Re:Govern 2025: The Data & AI Context Summit Recap
A look inside Atlan’s largest customer conference yet — where the world’s most AI-forward data teams came together to share how they’re evolving to make AI work, responsibly and at scale.
Last Updated on: November 05th, 2025 | 10 min read
Every data team today is facing the same challenge — figuring out what it really means to be AI-ready. The pressure to operationalize AI is high, but the playbooks are still being written.
That’s what made Re:Govern 2025 so special. It wasn’t about polished frameworks or one-size-fits-all roadmaps. It was about openness — leaders and practitioners coming together to share the messy, real, and inspiring work of evolving their teams, tools, and processes for the AI era.
Atlan’s largest customer conference yet brought together voices from Mastercard, Workday, CME Group, DigiKey, Marriott, VMO2, Dropbox, GitLab, Elastic, and more — packing an incredible amount of real-world insight and experience into one place.
Source: Atlan.
At the heart of every story was a single idea: context is king. In a world chasing models and metrics, the missing link is meaning — the shared context that allows humans and AI to understand, trust, and act on data responsibly.
This is what we call the Context Layer — the foundation that gives AI systems the context they need to reason, explain, and perform reliably.
Read the Context Layer Primer →
Re:Govern also marked the launch of the Modern Data & AI Governance Blueprint — a practical guide built from the real-world experiences of leading teams. It distills how the top 5% of organizations are evolving their governance models for the age of AI, and how anyone can start that journey today.
Opening Keynote: Context as the Foundation of AI #
The Re:Govern 2025 keynote opened with six leaders — from Workday, Mastercard, CME Group, DigiKey, Marriott, and VMO2 — each showing how context has become the foundation of trustworthy, scalable AI.
Workday: Context as Culture #
Joe DosSantos, VP of Enterprise Data and Analytics at Workday, described how his team built the “perfect data strategy” — only to realize it was perfect for a world that no longer existed. As AI entered the picture, they discovered that their beautifully governed data wasn’t machine-readable enough for AI to interpret or trust.
“Our beautiful governed data, while great for humans, isn’t particularly digestible for an AI.”
Workday’s response was to evolve governance into semantics — building a machine-readable layer of meaning that connects business logic directly to AI. As Joe put it, “In the future, our job will not just be to govern data. It will be to teach AI how to interact with it.”
Watch → Workday: Context as Culture
Mastercard: Context by Design #
Source: Atlan.
Andrew Reiskind, Chief Data Officer at Mastercard, shared how context is mission-critical in a business where every tap and swipe must be understood instantly and accurately.
“We’ve moved from privacy by design to data by design to now context by design.”
He spoke about embedding contextual data — the “tribal knowledge” of transactions — directly into Mastercard’s systems so AI can interpret events in real time. For Reiskind, context isn’t an add-on; it’s built into every product and process. “You can’t bolt on context later,” he said. “You have to build it in from the start.”
Watch → Mastercard: Context by Design
CME Group: Context at Speed #
Source: Atlan.
At CME Group, the world’s largest derivates exchange, Kiran Panja, Managing Director, showed how enriching data with context enables real-time decision-making without sacrificing speed or compliance.
“The thing slowing us down wasn’t how quickly we could move data — it was how quickly we could enrich it with the right context.”
By partnering with Atlan, CME unified metadata across systems and scaled to 18 million assets — allowing AI-driven analytics to operate at market speed. “For CME,” Kiran said, “context isn’t a back-office function. It’s the foundation for customer trust, market integrity, and future growth.”
Watch → CME Group: Context at Speed
DigiKey: Context Readiness #
Sridher Arumugham, Chief Data & Analytics Officer at DigiKey, reframed AI readiness as context readiness. When global supply chains broke during the pandemic, it wasn’t a lack of data that slowed them down — it was the lack of shared meaning.
“The path to AI readiness goes through context readiness. It is the greatest accelerator of the AI journey.”
By unifying context across six critical systems and over a million assets, DigiKey turned scattered information into a shared, living language that powers predictive decisions across their global operations.
Watch → DigiKey: Context Readiness
Marriott: Context Built to Change #
At Marriott International, Julia Morrison, VP of Data and Personalization, shared how the company is weaving context into its global transformation — spanning 9,600 properties and billions of data records.
“Old technology was built to last. We’re learning to build for change.”
For Marriott, context connects people and data — ensuring that every associate, franchisee, and hotel owner speaks a shared language when using data to personalize guest experiences.
Watch → Marriott: Context Built to Change
VMO2: Context for All #
Source: Atlan.
Mauro Flores, EVP of Data Democratisation at Virgin Media O2, closed the keynote with a story of scale — bringing 16,000 employees and 45 million customer connections together through a culture of context and collaboration.
“You have to have a great product that people trust, that they can consume and engage with — and you have to make sure more people are engaging with more products.”
By pairing democratization with governance, VMO2 turned self-service from chaos into confidence — enabling AI and analytics adoption at enterprise scale.
Across every story, one truth emerged: AI doesn’t just need data. It needs context. When humans and AI share the same understanding of data, trust and innovation can finally move at the same speed.
Watch → VMO2: Context for All
Deep Dives: Building the Foundations for AI Readiness #
Next, we moved into real stories from the teams at Mastercard, General Motors, and CME Group + Deutsche Börse Group and explored how some of the world’s most established enterprises are laying the groundwork for AI that actually works.
Top Down or Bottom Up: The World’s Largest Exchanges Show Two Paths to AI #
In one of Re:Govern’s most tactical sessions, leaders from CME Group and Deutsche Börse Group revealed two distinct but equally powerful approaches to AI readiness.
At CME, Zenul Pomal, Executive Director of Core Data Platform, shared how the 175-year-old exchange rebuilt its ecosystem after early “data swamps,” standardizing ownership and automating lineage across 26 petabytes of data. “We wanted teams to access data in a consistent and secure way — no matter where it lived or what tools they were using,” he said.
Source: Atlan.
At Deutsche Börse Group, Shraddha Sharma and Mira Boteva outlined a federated governance model balancing local freedom with global consistency. “Our framework cannot be a central model,” Shraddha noted. “It has to be federated — combining group-wide policies with domain-specific ownership.” Their approach empowers each business to govern locally while uniting the group through shared principles, councils, and a central catalog built with Atlan.
How Mastercard Is Building AI-Ready Data Products #
For Mastercard, governance only works when enablement and automation coexist. Vivek Radhakrishnan, SVP, Data Governance Engineering, and Fabien Thiaucourt, SVP, Data Governance & Enablement, shared how their teams are combining engineering precision with human context — automating lineage and quality while empowering stewards and local experts to add meaning.
“We didn’t start with tools,” Vivek said. “We started with outcomes — what should it feel like to use data at Mastercard?” The result: governance that scales globally, accelerates AI adoption, and turns trusted data into a true business enabler.
Contracts, Context, and Cars: General Motors’ Governance Blueprint for the AI Era #
Source: Atlan.
America’s largest and oldest automaker is turning a new kind of curve — one powered by data and AI. Sherri Adame, Enterprise Data Governance Leader at General Motors, shared how GM is reimagining governance as a system of contracts and context, not controls. By treating every dataset like an agreement between producers and consumers, GM is embedding trust and accountability into the fabric of its operations. Engineering and governance teams now work side by side to ensure meaning, quality, and lineage travel with every dataset — from the factory floor to the AI models shaping the future of mobility.
Roundtables: The Unfiltered Conversations #
Then, we went into Roundtable discussions, where we were joined by leaders from EasyJet, Nasdaq, Invitation Homes, Group 1001, New York Life, Mercury Insurance, Dropbox, Elastic, GitLab, Loopback Analytics, and Vimeo.
Across four powerful conversations — ROI Before AI, Evolution of Trust, Hidden Strategies Behind AI-Ready Tech Pioneers, and AI Governance in Regulated Industries — data and AI leaders got candid about what it really takes to drive value, earn trust, and redesign their operating models for the AI era.
ROI Before AI: How Leaders Make the Business Bet on Foundations #
Leaders from Nasdaq, EasyJet, and Invitation Homes shared how they’re proving the business value of governance long before AI outcomes appear. Mike Weiss of Nasdaq described how his team quantified “time to trust” — cutting resolution time for data issues from ten hours to two, and measuring adoption to show governance as an operational accelerator, not overhead. “You can bring the best tools in the world,” he said, “but if no one adopts them, it’s irrelevant.”
Chris Durham of Invitation Homes and Shiv Nayak of EasyJet echoed that governance success depends on sequencing and focus — choosing simple, high-impact domains, proving measurable wins, and scaling from there. From EasyJet’s “single customer view” to Invitation Homes’ certified KPI glossary, each story reinforced the same truth: AI ROI starts with governance ROI.
Source: Atlan.
The Evolution of Trust: Building the Foundations for Future-Readiness #
Leaders from Group 1001, New York Life, and Mercury Insurance revealed how regulated industries are future-proofing their data foundations for AI.
Gu Xie of Group 1001 described a dual mandate — protecting sensitive data while automating safe access through AI-powered classification and anonymization. Ashish Bisht shared how Mercury is creating PII-free, high-quality workspaces to make model governance simpler and faster. Meanwhile, Rahul Bakhshi of New York Life emphasized tying every governance initiative to business outcomes — turning compliance into a catalyst for innovation.
Hidden Strategies Behind AI-Ready Tech Pioneers #
Source: Atlan.
In one of Re:Govern’s most future-facing sessions, leaders from Dropbox, Elastic, GitLab, Loopback Analytics, and Vimeo revealed their AI readiness strategies:
- AJ Pryor, Vimeo: Built a metadata layer that distinguishes verified insights from experiments — turning governance into the trust engine for conversational AI.
- Amie Bright, GitLab: Unified structured, unstructured, and semantic data — treating context as the bridge between BI and AI.
- Cortney Worthy, Dropbox: Embedded governance directly into creation with data contracts and automated monitoring, making compliance effortless.
- Simon King, Loopback Analytics: Transformed scattered tribal knowledge into machine-readable context — enabling safe, explainable AI in healthcare.
- Takashi Ueki, Elastic: Balanced AI ambition with readiness — scaling data quality and context while keeping usability front and center.
Building AI That Works — Together #
In an era of unprecedented change, it’s hard to know if you’re doing something right — but it’s easier when you’re not doing it alone. That spirit defined Re:Govern 2025. From global banks and insurers to automakers and tech pioneers, every leader on stage shared not a playbook, but a perspective — an unfiltered look at how they’re navigating the messy, human side of AI readiness.
Across every story, one truth stood out: AI readiness is about context. It’s built in the quiet work of defining meaning and documenting context. And while no one has it all figured out, the teams at Re:Govern showed what’s possible when a community comes together — learning, experimenting, and evolving side by side.
👉 Revisit their stories, watch the sessions, and join the community at the Re:Govern Watch Center.
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