DataFramed

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

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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
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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

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

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