Come get your badge, a bite, and coffee before the day kicks off.
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Come get your badge, a bite, and coffee before the day kicks off.
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AI is already reshaping how every person at work asks questions and gets answers, but it's also reshaping how much we can trust those answers. Hear from Hex co-founder and CEO Barry McCardel on how organizations can thrive in this new era and how Hex is building toward this future.
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Two leaders from StubHub have spent their careers building with data and building data teams. Over the last few years, they've constantly reevaluated roles amid AI innovations and obsessed over what comes next for data careers. In this talk, Meghana and Nova walk through how they navigated the new normal as AI entered every aspect of their profession. They'll share how their thinking evolved: Not by clinging to old job definitions, but by re-contextualizing what their roles were for all along. Equal parts group therapy and field report, this session is for any data professional who's wondered where their role is headed.
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Every analytics team has more questions than it has people to answer. With AI accelerating self-service—and generating even more questions—The New York Times is finding that this challenge is becoming increasingly acute. And more analysis does not necessarily lead to better decisions. In this talk, Mariam shares how The New York Times is approaching this challenge, where AI fits into the solution, and how she sees the analytics function evolving in response.
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Check out the Hexpert bar or Swag store.
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When Chime®'s data team sought to scale AI across its organization, Jordan Farrer, Director of Data Science, put humans at the center of the work. He resisted pressure to mass-ingest the "valuable" context sitting in Confluence, Slack, and Google Drive. In this talk, Jordan, Dori, and Connor share where human judgment is non-negotiable, where AI belongs, and how they manage context at scale.
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How good are AI models at the work we actually pay analysts to do? There’s lots of SQL benchmarks, but that's a small fraction of “analytics”. A skilled analyst has to figure out what's really being asked, make sense of messy and conflicting data, and know whether a result is strong enough to base a decision on. We should expect the same of AI! At Hex, we built DataBench to test that standard across 100 realistic business questions. It changed how we view which ones are actually good at analytics. Izzy Miller shares the mistakes behind those results, what it took to catch them, and where models shine.
Breakout

AI has made it possible for anyone to build whatever they want: a dashboard, an app, a slide deck. The how isn’t the constraint anymore; it’s deciding what you actually want to create. In this 30-minute workshop, we’ll teach you how to prompt, design, and build generative apps. No prior coding required, and no matter where you sit — marketing, sales, CS, ops, or the data team — if you can describe what you want, you can build it.
Workshop
There are a lot of stories out there about AI on data, and many concerns about whether it can really work in a complex org. At the NHL, Oliver Gomes built his data team for the exact purpose of enabling everyone to self-serve using modern AI tools. In this session, you’ll learn how Oliver re-architected his team, what they learned, and how it resulted in a repeatable operating model that will scale across the league.
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AI can build a chart, write a query, even frame a story. But you’re the one who knows your business. As data teams take on new technology, the real work shifts to judgment: curating context, catching the moments AI gets the framing wrong, and creating the conditions for people across the business to use AI well. Andy Cotgreave draws on real examples of AI-driven data storytelling to lay out what leadership looks like when the technology moves faster than any policy can.
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Lunch
Join us during lunch as Joe Reis, author of Mixed Model Arts, hosts a live episode of his podcast, the Joe Reis Show. His guest will be Hex's co-founder and CEO, Barry McCardel. They'll dig into the evolving world of data, analytics, and AI — and what's next for how teams work with data. Bring your lunch and your questions.
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Every company suddenly has an AI mandate. "Be AI native," the leadership deck says, then hands you no map for what that means or how to get there. It's a directive without direction, and most teams are working it out alone. This lunch roundtable splits by function — marketing, ops, sales, and more — so you compare notes with people facing your exact version of the mandate: what does "AI native" mean in your world, and what's a waste of time?
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What's next for Hex? We can't wait to show you.
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When the digital editor-in-chief can vibe-code their own app in minutes, their expectations of waiting on the data team shrink to near zero. As data teams meet AI-speed expectations, the work shifts to judgment: knowing when fast-and-directional wins, when numbers must be exact, and owning the meaning people take from the data. Sean Hunter, SVP of Core Engineering at Fox, uses real examples to show how to deliver both when everyone expects data instantly.
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Change is the only constant, and every tweak to your agent's context brings uncertainty about what it'll actually do downstream. You update a guide, refine a semantic model, adjust an instruction... and then what? Does it help? In this hands-on workshop, we’ll teach you how to develop evals and ensure your context work is actually improving answers through measurable results.
Workshop
Check out the Hexpert bar or Swag store.
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Welcome to the post-AI data stack era. Now data teams have two jobs: enable everyone to confidently build with data and create a singular reality for their company to operate. The differentiator between an okay and a great data scientist is their ability to encode judgment, influence, and consensus into infrastructure. Join Ian Macomber, VP of Data at Ramp, as he shares the story of how we got here and why the scarce resource becomes company-wide truth.
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For years, the deal between data teams and business stakeholders was simple: you ask, we build. Then AI put a prompt bar in front of everyone, and suddenly business users could explore data themselves. Today both sides are figuring out new rules of engagement in real time. In this roundtable, John Choi and Nick Pasquinzo will deliberately and unapologetically force you to break down the barriers between business users and data folks. Come prepared for an interactive, lively discussion.
Breakout
With AI, analysis can happen anywhere. Devon Mittow, who leads data platform work at Lyft, keeps metrics and context in a tool-agnostic layer so analysis can start in Claude, pass through an MCP, and land in Hex. He and Hex co-founder and CTO Caitlin Colgrove dig into the next wave of data engineering: less about moving data between tools, more about structuring context for whatever surface the question comes from.
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If you're on a data team, you used to be measured by output - number of tickets resolved or dashboards created. Now, you're building products to help your whole org take advantage of agents - semantic models, agent skills, and integrations with MCP - Join Nikita to learn how he's thinking about building products for agents - and how you can use this same thinking as you to build out your data and AI platform.
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Data teams finally got what they've wanted for a decade: self-service. Stakeholders no longer wait in the queue—but that shift changed how data teams work. Four leaders who rebuilt their teams this year join Katie Bauer: Emily (LangChain), Nick (Ro Health), Evan (Whatnot), and Firdevs Abacioglu (Airwallex). They'll cover the trade-offs of self-service and how they rebuilt workflows now that everyone's a data person.
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Close out the conference at our Halloween Ball in the historic SF Mint basement. We'll have private access to the famous haunted house, so you can take a self-guided haunted house tour between drinks. Unwind after a full day, meet the speakers, and reconnect with people from your roundtables.
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