The Joe Reis Show

The data job market is evolving, but it's still there. In this episode, I give my thoughts on the data job market, ways to navigate it, going solo and having a Plan B, and more.

In this episode, I sit down with Demetrios Brinkmann (godfather of the MLOps Community) to talk about the absolute Wild West of AI right now. We cover how fast coding agents are changing the game, the reality of "vibe coding" your own CRM , and how Demetrios's community saved $20,000 just by ditching bloated enterprise tools.But we don't just talk tech. We get into the weeds on the content creation pipeline, from the bizarre rise of AI OnlyFans to the "Doorman Paradox" of automated content. Finally, we spill some serious inside baseball on the tech sponsorship game, calling out the sheer audacity of heavily-funded startups expecting free labor from communities , and why protecting your reputation is worth more than any quick paycheck.

In this episode, Matt Housley and I reunite for a Friday catch-up, bringing back some of that classic Monday Morning Data Chat energy. We dive into the absurdity of the "buzzword industrial complex," and why declaring it the "Year of Context" is mostly just industry hype, per usual.We also tackle the chaotic reality of deploying AI agents (including the ultimate YOLO, OpenClaw) without proper data governance, the Anthropic class action lawsuit regarding copyright, and why regional conferences like DataTune are awesome. Finally, we discuss the shifting landscape of media, the death of traditional book publishing models, and the rise of the independent, niche creator.

The white-collar tech industry isn't what it used to be, and anyone could be on the chopping block at a moment's notice. With tens of thousands of highly skilled people getting laid off from Big Tech on a seemingly bi-weekly basis, competing in the traditional job market is brutal right now.In this episode, Jody Hesch and I discuss why building a freelance data consulting business isn't just a career pivot—it is a necessary Plan B. We break down the exhaustion of constantly reinventing yourself and navigating new team dynamics every time you switch full-time roles. We also explore the counterintuitive reality that by going freelance, you only have to build your network and reputation once to create a repeatable motion. Whether you are actively looking for an exit or just realizing that the gig economy is coming for data engineering, this conversation covers the realities of making the jump.

In this conversation, Paul Blankley and Ryan Janssen, founders of Zenlytic, drop in to discuss the massive shift in how we build software and handle data. We trace their journey from studying early NLP and Transformers at Harvard right when the BERT paper dropped, to building a company that relies on cutting-edge LLMs. As far as I know, they're the first to use LLM's for analytics.We dive deep into the reality of the agentic era: engineers are no longer writing the bulk of the code; they are managing agents, verifying outputs, and maintaining ridiculously high standards. We also explore why the industry needs to embrace "net negative scaffolding" as models get smarter, and why having good "taste" might be the ultimate human moat left in tech.Bonus: To prove that software development is changing faster than ever, we literally "vibe coded" a brand-new CRM called "Slop Force" in 20 minutes during this episode. Zenlytic: https://www.zenlytic.com/

We often hear about the AI skills gap, where people need to get training on the latest AI tools. There's also the AI competence gap, where people might not have the skills or competence in a field, and use AI to mask over those shortcomings. The results are what you expect - chaos. In this episode, I unpack these two gaps, and do my usual ranting about learning the fundamentals and investing in oneself.----------

In this conversation, I sit down with Tim Delisle and Chris Crane, co-founders of 514, to discuss bridging the gap between software development and data engineering. We cover their experience leading global data engineering at Nike and why software teams are increasingly taking ownership of heavy analytical workloads.We also dive into how they are building the Moose Stack to give developers a local-first, code-first analytics experience. Finally, we explore how AI co-pilots are acting like an "army of interns" to fundamentally change how we write code , and why the "personal data lake" might be the future of privacy and local compute.Check out 514 & The Moose Stack: https://www.fiveonefour.com/

Sadie St. Lawrence joins me to unpack her concept of the "AI Orchestrator," explaining how it shifts our mindset from being a musician to a conductor in the age of AI. She shares insights from her work at the Human-Machine Collaboration Institute (HMCI), detailing how her team is building AI-powered solutions and tackling complex problems. We also chat about the common pitfalls in AI adoption, from unfounded fears to "work slop," and why foundational systems thinking remains paramount.

This week, I published an article called "2028, the Great Data Reckoning," which got a ton of response. Although I originally meant it to be satire, when I re-read it I felt like it was actually a glimpse into what's happening in our field right now. In this episode, I chat about the implications of the Great Data Reckoning on practitioners, leaders, and founders. Article: https://joereis.substack.com/p/2028-the-great-data-reckoning----------

In this episode, I sit down with Prashant Sridharan, a 30-year veteran of developer marketing who has shaped go-to-market strategies for tech giants like Sun Microsystems, Microsoft, AWS, Facebook, and Twitter, and currently runs product marketing at Supabase. We dive deep into the origins of DevRel and how marketing to developers has evolved in an increasingly noisy, AI-saturated landscape.Topics covered:- Transitioning from massive tech companies to the fast-paced startup world - How to genuinely measure the success of Developer Relations without ruining communities - Using AI tools like Claude to accelerate mechanical marketing tasks while preserving authentic storytelling - The shift from traditional SEO to GEO (Generative Engine Optimization) for developer tools - The thrill of live, unscripted coding demos and stories from sharing the stage with Steve Ballmer - Prashant's upcoming fiction novel, The Midnight Coders Children, and the craft of writing Find more from Prashant at StrategicNerds.com and check out his non-fiction book, Picks and Shovels: https://amzn.to/4cJ2TRO

For 40+ years, the data industry has tried to teach good practices and get adoption, often in the same way. And for 40+ years, that approach keeps failing over and over. Based on the recent Practical Data Community Survey, practitioners face challenges like time pressures, lack of direction, and lack of clear ownership. Do we need to try something else as an industry? Or do we continue to be the poster child for the definition of insanity - doing the same thing over and over, yet expecting different results? I hope not.

Why are we still using row-based protocols like ODBC and JDBC in a column-oriented world? In this episode, I sit down with Ian Cook, co-founder of Columnar and a long-time Apache Arrow contributor, to discuss the critical infrastructure changes needed to speed up modern analytics and AI.We dive deep into the technical bottlenecks of legacy standards - specifically the "serialization tax" of converting columns to rows and back again - and how ADBC (Arrow Database Connectivity) solves this by keeping data columnar from end-to-end. Ian also shares his insights on the intersection of tabular data and LLMs, why AI agents need better access to OLAP systems, and the tension between vibe coding speed and the stability required for critical open-source infrastructure.

The 2026 Practical Data Community State of Data Engineering dropped this week. It's full of some obvious and very counterintuitive information about the state of data engineers around the globe, in all sizes and types of organizations. Check it out!Also, I talk about the book writing process, where I messed up on this latest book, it's progress toward publication, and more.Survey: https://joereis.github.io/practical_data_data_eng_survey---------------------This episode is brought to you by Ellie.aiEllie makes data modeling as easy as sketching on a whiteboard—so even business stakeholders can contribute effortlessly. By skipping redraws, rework, and forgotten context, and by keeping all dependencies in sync, teams report saving up to 78% of modeling time.Check out Ellie: https://ellie.ai/

I sat down with Paul Dudley (CEO) and Ricky Thomas (CTO) from StreamKap to catch up on where the world of streaming data is heading—and things have changed fast since we last spoke.We dive into the concept of "vibe coding" and how AI is radically accelerating how we build software (I even share a story about building a data analysis tool in an hour). But the real meat of this conversation is about the intersection of streaming data and AI agents. Everyone is building agents, but without real-time context, they're flying blind. We discuss why streaming is a missing link for agentic workflows, the shift from dashboards to automated decision-making, and why SaaS companies are racing to build walled gardens around their data.We also get into the nitty-gritty of the UK vs. US tech markets, the resurgence of PR in the AI era, and StreamKap's upcoming move into the Snowflake native app ecosystem.Streamkap: https://streamkap.com/

This week was a doozy with new AI releases, the stock market, and more. It really feels like this was the first tremor in AI's impact on the SaaS market. What's do I think is next? Listen and find out.

In this episode, I sit down with Mike Driscoll, founder of Rill Data, to discuss the evolving landscape of business intelligence and data engineering. We explore why the industry keeps "rediscovering" old concepts like the semantic layer and how the rise of AI agents is forcing us to rethink how we structure data.Mike shares his insights on the "shape" of analytics, debating whether conversational interfaces will replace dashboards or simply complement them. We also dig into the growing demand for data engineering, the importance of watermarks and temporal semantics, and why data visualization remains a critical tool for "trust but verify" in an AI world.Rill Data Mike's Podcast: Data Talks on the Rocks

As I use AI, I'm finding that I create MORE work for myself, not less. One task completed means five more to do. This is the paradox of today - AI might actually mean more work, not less. I talk about this, the Data Day Texas final episode, and more.Check out the review I did of Cube's new analytics agent: https://www.youtube.com/watch?v=p3frGJOUl1E(Thanks to Cube for partnering on the review)

Lak Lakshmanan had a successful career in Private Equity and Big Tech, but he realized he couldn't just "coach the game" while the rules were changing. He had to get back on the field play it. We discuss vertical AI, the "foolhardiness" required to start a company , the reality of the AI technology wave, and why sitting on the sidelines is the biggest risk of all.LinkedIn: https://www.linkedin.com/in/valliappalakshmananGenerative AI Design Patterns (book): https://amzn.to/45v0xBO

In this episode, I talk about how I'm kind of living in a bubble of cool tech and AI, and how the 99% of businesses out there are still grappling with the same old data and tech problems they've always dealt with.I also talk about how me and my friends are using AI to automate the boring stuff and scratch our own itches.

In this episode, I sit down with science fiction author, activist, and journalist Cory Doctorow to unpack his viral concept of Enshitification, the three-act tragedy of platform decay: 1. be good to users 2. lock them in 3. extract value from users to feed advertisers and shareholdersWe also dive into:- The AI bubble: Cory's case that parts of the sector are propped up by aggressive accounting and incentives, not durable value.- The “Reverse Centaur”: How workers (from Amazon drivers to radiologists) are being reorganized to serve machine workflows, rather than machines serving humans.- Software engineering vs. “vibe coding”: Why autocomplete isn't engineering, and why AI can't replace process knowledge and domain context.- The Post-American Internet: What happens when the U.S. weaponizes platforms, and the rest of the world builds alternatives.About Cory Doctorow: Cory is a multi-time international bestselling author, special advisor to the Electronic Frontier Foundation, and creator of the blog/newsletter Pluralistic.If you got value from this conversation, hit Follow and share it with one person who cares about the future of tech.

Tech is full of smart people with smart ideas - enterprise data models, ontologies, data mesh, proprietary AI strategies - that repeatedly fail to gain traction. When they fail, the blame usually goes to "stupid users", "lazy and immature organizations." Perhaps, but I don't think that's the whole story, and if you adopt that mindset, you're sure to keep failing.I think there's more to the story. Listen and find out...

In this episode, I visited the Hex office and sat down with Barry McCardle (CEO of Hex) to talk about the massive shift we're seeing in the data stack. Countless companies have spent decades buying BI tools in the hope of "self-serve Nirvana," yet most dashboards still raise more questions than they answer. Barry and I dive into why the traditional dashboard is becoming a "jumping-off point" rather than a destination, and how AI agents are finally closing the gap between having a question and getting a sophisticated answer.We also discuss building tools people love, "commitment engineering", Barry's story, and much more.

What status game are you playing? Are you trying to outcompete others, or playing your own game? In this episode, I talk about status games in data and careers in general.

The technology industry is prone to moving fast and forgetting its history. This is a shame because our industry is built on the shoulders of many giants, often long forgotten. Bill Inmon, Roger Whatley, and I discuss the history of technology and computing, covered in their new book, From Stone to Silicon. We talk about the big people and moments in technology and computing, and much more.From Stone to Silicon (book): https://amzn.to/4pLfqat

Welcome to 2026! In this spontaneous Friday AMA, I take listener questions on ontologies, the “leaky abstractions” of AI coding tools, why the “button pusher” era of engineering is a professional dead end, and the shifting landscape of data engineering.I also provides an update on my upcoming book, Mixed Model Arts (launching in March 2026), and discuss the unexpected convergence of library science, ontologies, and traditional data modeling, something not on my 2025 bingo card.Great turnout, especially for no notice. Thanks to everyone who showed up!

Happy 2026! In this episode, I rant about whether vibe coding and AI coding agents makes the Law of Leaky Abstractions obsolete, making your first dollar (or whatever currency), and more.The Law of Leaky Abstractions: https://www.joelonsoftware.com/2002/11/11/the-law-of-leaky-abstractions/If you like this podcast, please take 10 seconds and give it a rating or review on your podcast platform of choice. It will go a long way to giving the show more visibility. Thanks!

2025 is nearly gone, and in this episode, I give some thoughts on what I think might happen in 2026. I also chat about this week's surge of interest in Kimball vs Inmon (and the podcast I tried to organize with them) and much more.

“What I built today might be obsolete tomorrow.”This is something I heard this week from a developer, and this is not uncommon given the warp speed nonstop advancement of AI models every week. We used to measure the rate of change in months or years. Now it's days or weeks.In this episode, I talk about why writing code is rarely hard part, and why having good taste and shipping things that people love is the most important things we can do.

In this episode, Nik Suresh returns to the show to discuss his first year running a bootstrapped services company. And no, he probably will not throat punch or pile drive you.Nik explains why he moved away from hourly billing to fixed pricing, why writing code is often the least profitable part of a project, and how to spot "status games" in the tech industry. We also dive into the current state of AI, why bad leadership is the real problem behind failed tech initiatives, and trade stories about MMA and boxing.We debunk the myth that starting a business has to be miserable, explore the performative nature of "hustle culture" in Silicon Valley, and break down why engineers often struggle with consulting sales.

Oh yeah...ontologies. In this mini-clip from Matt Housley and I, we chat about why ontologies are super popular now.

Had an interesting discussion with my 15 year old son. He and his friends see white collar work as “cooked.” They see it as a rat race where the work is increasingly insecure, abusive, and meaningless. Then there's the looming question of AI…Instead, they're interested in careers they find meaningful and not as exposed to whatever AI does to work. And if they own a company, they'll just hire “clankers” whenever that moment arrives.I'm excited that these kids are looking at what's happening right now, questioning if it's their path, and choosing a life that's fit for them.More broadly, especially in the age of AI, I think some of the most important conversations we need to have is over what we find valuable and meaningful, making a living and the nature of work, and the nature of community.

It's Friday! Matt Housley and I catch up to discuss the aftermath of AWS re:Invent and why the industry's obsession with AI Agents might be premature. We also dive deep into the hardware wars between Google and NVIDIA , the "brain-damaged" nature of current LLMs , and the growing "enshittification" of the internet and platforms like LinkedIn. Plus, I reveals some details about my upcoming "Mixed Model Arts" project.

In this episode, I sit down with Mark Freeman and Chad Sanderson (Gable.ai) to discuss the release of their new O'Reilly book, Data Contracts: Developing Production-Grade Pipelines at Scale. They dive deep into the chaotic journey of writing a 350-page book while simultaneously building a venture-backed startup.The conversation takes a sharp turn into the evolution of Data Contracts. While the concept started with data engineers, Mark and Chad explain why they pivoted their focus to software engineers. They argue that software engineers are facing a "Data Lake Moment, "prioritizing speed over craftsmanship, resulting in massive technical debt and integration failures.Gable: https://www.gable.ai/

I meet a lot of people who want to accomplish major goals next year. Then the year comes and goes and most people are still waiting to get started.It's almost December. Rather than wait until the New Year to get going, use December to plan how you'll execute on "that thing" you're itching to accomplish. Time waits for nobody, so get going.

In this episode, Ciro Greco (Co-founder & CEO, Bauplan) joins me to discuss why the future of data infrastructure must be "Code-First" and how this philosophy accidentally created the perfect environment for AI Agents.We explore why the "Modern Data Stack" isn't ready for autonomous agents and why a programmable lakehouse is the solution. Ciro explains that while we trust agents to write code (because we can roll it back), allowing them to write data requires strict safety rails. He breaks down how Bauplan uses "Git for Data" semantics - branching, isolation, and transactionality - to provide an air-gapped sandbox where agents can safely operate without corrupting production data. Welcome to the future of the lakehouse.Bauplan: https://www.bauplanlabs.com/

Just launched your Substack? Great! Here's what to do next.This episode covers the realities of writing long-form in public, the traps that cause most writers to stall, how to build consistency, and how to grow an engaged audience from day one.

Data engineering is undergoing a fundamental shift. In this episode, I sit down with Nick Schrock, founder and CTO of Dagster, to discuss why he went from being an "AI moderate" to believing 90% of code will be written by AI. Being hands on also led to a massive pivot in Dagster's roadmap and a new focus on managing and engineering context.We dive deep into why simply feeding data to LLMs isn't enough. Nick explains why real-time context tools (like MCPs) can become "token hogs" that lack precision and why the future belongs to "context pipelines": offline, batch-computed context that is governed, versioned, and treated like code.We also explore Compass, Dagster's new collaborative agent that lives in Slack, bridging the gap between business stakeholders and data teams. If you're wondering how your role as a data engineer will evolve in an agentic world, this conversation maps out the territoryDagster: dagster.io Nick Schrock on X: @schrockn

The days of easy entry into data jobs over. Maggie Wolff joins the show to discuss the new reality of the data career landscape. We dive into why the bar is higher than ever and why "cold DMing" on LinkedIn is a terrible strategy.Maggie also breaks down her secret strategy for networking as an introvert: treating events like a game or role-playing a more extroverted friend. Plus, we discuss the rise of AI in education, the problem with "lazy" learning , and why companies replacing humans with AI are making a mistake.

Matt Housley joins me for our monthly round-up of topics. This time, there's danger everywhere - The AI Bubble, how vibe coding is evolving, AI slop, and more.

After 1,500+ conversations with CDOs and VPs of data , guest Malcolm Hawker noticed a disturbing pattern: a "limiting mindset" that causes data leaders to fail. He argues that too many leaders blame external factors such as "culture" , "data literacy", or a lack of support rather than taking accountability for delivering value.In this conversation, Malcolm breaks down how this mindset is reinforced by the analyst and consultant community and why it leads to a "value fatigue" where no one can prove their own ROI. He offers a clear path forward, starting with a simple 3-question framework for any new CDO and explains why "culture" is actually an outcome of delivering value, not a prerequisite for it. We also discuss his new book, "The Data Hero Playbook," tackle the "AI Ready" myth , explaining why conflating it with "BI Ready" is holding companies back and why your data is likely "good enough" to start right now.

In this conversation, Dr. Cecilia Dones and I discuss the social skills we're losing as AI becomes more integrated into our lives. We explore the erosion of social norms, from AI companions joining Zoom calls without consent, endless enshitified content, to my son's generation calling AI girlfriends "clankers".Is there hope? We break down the "rage currency" that dominates media and the positive AI stories that go unheard. The biggest takeaway: as the world becomes more synthetic, "showing up" in person will become the ultimate "premium value."

In conversations I've been having with leaders and practitioners, there's some open-ended questions about the impact of AI on vendors and open-source projects. If you don't have a moat, you need to start thinking about how AI coding tools will erode the edges of your product. And what about getting users and traction? I cover this and much more in this episode. Enjoy!

Sujay Dutta and Sidd Rajagopal, authors of "Data as the Fourth Pillar," join the show to make the compelling case that for C-suite leaders obsessed with AI, data must be elevated to the same level as people, process, and technology.They provide a practical playbook for Chief Data Officers (CDOs) to escape the "cost center" trap by focusing on the "demand side" (business value) instead of just the "supply side" (technology). They also introduce frameworks like "Data Intensity" and "Total Addressable Value (TAV)" for data.We also tackle the reality of AI "slopware" and the "Great Pacific garbage patch" of junk data , explaining how to build the critical "context" (or "Data Intelligence Layer") that most GenAI projects are missing. Finally, they explain why the CDO must report directly to the CEO to play "offense," not defense.

Matt Turck (VC at FirstMark) joins the show to break down the most controversial MAD (Machine Learning, AI, and Data) Landscape yet. This year, the team "declared bankruptcy" and cut over 1,000 logos to better reflect the market reality: a "Cambrian explosion" of AI companies and a fierce "struggle and tension between the very large companies and the startups".Matt discusses why incumbents are "absolutely not lazy" , which categories have "largely just gone away" (like Customer Data Platforms and Reverse ETL) , and what new categories (like AI Agents and Local AI) are emerging. We also cover his investment thesis in a world dominated by foundation models, the "very underestimated" European AI scene , and whether an AI could win a Nobel Prize by 2027.https://www.mattturck.com/mad2025

I travel a TON, and the most frequent questions I get relate to traveling: Why I do it and any tips I have for traveling. Here, I answer those questions and more.

Jeremiah Lowin, founder of Prefect , returns to the show to discuss the seismic shift in the data and AI landscape since our last conversation a few years ago. He shares the wild origin story of FastMCP, a project he started to create a more "Pythonic" wrapper for Anthropic's Model Context Protocol (MCP).Jeremiah explains how this side project was incorporated into Anthropic's official SDK and then exploded to over a million downloads a day after MCP gained support from OpenAI and Google.He clarifies why this is an complementary expansion for Prefect, not a pivot , and provides a simple analogy for MCP as the "USB-C for AI agents". Most surprisingly, Jeremiah reveals that the primary adoption of MCP isn't for external products, but internally by data teams who are using it to finally fulfill the promise of the self-serve semantic layer and create a governable, "LLM-free zone" for AI tools.

I'm back, and give some notes from the road, thoughts on choosing tools and vendors, having a plan B for tools, and more.

There's no shortage of technical content for data engineers, but a massive gap exists when it comes to the non-technical skills required to advance beyond a senior role. I sit down with Yordan Ivanov, Head of Data Engineering and writer of "Data Gibberish," to talk about this disconnect.We dive into his personal journey of failing as a manager the first time, learning the crucial "people" skills, and his current mission to help data engineers learn how to speak the language of business.Key areas we explore:The Senior-Level Content Gap: Yordan explains why his non-technical content on career strategy and stakeholder communication gets "terrible" engagement compared to technical posts, even though it's what's needed to advance.The Managerial Trap: Yordan's candid story about his first attempt at management, where he failed because he cared only about code and wasn't equipped for the people-centric aspects and politics of the role.The Danger of AI Over-reliance: A deep discussion on how leaning too heavily on AI can prevent the development of fundamental thinking and problem-solving skills, both in coding and in life.The Maturing Data Landscape: We reflect on the end of the "modern data stack euphoria" and what the wave of acquisitions means for innovation and the future of data tooling.AI Adoption in Europe vs. the US: A look at how AI adoption is perceived as massive and mandatory in Europe, while US census data shows surprisingly low enterprise adoption rates

The world of data is being reset by AI, and the infrastructure needs to evolve with it. I sit down with streaming legend Tyler Akidau to discuss how the principles of stream processing are forming the foundation for the next generation of "agentic AI" systems.Tyler, who was an AI cynic until recently, explains why he's now convinced that AI agents will fundamentally change how businesses operate and what problems we need to solve to deploy them safely.Key topics we explore:From Human Analytics to Agentic Systems: How data architectures built for human analysis must be re-imagined for a world with thousands of AI agents operating at machine speed.Auditing Everything: Why managing AI requires a new level of governance where we must record all data an agent touches, not just metadata, to diagnose its complex and opaque behaviorThe End of Windowing's Dominance: Tyler reflects on the influential Dataflow paper he co-authored and explains why he now sees a table-based abstraction as a more powerful and user-friendly model than focusing on windowing.The D&D Alignment of AI: Tyler's brilliant analogy for why enterprises are struggling to adopt AI: we're trying to integrate "chaotic" agents into systems built for "lawful good" employees.A Reset for the Industry: Why the rise of AI feels like the early 2010s of streaming, where the problems are unsolved and everyone is trying to figure out the answers.

I still see some companies acting sheepish with AI, too scared to even try it out. That's a massive mistake. Now is the time to play offense with incorporating AI into your company and reimagining what it can become.

Are dashboards dead? For complex enterprise use cases, the answer might be yes. In this episode, I'm joined by Irina Malkova (VP Data & AI at Salesforce), to discuss her team's transformational journey from building complex dashboards to deploying AI-powered conversational agents.We dive deep into how this shift is not just a change in tooling, but a fundamental change in how users access insights and how data teams measure their impact.Join us as we cover:The Shift from Dashboards to Agents: We discuss why dashboards can create a high cognitive load and fail users in complex scenarios , and how conversational agents in the flow of work (like Slack) provide targeted, actionable insights and boost adoption.What is Product Telemetry?: Irina explains how telemetry is evolving from a simple engineering observability use case to a critical data source for AI, machine learning, and recommendation systems.Why Standard RAG Fails in the Enterprise: Irina shares why typical RAG approaches break down on dense, entity-rich corporate data (like Salesforce's help docs) where semantic similarity isn't enough, leading to the rise of Graph RAG.The New, Measurable ROI of Data: How moving from BI to agents allows data teams to precisely measure impact, track downstream actions, and finally have a concrete answer to the ROI question that was previously impossible to justify.Data Teams as Enterprise Leaders: Why data teams are uniquely positioned to lead AI transformation, as they hold the enterprise "ontology" and have experience building products under uncertainty.