DataFramed
DataFramed is a weekly podcast that explores how artificial intelligence and data are transforming the world. Hosts Adel Nehme and Richie Cotton interview data and AI leaders about their insights and experiences. The show covers topics from career advice to the latest tools and trends, aiming to inform both beginners and experienced practitioners.
Episodes
#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
#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 wit
#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 ar
#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
#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
#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 Sh
#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
#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 inv
#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
#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 ethica
#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 Pre
#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. Whi
#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
#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 ta
#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 t
#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&nbs
#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
#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
#351 Will World Models Bring us AGI? with Eric Xing, President & Professor at MBZUAI
World models are emerging as the next step after large language models, pushing AI from book knowledge toward systems that can simulate the physical and social world. Instead of just generating text or short videos, the goal is steerable simulation with long-horizon consistency and planning. For practitioners, this raises practical choices: what data and representations do you need, and when do yo
#350 How to Make Hard Choices in AI with Atay Kozlovski, Researcher at the University of Zurich
Across the AI industry, high-stakes tools are being deployed in places where errors can harm people: sepsis alerts in hospitals, identity checks, welfare fraud detection, immigration enforcement, and recommendation systems that shape life outcomes. The pattern is familiar: scale and speed go up, while human review becomes rushed, shallow, or punished for disagreeing. In daily work, that can look l
#349 From AI Governance to AI Enablement with Stijn Christiaens, Chief Data Citizen at Collibra
Data governance has been around long enough to develop playbooks, but AI governance is evolving in real time. Industry trends like LLMs, agents, and emerging “swarms” are changing what oversight even means, from data lineage to agent-to-agent provenance.For working teams, the questions are immediate: who leads—legal, security, IT, data, or a new AI role? How do you set standards so engineers aren’
#348 AI Agents in Your Systems: Speed, Security, and New Access Risks with Jeremy Epling, CPO at Vanta
Automation is moving from APIs to full “computer use,” where agents click through screens like a human. That power is transforming evidence collection, access reviews, and repetitive security tasks, but it also raises new risk. In everyday workflows, the safest gains often start with read-only actions, sandboxes, and clear opt-in for anything that writes changes. Do your tools know when an access
#347 Let's Get Physical with AI with Ivan Poupyrev, CEO at Archetype AI
Physical AI is showing up across the industry as sensors, connected devices, and foundation models move from the cloud into the real world. After years of IoT wiring everything to the internet, the big shift is turning raw measurements and video into meaning, not just dashboards. For day-to-day teams, that changes how you monitor equipment, detect failures, and decide what to do next. When thousan
#346 Get Quantum Ready with Yonatan Cohen, CTO at Quantum Machines
Quantum computing is advancing fast, but it comes with a core industry challenge: noise. The big promise—better simulations, faster optimization, and maybe new kinds of AI—depends on quantum error correction and scaling from physical qubits to reliable logical qubits. For working professionals, that translates into system design questions, not just theory. How do you budget for the overhead of err
#345 How to Drive Innovation with Brian Solis, Head of Global Innovation at ServiceNow
AI moves fast, and the news cycle can feel like a fire hose. New tools like agents and digital twins promise to help, but they also add more choices and noise. In day-to-day work, the challenge is less about knowing every breakthrough and more about deciding what matters, then making time to act. How do you cut meetings down, say no without friction, and still ship real work? How do you open your
#344 Governing Pandora's Box: Managing AI Risks with Andrea Bonime-Blanc, CEO at GEC Risk Advisory
AI leaders talk about innovation, but the wider reality is messy: fast change, uneven guardrails, and threats that span cyber, reputation, and customer harm. Industry-wide, organizations are shifting from one-off compliance to lifecycle governance—from inception to decommissioning—supported by boards, CEOs, and frontline teams. For professionals, that shows up as coordination work: shared metrics,
#343 Vibe Coding and the Rise of the Non-Developer Builder with Matt Palmer, Developer Relations at Replit
Data and AI teams are drowning in tools, but the big trend is consolidation and speed. AI-driven building is making dashboards, internal apps, and even data workflows feel more like products than reports. Custom interfaces, interactive presentations, and ad hoc apps are becoming easier to create than traditional BI artifacts.For working professionals, this raises practical questions: should you bu
#342 The Secrets to High AI Adoption with Stefano Puntoni, Professor at Wharton
AI tools are becoming part of daily work for more professionals than ever before, yet adoption rates vary significantly across functions and company sizes. What separates organizations that successfully integrate AI from those that struggle? How do psychological factors like identity and autonomy shape how workers respond to AI implementation? And what role does corporate culture play in determini
#341 Our Data Trends & Predictions of 2026 with DataCamp's CEO & COO, Jonathan Cornelissen & Martijn Theuwissen
2026 is shaping up to be a pivotal year for data, AI, and how we work. From step-change improvements in foundation models to AI-native workflows reshaping careers, commerce, and education, the pace of change shows no signs of slowing. After revisiting and scoring their previous predictions, Richie, Jo, and Martijn turn their focus to what’s coming next in 2026.Building on last year’s discussion, w
#340 Reviewing Our Data Trends & Predictions of 2025 with DataCamp's CEO & COO, Jonathan Cornelissen & Martijn Theuwissen
2025 was another huge year for data and AI. Generative AI continued to reshape how we work and interact with technology, with organizations moving beyond experimentation and pushing AI firmly into production. We saw major progress in foundation models, the rise of long-running AI agents, production-ready generative video, and wider adoption of synthetic data. At the same time, AI literacy, adopti
#339 Modern Analytics with Mike Palmer, CEO at Sigma
Self-service analytics has been a goal for data teams for years, but recent advances in AI are accelerating progress in unexpected ways. The combination of natural language interfaces and spreadsheet-like tools is lowering barriers to data access across organizations. But how do you balance the freedom of self-service with the need for governance and accuracy? What skills do analysts need to work
#338 The New Paradigm for Enterprise AI Governance with Blake Brannon, Chief Innovation Officer at OneTrust
AI governance is becoming critical as organizations deploy more intelligent systems across their operations. With predictions of over a billion AI agents entering the workforce in the coming years, traditional governance approaches simply cannot keep pace. How do you ensure your AI systems are using data responsibly without slowing down innovation? What happens when an AI agent makes decisions tha
#337 DataFramed, Distilled. The Best Moments of 2025 with Richie Cotton
2025 was the year AI stopped being a curiosity and started reshaping real work. From data analysts speeding up entire workflows in minutes, to managers learning how to lead hybrid teams of humans and agents, the pace of change has been relentless. Across DataFramed this year, one theme kept surfacing: AI isn’t replacing data professionals—it’s raising the bar on what good looks like. Skills are sh
#336 From City Sewers to Sovereign AI with Russ Wilcox, CEO at ArtifexAI
The concept of sovereign AI is becoming increasingly critical in our interconnected world. Nations and organizations are grappling with who controls the data, infrastructure, and technology that power artificial intelligence systems. But what does this mean for your work in data science and AI implementation? How do you navigate the complex landscape of data ownership when building AI solutions? A
#335 Rebuilding Trust in the Digital Age with Jimmy Wales, Founder at Wikipedia
The internet has transformed how we access information, but it's also created unprecedented challenges around trust and reliability. How do we build digital spaces where collaboration thrives and quality information prevails? What separates toxic online environments from productive ones? The principles of neutrality, transparency, and assuming good faith have proven essential in creating sustainab
#334 The State of Data & AI with Tom Tunguz, VC at Theory Ventures
The AI landscape is evolving at breakneck speed, with new capabilities emerging quarterly that redefine what's possible. For professionals across industries, this creates a constant need to reassess workflows and skills. How do you stay relevant when the technology keeps leapfrogging itself? What happens to traditional roles when AI can increasingly handle complex tasks that once required speciali
#333 Creating an AI-First Data Team with Bilal Zia, Head of Data Science & Analytics at DuoLingo
Data science leadership is about more than just technical expertise—it’s about building trust, embracing AI, and delivering real business impact. As organizations evolve toward AI-first strategies, data teams have an unprecedented opportunity to lead that transformation. But how do you turn a traditional analytics function into an AI-driven powerhouse that drives decision-making across the busines
#332 How to Build AI Your Users Can Trust with David Colwell, VP of AI & ML at Tricentis
The relationship between data governance and AI quality is more critical than ever. As organizations rush to implement AI solutions, many are discovering that without proper data hygiene and testing protocols, they're building on shaky foundations. How do you ensure your AI systems are making decisions based on accurate, appropriate information? What benchmarking strategies can help you measure re
#331 The Future of Data & AI Education Just Arrived with Jonathan Cornelissen & Yusuf Saber
The future of education is being reshaped by AI-powered personalization. Traditional online learning platforms offer static content that doesn't adapt to individual needs, but new technologies are creating truly interactive experiences that respond to each learner's context, pace, and goals. How can personalized AI tutoring bridge the gap between mass education and the gold standard of one-on-one
#330 Harnessing AI to Help Humanity with Professor Sandy Pentland, HAI Fellow at Stanford, Co-founder of MIT Media Lab
Data storytelling isn't just about presenting numbers—it's about creating shared wisdom that drives better decision-making. In our increasingly polarized world, we often miss that most people actually have reasonable views hidden behind the loudest voices. But how can technology help us cut through the noise and build genuine understanding? What if AI could help us share stories across different c
#329 Building Trust in AI Agents with Shane Murray, Senior Vice President of Digital Platform Analytics at Versant Media
Data quality and AI reliability are two sides of the same coin in today's technology landscape. Organizations rushing to implement AI solutions often discover that their underlying data infrastructure isn't prepared for these new demands. But what specific data quality controls are needed to support successful AI implementations? How do you monitor unstructured data that feeds into your AI systems
#328 The Challenges of Enterprise Agentic AI with Manasi Vartak, Chief AI Architect at Cloudera
The promise of AI in enterprise settings is enormous, but so are the privacy and security challenges. How do you harness AI's capabilities while keeping sensitive data protected within your organization's boundaries? Private AI—using your own models, data, and infrastructure—offers a solution, but implementation isn't straightforward. What governance frameworks need to be in place? How do you eval
#327 Building a Sales and Marketing Capability for Data Applications with Denise Persson, CMO at Snowflake, and Chris Degnan, former CRO at Snowflake
The journey from startup to billion-dollar enterprise requires more than just a great product—it demands strategic alignment between sales and marketing. How do you identify your ideal customer profile when you're just starting out? What data signals help you find the twins of your successful early adopters? With AI now automating everything from competitive analysis to content creation, the tradi
#326 Is the Data Analyst Role Dying Out? with Mo Chen, Data & Analytics Manager at NatWest Group
The role of data analysts is evolving, not disappearing. With generative AI transforming the industry, many wonder if their analytical skills will soon become obsolete. But how is the relationship between human expertise and AI tools really changing? While AI excels at coding, debugging, and automating repetitive tasks, it struggles with understanding complex business problems and domain-specific
#325 Using Data to Master the Cycles of Leadership with Carolyn Dewar, Global Practice Leader at McKinsey
Leadership in data-driven organizations requires a delicate balance of technical expertise and human understanding. As businesses navigate unprecedented uncertainty in global markets, geopolitics, and technological change, the role of data as a source of truth becomes increasingly vital. But how do you create a culture where data informs decisions at every level? What separates leaders who merely
#324 Using Behavioral Science to Hack Your Customers Minds with Richard Shotton, Founder at Astroten
Behavioral science is revolutionizing how businesses connect with customers and influence decisions. By understanding the psychological principles that drive human behavior, companies can create more effective marketing strategies and product experiences. But how can you apply these insights in your data-driven work? What simple changes could dramatically improve how your audience responds to your
#323 The Evolution of Data Literacy & AI Literacy with Jordan Morrow, Godfather of Data Literacy
Data literacy and AI literacy are becoming essential skills in today's digital landscape. As organizations collect more data and deploy AI solutions, the ability to understand, interpret, and make decisions with these tools is increasingly valuable. But how do we develop these skills effectively across an organization? What does successful implementation of data and AI literacy programs look like
#322 How Next-Gen Data Analytics Powers Your AI Strategy with Christina Stathopoulos, Founder at Dare to Data
The relationship between AI assistants and data professionals is evolving rapidly, creating both opportunities and challenges. These tools can supercharge workflows by generating SQL, assisting with exploratory analysis, and connecting directly to databases—but they're far from perfect. How do you maintain the right balance between leveraging AI capabilities and preserving your fundamental skills?
#321 Developing Financial AI Products at Experian with Vijay Mehta, EVP of Global Solutions & Analytics at Experian
Financial institutions are racing to harness the power of AI, but the path to implementation is filled with challenges. From feature engineering to model deployment, the technical complexities of AI adoption in finance require careful navigation of both technological and regulatory landscapes. How do you build AI systems that satisfy strict compliance requirements while still delivering business v
#320 The Next Industrial Revolution is Industrial AI | Barbara Humpton, CEO at Siemens USA and Olympia Brikis, Director of Industrial AI at Siemens USA
The manufacturing floor is undergoing a technological revolution with industrial AI at its center. From predictive maintenance to quality control, AI is transforming how products are designed, produced, and maintained. But implementing these technologies isn't just about installing sensors and software—it's about empowering your workforce to embrace new tools and processes. How do you overcome AI
#319 Building & Managing Human+Agent Hybrid Teams with Karen Ng, Head of Product at HubSpot
The line between human work and AI capabilities is blurring in today's business environment. AI agents are now handling autonomous tasks across customer support, data management, and sales prospecting with increasing sophistication. But how do you effectively integrate these agents into your existing workflows? What's the right approach to training and evaluating AI team members? With data quality
#318 Master Your Inner Game to Avoid Burnout with Klaus Kleinfeld, Former CEO at Alcoa and Siemens
The modern workplace often glorifies constant productivity and hustle culture, but at what cost? More professionals are burning out earlier in their careers, while elite athletes are extending their peak performance years. What can business leaders learn from high-performance sports about energy management and sustainable success? How do you distinguish between your 'inner game'—managing your ener
Industry Roundup #6: GPT-5 Launch & Scaling Limits, Meta’s Chatbot Guidelines Leak, and AI Safety Concerns
Welcome to DataFramed Industry Roundups! In this series of episodes, we sit down to discuss the latest and greatest in data & AI. In this episode, with special guest, DataCamp Editor Alex, we touch upon the launch of GPT-5, scaling limits in AI, Meta’s leaked chatbot guidelines, trust in AI tools from the Stack Overflow survey, why OpenAI and Anthropic are giving models away to the US gov
#317 How to Reengineer Your Business Processes with Nelson Repenning, Distinguished Professor at MIT Sloan & Don Kieffer, Senior Lecturer in Operations Management at MIT Sloan
Every day, knowledge workers face the challenge of managing competing priorities and constant interruptions. When systems are managing us rather than us managing them, productivity suffers and morale plummets. But what if the key to improvement isn't complex reorganization but rather understanding how work actually flows through your team or organization? How can visualizing your workflow and regu
#316 Enterprise AI Agents with Jun Qian, VP of Generative AI Services at Oracle
Combining LLMs with enterprise knowledge bases is creating powerful new agents that can transform business operations. These systems are dramatically improving on traditional chatbots by understanding context, following conversations naturally, and accessing up-to-date information. But how do you effectively manage the knowledge that powers these agents? What governance structures need to be in pl
#315 DataFramed x Alter Everything: Future-Proofing Your Career in AI and Data Analytics | Richie & Megan Bowers
The relationship between AI and data professionals is evolving rapidly, creating both opportunities and challenges. As companies embrace AI-first strategies and experiment with AI agents, the skills needed to thrive in data roles are fundamentally changing. Is coding knowledge still essential when AI can generate code for you? How important is domain expertise when automated tools can handle techn
#314 How to Have a Career in Data Science in 2025 with Dawn Choo, Data Careers Influencer, Co-Founder at Interview Master
Data science continues to evolve in the age of AI, but is it still the 'sexiest job of the 21st century'? While generative AI has transformed the landscape, it hasn't replaced data scientists—instead, it's created more demand for their skills. Data professionals now incorporate AI into their workflows to boost efficiency, analyze data faster, and communicate insights more effectively. But with the
#313 Developing Better Predictive Models with Graph Transformers with Jure Leskovec, Pioneer of Graph Transformers, Professor at Stanford
The structured data that powers business decisions is more complex than the sequences processed by traditional AI models. Enterprise databases with their interconnected tables of customers, products, and transactions form intricate graphs that contain valuable predictive signals. But how can we effectively extract insights from these complex relationships without extensive manual feature engineeri
#312 Can we Create an AI Doctor? with Aldo Faisal, Professor in AI & Neuroscience at Imperial College
Healthcare AI is rapidly evolving beyond simple diagnostic tools to comprehensive systems that can analyze and predict patient outcomes. With the rise of multimodal AI models that can process everything from medical images to patient records and genetic information, we're entering an era where AI could fundamentally transform how healthcare decisions are made. But how do we ensure these systems ma
#311 The Human Element of AI-Driven Transformation with Steve Lucas, CEO at Boomi
The relationship between humans and AI in the workplace is rapidly evolving beyond simple automation. As companies deploy thousands of AI agents to handle everything from expense approvals to customer success management, a new paradigm is emerging—one where humans become orchestrators rather than operators. But how do you determine which processes should be handled by AI and which require human ju
#310 The State of BI in 2025 with Howard Dresner, Godfather of BI
Business intelligence has been transforming organizations for decades, yet many companies still struggle with widespread adoption. With less than 40% of employees in most organizations having access to BI tools, there's a significant 'information underclass' making decisions without data-driven insights. How can businesses bridge this gap and achieve true information democracy? While new technolog
#309 What Science Fiction Can Tell Us About the Future of AI with Ken Liu, Sci-Fi Author
Technology and human consciousness are converging in ways that challenge our fundamental understanding of creativity and connection. As AI systems become increasingly sophisticated at mimicking human thought patterns, we're entering uncharted territory where machines don't just assist creative work—they actively participate in it. But what does this mean for the future of human creativity and our
Industry Roundup #5: AI Agents Hype vs. Reality, Meta’s $15B Stake in Scale AI, and the First Fully AI-Generated NBA Ad
Welcome to DataFramed Industry Roundups! In this series of episodes, we sit down to discuss the latest and greatest in data & AI. In this episode, with special guest, DataCamp COO Martijn, we touch upon the hype and reality of AI agents in business, the McKinsey vs. Ethan Mollick debate on simple vs. complex agents, Meta's $15B stake in Scale AI and what it means for data and talent, Appl
#308 A Framework for GenAI App and Agent Development with Jerry Liu, CEO at LlamaIndex
The enterprise adoption of AI agents is accelerating, but significant challenges remain in making them truly reliable and effective. While coding assistants and customer service agents are already delivering value, more complex document-based workflows require sophisticated architectures and data processing capabilities. How do you design agent systems that can handle the complexity of enterprise
#307 Human Guardrails in Generative AI with Wendy Gonzalez & Duncan Curtis, CEO & SVP of Gen AI at Sama
The line between generic AI capabilities and truly transformative business applications often comes down to one thing: your data. While foundation models provide impressive general intelligence, they lack the specialized knowledge needed for domain-specific tasks that drive real business value. But how do you effectively bridge this gap? What's the difference between simply fine-tuning models vers
#306 The Next Generation of Business Intelligence with Colin Zima, CEO at Omni
The modern data stack has transformed how organizations work with data, but are our BI tools keeping pace with these changes? As data schemas become increasingly fluid and analysis needs range from quick explorations to production-grade reporting, traditional approaches are being challenged. How can we create analytics experiences that accommodate both casual spreadsheet users and technical data m
#305 RAG 2.0 and The New Era of RAG Agents with Douwe Kiela, CEO at Contextual AI, Adjunct Professor at Stanford University, Inventor of RAG
Retrieval Augmented Generation (RAG) continues to be a foundational approach in AI despite claims of its demise. While some marketing narratives suggest RAG is being replaced by fine-tuning or long context windows, these technologies are actually complementary rather than competitive. But how do you build a truly effective RAG system that delivers accurate results in high-stakes environments? What
#304 Accelerating Data Science with Nick Becker, Technical Product Manager at NVIDIA & Dan Hannah, Associate Director at SES AI
GPU acceleration is transforming how data scientists tackle computationally intensive problems in the AI and materials science fields. When dealing with billions of potential molecular combinations or massive datasets requiring dimensionality reduction, traditional CPU approaches often become prohibitively slow and expensive. How can data professionals determine when GPU acceleration will provide
#303 Increasing Your Organization's AI Maturity with Iwo Szapar & Eryn Peters, Founders at AI Maturity Index
AI maturity isn't achieved through technology alone—it requires organizational alignment, cultural readiness, and strategic implementation. Companies across industries are working to move beyond experimental AI use toward systematic integration that delivers measurable business value. How do you assess where your organization stands on the AI maturity spectrum? What frameworks can help prioritize
#302 Making AI Applications like Greased Lightning with William Falcon, CEO at Lightning AI
AI tooling continues to expand with specialized solutions for every step of the development process. For data scientists and engineers, this creates a paradox: more options but potentially more complexity and integration challenges. How do you determine which tools actually improve productivity versus adding unnecessary overhead? Should you prioritize flexibility with individual best-of-breed comp
#301 What the Like Button Tells You About Your Customers with Bob Goodson, Inventor of the Like Button
The like button has transformed how we interact online, becoming a cornerstone of digital engagement with over 7 billion clicks daily. What started as a simple user interface solution has evolved into a powerful data collection tool that companies use to understand customer preferences, predict trends, and build sophisticated recommendation systems. The data behind these interactions forms what ex
Industry Roundup #4: O3 & O4-mini, LLama 4’s Rocky Release & Google’s Agent Ecosystem
Welcome to DataFramed Industry Roundups! In this series of episodes, Adel & Richie sit down to discuss the latest and greatest in data & AI. In this episode, we touch upon the launch of OpenAI’s O3 and O4-mini models, Meta’s rocky release of Llama 4, Google’s new agent tooling ecosystem, the growing arms race in AI, the latest from the Stanford AI Index report, the plausibility of AGI and
#300 End to End AI Application Development with Maxime Labonne, Head of Post-training at Liquid AI & Paul-Emil Iusztin, Founder at Decoding ML
The roles within AI engineering are as diverse as the challenges they tackle. From integrating models into larger systems to ensuring data quality, the day-to-day work of AI professionals is anything but routine. How do you navigate the complexities of deploying AI applications? What are the key steps from prototype to production? For those looking to refine their processes, understanding the full
#299 From Panic to Profit, Via Data with Bill Canady, CEO at Arrowhead Engineered Products
Data-driven turnarounds are transforming how struggling businesses find their path back to profitability. When companies falter, the key to recovery can often lies in understanding which 20% of customers and products generate 80% of profits. But how do you quickly identify these critical assets when time is running out? What metrics should you prioritize when cash flow is tight? For data professio
#298 Data Storytelling Skills to Increase Your Impact with Kat Greenbrook, Author of The Data Storyteller's Handbook
We live in an era where data is abundant, yet making sense of it is harder than ever. The best insights often go unnoticed—not because they lack value, but because they lack a compelling story. Simply presenting numbers isn’t enough; the way we shape and frame data determines whether it sparks action or fades into the background. Crafting a strong data story means knowing your audience, structurin
#297 The Past and Future of Language Models with Andriy Burkov, Author of The Hundred-Page Machine Learning Book
Misconceptions about AI's capabilities and the role of data are everywhere. Many believe AI is a singular, all-knowing entity, when in reality, it's a collection of algorithms producing intelligence-like outputs. Navigating and understanding the history and evolution of AI, from its origins to today's advanced language models is crucial. How do these developments, and misconceptions, impact your d
#296 How YPulse Built an AI Application for Market Research with Dan Coates, President at YPulse
The explosion of content in market research has created a paradox - more information but less time to consume it. Companies are now turning to AI chatbots to solve this problem, transforming how professionals interact with research data. Instead of expecting teams to read everything, these tools allow users to extract precisely what they need when they need it. This approach is proving not just mo
#295 How To Get Hired As A Data Or AI Engineer with Deepak Goyal, CEO & Founder at Azurelib Academy
The role of data and AI engineers is more critical than ever. With organizations collecting massive amounts of data, the challenge lies in building efficient data infrastructures that can support AI systems and deliver actionable insights. But what does it take to become a successful data or AI engineer? How do you navigate the complex landscape of data tools and technologies? And what are the key
#294 Six Skills Data Professionals Need To Succeed with Abhijit Bhaduri, Brand Evangelist & Former General Manager of Global L&D at Microsoft
As data professionals, mastering the technical aspects of AI and data is only half the battle. The real challenge lies in effectively communicating insights to drive action and influence decisions. How do you ensure your data stories resonate with diverse audiences? It's not just about the numbers—it's about crafting a narrative that speaks to stakeholders. What strategies can you employ to make y
#293 Unlocking Humanity in the Age of AI with Faisal Hoque, Founder and CEO of SHADOKA
The integration of AI into everyday business operations raises questions about the future of work and human agency. With AI's potential to automate and optimize, how do we ensure that it complements rather than competes with human capabilities? What measures can be taken to prevent AI from overshadowing human input and creativity? How do we strike a balance between embracing AI's benefits and pres
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