Marina explore how AI is reshaping the machine learning engineer role, the shifting balance between coding and planning, why evaluation matters more than ever, the differences between ML engineer, AI engineer, and data scientist roles, how to break into the field from a non-technical background, what makes a strong portfolio project, the hiring process at big tech, how to prepare for technical interviews, networking strategies that actually work, what success looks like in your first few months on the job, and much more. Links Mentioned in the Show • Chip Huyen — AI Engineering (book) • Andrew Codesmith on YouTube • Phillip Choi on YouTube • A Life Engineered on YouTube • Keras • LeetCode • Connect with Marina: LinkedIn • AI-Native Course: Intro to AI for Work • Related Episode: How to Have a Career in Data Science in 2025 with Dawn Choo New to DataCamp? Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business
May 25 · 48 min
#361 If You Want AI to Work, Fix This Boring Thing First with Veronika Durgin, VP of Data at Saks
Every conversation about AI in data eventually arrives at the same question: which roles survive, and which ones get automated away? Generative AI can already draft SQL, build dashboards, and run exploratory analysis — but it still can't sit with a business stakeholder and untangle what "customer" actually means across five teams. For data professionals, that shifts the day-to-day from production work toward translation, modeling, and judgment. So which skills are worth doubling down on? Which roles are becoming central, and which are quietly disappearing? And what should anyone hiring — or being hired — be paying attention to right now? Veronika Durgin is the VP of Data at Saks Global, where she leads data strategy across the luxury retail group. A full-stack data executive with more than two decades of experience spanning database administration, data engineering, platform architecture, data modeling, and analytics, Veronika is a Snowflake Data Superhero and a member of CDO Magazine's Global Editorial Board. She writes about data modeling, data culture, and data leadership on her Substack and Medium. In the episode, Richie and Veronika explore the future of data careers under AI, why analytics engineering becomes the catch-all role, the skills and hiring shifts data leaders are making, centralized data with decentralized analytics, keeping enterprise data teams agile, conceptual data modeling as the unglamorous prerequisite to AI, semantic layers, agentic commerce, and much more. Links Mentioned in the Show: Connect with Veronika: LinkedIn Veronika's Substack: Think. Solve. Repeat. dbt — referenced as the origin of "analytics engineering" Open Data Science Conference (ODSC) — Veronika's recent talk on data and company politics Amazon "two-way door" decisions — Bezos shareholder letter Jessica Talisman — Veronika's recommendation for knowledge graphs and ontologies Juan Sequeda — referenced on semantic layers and knowledge graphs Catalog & Cocktails podcast (hosted by Juan Sequeda) AI-Native Course: Intro to AI for Work Related Episode: Creating an AI-First Data Team with Bilal Zia, Head of Data Science & Analytics at Duolingo New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business
May 18 · 57 min
#360 What's Your Biggest AI Ethical Nightmare? | Reid Blackman, CEO at Virtue Consultants
Most AI ethics conversations sound the same: be fair, be transparent, be accountable. The values are right, but in practice they don't get teams out of bed in the morning. Executives nod along, employees take the compliance training, and meanwhile real risks like hallucinations, cascading failures, and autonomous agents acting at scale slip through. So what shifts when teams stop chasing an ethical ideal and start naming the specific disasters they want to avoid? Who needs to be in the room to spot them? And what kind of training actually changes how people use AI day to day? Reid Blackman is the founder and CEO of Virtue, an AI ethical risk consultancy, and the author of The Ethical Nightmare Challenge: How to Avoid the Worst of AI (2026) and Ethical Machines (HBR Press, 2022). A former philosophy professor at Colgate with a PhD from the University of Texas at Austin, he has designed responsible AI programs for organizations including Amazon, Etsy, Kraft Heinz, Merck, US Bank, and Nationwide, and has advised the FBI, NASA, the World Economic Forum, and the Canadian government on federal AI regulations. He also hosts the Ethical Machines podcast. In the episode, Richie and Reid explore why responsible AI fails to motivate organizations, the biggest AI ethical nightmares facing companies today, the unique risks of agentic AI including cascading failures and emergent risks, the Ethical Nightmare Challenge framework, cross-functional ENC teams, training employees in plain language, scaling AI governance, measuring success by what you avoid, and much more. Links Mentioned in the Show: • The Ethical Nightmare Challenge by Reid Blackman • Ethical Machines by Reid Blackman • Ethical Machines podcast • Claude Code • Connect with Reid: LinkedIn • AI-Native Course: Intro to AI for Work • Related Episode: #350 How to Make Hard Choices in AI with Atay Kozlovski New to DataCamp? Learn on the go using the DataCamp mobile app. Empower your business with world-class data and AI skills with DataCamp for business.
May 12 · 43 min
#359 My Best Friend is AI with Valerie Tiberius, Professor of Philosophy at University of Minnesota
Valerie Tiberius is the Paul W. Frenzel Chair in Liberal Arts and Professor of Philosophy at the University of Minnesota. She is an expert in ethics, moral psychology, and well-being, and the author of five books including What Do You Want Out of Life? and the forthcoming Artificially Yours: Real Friendship in a World of Chatbots (Princeton University Press, May 2026). She previously served as President of the Central Division of the American Philosophical Association. In the episode, Richie and Valerie explore the purpose of friendship and whether AI can replicate it, the benefits and risks of chatbot companions for loneliness, how sycophantic AI responses distort advice and self-perception, the dangers of companion chatbots for children's social development, designing ethical AI companions that promote human flourishing, the zone of proximal development as a framework for better AI tools, and much more. Links Mentioned in the Show: Artificial Intimacy by Sherry Turkle Being You: A New Science of Consciousness by Anil Seth Liberation Day: Stories by George Saunders Hard Fork podcast (NYT) Connect with Valerie AI-Native Course: Intro to AI for Work Related Episode: #342 — "The Secrets to High AI Adoption" with Stefano Puntoni, Professor at Wharton New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business
May 4 · 58 min
#358 How AI Agents Will Work While You Sleep | Ruslan Salakhutdinov, Professor at Carnegie Mellon
Almost every AI agent demo lands in roughly the same place: it works most of the time, looks remarkable, and then fails in a way no one anticipated. Self-driving cars hit this wall a decade ago, and agents are running into it now. For data and AI teams, the question is no longer whether agents can complete a task — it's whether they can complete it reliably enough to remove the human reviewer. Which categories of work tolerate a 90% success rate? Which absolutely don't? And where should the next layer of guardrails sit? Ruslan Salakhutdinov is a UPMC Professor of Computer Science at Carnegie Mellon University and one of Geoffrey Hinton's former PhD students. He has previously served as Director of AI Research at Apple and VP of Research in Generative AI at Meta. His research focuses on deep learning, reasoning, and AI agents. In the episode, Richie and Russ explore the most exciting use cases of AI agents today, long horizon tasks, the credit assignment problem, multi-agent systems, designing reliable human-in-the-loop workflows, agent safety and guardrails, embodied and physical AI, lessons from self-driving cars, the difference between academia and industry, and much more. Links Mentioned in the Show: • Claude Code (Anthropic) • Yutori • Waymo • Apple Project Titan • DeepSeek-V3 Technical Report • Kimi K2 Technical Report • Connect with Ruslan: LinkedIn • AI-Native Course: Intro to AI for Work • Related Episode: AI Agents at Work: What Actually Breaks (and How to Fix It) with Danielle Crop New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business
April 27 · 58 min
#357 Data-Driven Workforce Analytics with Ben Zweig, CEO at Revelio Labs
The data field has changed shape faster than almost any other. The role that used to be a statistician became a data scientist, became an ML engineer, and is now morphing into AI engineer. Consulting firms are hiring fewer entry-level analysts and more vibe-coders who can ship AI systems to production. For data and AI professionals, this raises immediate questions. Which parts of the work are most exposed to automation, and which are not? Where should you invest your time? And which backgrounds are now producing the strongest hires, whether you are building a team or trying to join one? Ben Zweig is the CEO and Co-Founder of Revelio Labs, where he leads the development of a universal HR database built on over a billion public employment profiles and more than 5 billion job postings. He holds a PhD in Economics from the CUNY Graduate Center and teaches Data Science and The Future of Work at NYU Stern. Before founding Revelio Labs, he managed Workforce Analytics projects in the IBM Chief Analytics Office and worked as a data scientist at an emerging-markets hedge fund. He is the author of Job Architecture: Building a Workforce Intelligence Taxonomy. In the episode, Richie and Ben explore why hiring is a broken two-sided market, why jobs are bundles of tasks not skills, building universal taxonomies from billions of job postings, which data careers resist AI, advice for hiring data talent, when traditional NLP beats LLMs, and much more. Links Mentioned in the Show: Ben's book — Job Architecture: Building a Workforce Intelligence Taxonomy Revelio Labs O*NET — the US government occupational taxonomy Ben critiques Baruch Lev — The End of Accounting Haskel & Westlake — Capitalism Without Capital Justified Posteriors podcast (Andrey Fradkin & Seth Benzell) Connect with Ben: LinkedIn AI-Native Course: Intro to AI for Work Related Episode: Our Data Trends & Predictions for 2026 with Jonathan Cornelissen & Martijn Theuwissen New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business
April 20 · 53 min
#356 The Forecast for Time Series Forecasts with Rami Krispin, Senior Manager of Data Science at Apple
Time series data is everywhere — from inventory systems and energy grids to financial planning and product demand. As data volumes grow, the old ways of building individual forecasting models simply don't scale. How do you forecast hundreds of thousands of products without spending months on manual modeling? How do you know when to trust automation and when to step in? And what does it actually take to produce forecasts that business stakeholders will act on? Rami Krispin is Senior Director of Data Science and Engineering at Apple Finance, where he leads teams working at the intersection of statistical modeling, machine learning, and production forecasting. He is the author of Hands-On Time Series Analysis with R, an open-source contributor, Docker Captain, and instructor. He holds an MA in Applied Economics and an MS in Actuarial Mathematics from the University of Michigan, where he began his journey learning time series on DataCamp — before going on to build his own course there. In the episode, Richie and Rami explore time series foundation models and the case for scaling, traditional versus modern forecasting approaches, feature engineering in the business world, backtesting and model selection, risk management in automated forecasting, communicating forecast uncertainty to stakeholders, the evolving role of data scientists as architects, and much more. Links Mentioned in the Show: Forecasting: Principles and Practice (Rob Hyndman) Nixtla skforecast Prophet Connect with Rami AI-Native Course: Intro to AI for Work Related Episode: Developing Better Predictive Models with Graph Transformers New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business
April 13 · 52 min
#355 AI's Impact on Databases with Shireesh Thota, CVP of Databases at Microsoft
Cloud data platforms now offer hundreds of services, plus a growing menu of SQL, NoSQL, and open source options. Unified environments promise a simpler path, but the hard trade-offs—consistency versus scale, single-writer versus sharded, RPO/RTO targets—still matter. In daily work, you may be deciding between SQL Server, Postgres, and a globally distributed JSON store, while also asking AI tools to draft queries and spot issues. Should you still learn SQL if an agent can write it? How do you validate the intent, performance, and security of generated queries? And can monitoring agents actually reduce on-call pain without taking away needed control? Shireesh is the CVP of Databases at Microsoft. He leads product management, engineering, and cloud operations for Azure Databases as well as App Development for Microsoft Fabric. The products in his team’s portfolio include Azure SQL Database (on-prem, Hybrid and Cloud), Azure Cosmos DB, Azure PostgreSQL, and Azure MySQL.\\n\\n Previously, as the Senior Vice President at SingleStore, Shireesh was responsible for end-to-end engineering and product vision of the company. Before moving to SingleStore, Shireesh was a founding member of Cosmos DB, where he architected, designed, and directly contributed to multiple key pieces of the services.\\n\\n Shireesh has 20+ years of experience on large scale, big data, scale-out, relational and schema agnostic distributed systems across SQL, Azure Cosmos DB and PostgreSQL/Citus. In the episode, Richie and Shireesh explore how AI agents are reshaping data stacks, why unified platforms like Fabric matter, how semantic models and ontologies reduce confusion in metrics, SQL and NoSQL choices on Azure, Postgres to Cosmos DB with guidance for builders, and much more. Links Mentioned in the Show: Microsoft Fabric Azure Cosmos DB What is Azure SQL Database? Connect with Shireesh AI-Native Course: Intro to AI for Work Related Episode: Six Skills Data Professionals Need To Succeed with Abhijit Bhaduri, Brand Evangelist & Former General Manager of Global L&D at Microsoft Explore AI-Native Learning on DataCamp New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business
April 6 · 46 min
#354 Beyond BI: Decision Intelligence with Graphs with Jamie Hutton, CTO at Quantexa
Decision intelligence is showing up across data and AI teams as companies move beyond dashboards to decisions made with context. Graphs, entity resolution, and better data products are becoming core tools as messy, siloed data meets stricter risk and compliance needs. In day-to-day work, this means linking “James,” “Jim,” and “Jamie” across systems, enriching records with third‑party sources, and pushing models where the data already lives in your lakehouse. How do you trust your customer counts? Which links in a graph matter, and which are noise? Can graph-based context reduce LLM hallucinations enough for regulated decisions with humans still in-loop. Jamie Hutton is the Co-founder and Chief Technology Officer of Quantexa, where he leads the company’s global research and development organization in advancing its market-leading Decision Intelligence Platform. With over two decades of experience pioneering data-driven technologies, Jamie has been at the forefront of innovations that connect and unify data at scale to solve complex real-world challenges. He is the creator of dynamic Entity Resolution, a pioneering capability that has redefined how the world’s leading organizations transform raw data into trusted, decision-ready intelligence. This innovation enables enterprises to prepare their data for AI, uncover new revenue streams, and expose hidden connections in even the most sophisticated criminal networks. By providing the foundation for accurate, explainable, and actionable insights, Jamie’s work has empowered governments, financial institutions, and global enterprises to make faster, smarter, and more confident decisions. Prior to co-founding Quantexa, Jamie held senior technology and analytics leadership roles at SAS and Detica, where he delivered mission-critical solutions for organizations operating in some of the most complex and high-stakes environments in the world. Jamie holds a First-Class master’s degree in computer engineering and is recognized as a leading authority in contextual analytics, data integration, and applied AI for mission-critical decision-making. In the episode, Richie and Jamie explore decision intelligence beyond BI, entity resolution across siloed data, building context graphs for fraud, AML, credit risk, and growth, how graph analytics separates meaningful links from noise, graph-RAG for LLMs to cut hallucinations, human-in-the-loop workflows, and ways to start today, and much more. Links Mentioned in the Show: Quantexa Dun & Bradstreet Data Enrichment Connect with Jamie AI-Native Course: Intro to AI for Work Related Episode: How Optimization Powers Decision Intelligence with Duke Perrucci & Ed Klotz, CEO and Senior Mathematical Optimization Specialist at Gurobi Optimization Explore AI-Native Learning on DataCamp New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business
March 30 · 49 min
#353 The Data Team's Agentic Future with Ketan Karkhanis, CEO at ThoughtSpot
Data and AI platforms are racing toward agentic and even autonomous analytics. But the bottleneck is rarely the model—it’s data readiness: governed metrics, clear metadata, and a semantic layer machines can read. For data engineers and analysts, this shifts work from hand-built SQL and dashboard tweaks to designing meaning and trust. If an agent can draft column descriptions, propose a model for a new business question, and build the first dashboard layout, where do you add the most value? What do you measure to prove ROI in 30 days? How do you prevent “shiny demos” from driving strategy too early. Ketan Karkhanis is the CEO of ThoughtSpot. Prior to joining the company in September 2024, Ketan was the Executive Vice President and General Manager of Sales Cloud at Salesforce. He returned to Salesforce in March 2022 after his time as the COO of Turvo, an emerging supply-chain collaboration platform. Before that, Ketan spent nearly a decade at Salesforce, where he led product areas in Sales, Service Cloud, Lightning Platform, and finally Analytics, wherein as the Senior Vice President & GM of Einstein Analytics, he pioneered incredible innovation, customer success, and business acceleration from launch to over $300M and a 30,000 strong user community. Prior to Salesforce, Ketan was at Cisco Systems where he led various technology initiatives and initiatives spanning Customer Advocacy, Cisco Certifications & eLearning. In the episode, Richie and Ketan explore AI agents for analytics, why “self‑service BI” often fails, using agents to answer questions, build dashboards, and automate data modeling, how analyst and engineer roles shift toward governance and agent design, how transparency, culture, and ROI drive safe adoption, and much more. Links Mentioned in the Show: Thoughtspot Thoughspot’s Spotter Agents Connect with Ketan AI-Native Course: Intro to AI for Work Related Episode: AI Agents at Work: What Actually Breaks (and How to Fix It) with Danielle Crop, EVP Digital Strategy & Alliances at WNS Explore AI-Native Learning on DataCamp New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business
March 23 · 56 min
#352 AI Agents at Work: What Actually Breaks (and How to Fix It) with Danielle Crop, EVP Digital Strategy & Alliances at WNS
AI agents are spreading across the data and AI industry, promising to automate everything from research to outreach. At the same time, teams are learning that these tools can hallucinate, leak data, or act in surprising ways. In day-to-day work, the challenge is deciding which tasks to hand off, what data to share, and how to keep the output trustworthy. Do your agents actually add value, or just add noise? Are they running in a secured, ring-fenced environment? How do you balance playful experimentation with critical checking when an agent confidently gets a key fact wrong? Danielle leads go-to-market strategy at WNS, Capgemini's AI transformation services arm. Previously, Danielle was Chief Data Officer at American Express and Albertsons. She also write The Remix substack on technology trends, and is an Editorial Board Member for CDO Magazine. In the episode, Richie and Danielle explore AI agents at work, experimentation with guardrails, data privacy, access, tone controls, OpenClaw automation wins and failures, token costs, tying AI plans to P&L strategy, shifts in careers and hiring, how data teams handle unstructured data governance, and much more. Links Mentioned in the Show: WNS Connect with Danielle AI-Native Course: Intro to AI for Work Catch Danielle speaking at RADAR—April 1 Related Episode: AI Agents Are the New Shadow IT (And Your Governance Isn’t Ready) with Stijn Christiaens, CEO at Collibra Explore AI-Native Learning on DataCamp New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business