Euno

No need for Euno's context on this one.

Sunday metadata thoughts 💭 Context consumption by AI is redefining the data landscape. TL;DR →  As AI becomes the primary interface to data, context becomes the runtime, and every interaction depends on it. Unlike traditional data tools priced per seat or per asset, the emerging context layer is consumed per AI interaction. Each interaction, whether answering a business question, modifying a pipeline, triggering a workflow, or validating access to sensitive data, requires: → Traversing lineage across systems → Resolving business meaning and definitions → Incorporating usage and ownership signals → Enforcing governance and policy constraints As a result, the unit of value shifts to context resolution per interaction. And as AI adoption matures, interactions move from user-driven queries to agent-driven systems operating directly on enterprise data platforms. At the same time, the scale of agents continuously interacting with enterprise data systems is driving a shift in value away from applications and tooling, toward the context layer that makes these AI-driven workflows accurate and explainable. For the first time, context is part of the execution path, not just a supporting layer on the side! Think hundreds of agents operating across analytics, engineering, and security. Each executing thousands of interactions per day against core data platforms, like Snowflake and Databricks, powering continuous monitoring and automated workflows. This quickly compounds into millions of AI-driven data interactions across the enterprise each year. This is not a new category of activity. It is existing data usage, now executed by systems instead of people, at far greater speed and scale. That shift is what ultimately drives the next leap in organizational productivity.

𝐄𝐮𝐧𝐨 𝐰𝐡𝐚𝐭 𝐭𝐨 𝐝𝐨! ✨ #GartnerDA London won’t stay enchanted forever… catch us before it disappears into the mist 💜🦄🪽

A quick note as we kick off Gartner D&A Summit in London. There’s a lot of focus right now from CDOs, CIOs, and CAIOs on one thing: realizing quantifiable value from enterprise AI. As Gartner aptly framed it, the AI value gap is the space between "blue money" (potential value) and "green money" (realized value). At the Euno booth: My team and I will be showcasing real examples of how enterprises are using Euno’s context to enable AI systems to act reliably on enterprise data and boost business productivity – with measurable ROI that stakeholders can actually track end to end. A few examples: 1. 𝐃𝐞𝐩𝐥𝐨𝐲𝐢𝐧𝐠 𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐀𝐈 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐢𝐧 𝐚 𝐟𝐞𝐰 𝐰𝐞𝐞𝐤𝐬 Giving business users trusted access to data, with full explainability, dramatically boosting time-to-insight, and eliminating the days or even weeks often spent waiting on analysis. 2. 𝐑𝐞𝐝𝐮𝐜𝐢𝐧𝐠 𝐦𝐚𝐧𝐮𝐚𝐥 𝐝𝐚𝐭𝐚 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐨𝐯𝐞𝐫𝐡𝐞𝐚𝐝 𝐛𝐲 50-200𝐱 Saving operational teams hundreds of hours each month by automating change impact detection, conflict resolution, and data certification, while preventing breaking changes from coding agents, and accelerating pipeline jobs with autonomous agents. 3. 𝐋𝐨𝐰𝐞𝐫𝐢𝐧𝐠 𝐬𝐞𝐧𝐬𝐢𝐭𝐢𝐯𝐞 𝐝𝐚𝐭𝐚 𝐞𝐱𝐩𝐨𝐬𝐮𝐫𝐞 𝐫𝐢𝐬𝐤 𝐟𝐫𝐨𝐦 𝐀𝐈 𝐛𝐲 50% Automatically detecting when PII becomes exposed to unauthorized users through reports, dashboards, or AI agents. Then, tracing the source and scope of exposure in real time. And of course, there’s another factor quickly becoming inseparable from enterprise AI investment decisions: 𝐭𝐨𝐤𝐞𝐧 𝐬𝐚𝐯𝐢𝐧𝐠𝐬. Euno’s continuous, automated reconciliation of context across the entire data and AI stack delivers about 10-20x better token efficiency compared to agents independently querying every MCP server of every disconnected data tool. *** I’m looking forward to hearing Gartner’s perspectives throughout the summit, and also to sharing what we believe is one of the fastest and most cost-effective ways to realize enterprise AI value with Euno. Catch us at booth 109, or DM me if you’d like to grab coffee while we’re there.

Euno we’ll be there! 3 reasons to stop by our booth at Gartner's Data & Analytics Summit in London next week: 1. Enterprises are stuck between AI ambition and data chaos. Enterprise AI breaks at scale because agents don’t understand the data they act on: how it flows, what it means, who owns it, whether it’s trusted, or what rules govern it. Euno fixes that. 2. We connect with your entire data & AI stack. From warehouses and BI to quality tools and DSPMs, Euno automatically builds a complete, unified context graph covering lineage, semantics, usage, access, sensitivity, institutional knowledge, governance, and more. That full context gets surfaced at runtime to any AI agent. 3. Real use cases. See how data teams leverage Euno's context to deploy scalable AI analytics in weeks, retire data catalogs for AI-first governance, prevent breaking changes by coding agents, and reduce the risk of sensitive data exposure by AI. Bonus: The swag. 📍 Booth #109, Data Governance Village  Or lock in time with our team: https://lnkd.in/dbCAu2TK

Thought we’d add context to the meaning behind every red carpet look.

Sunday metadata thoughts 💭 Your teams don’t ask if AI works on enterprise data, they expect it to. We’ve already seen AI unlock massive gains in personal productivity and narrow business workflows. That part is real. But when it comes to connecting AI to the core of the enterprise, the data platforms, the story breaks down. Not because the models aren’t good enough. Because the context isn’t. Enterprise data environments were never designed for AI. They’re complex, fragmented, and constantly evolving. Data lives everywhere, definitions drift, ownership is unclear, and trust is uneven. So when agents try to find and query the right data, they struggle. They need context: what exists where, how it’s used, and whether it can be trusted. But that context isn’t in one place. It’s inherently federated across your entire stack: warehouses, catalogs, BI tools, pipelines, quality systems, security platforms, and tribal knowledge. That’s the real bottleneck. The organizations pulling ahead are the ones that recognize this shift: where context exists, AI works. And the wider the context, the more you can trust it.

Off to #TC26! Here’s why you should meet us: With Euno, enterprises using Tableau on top of data platforms like Snowflake and Databricks can leverage all of the logic built in Tableau when connecting AI agents like Claude or Tableau Next to their data. → Tableau’s intelligence is captured in the automated, column-level lineage Euno generates and becomes part of the full context stack of your data ecosystem. → That context is then surfaced at runtime to agents, enabling safe, accurate, and fully explainable results. This is a big step toward deploying AI workflows not just across analytics, but also across data engineering and governance. Your teams expect AI to just work on enterprise data. Let's help them create more value from what you already have in Tableau, and use it to power enterprise AI... at the speed AI demands. *** Book time with me at the Euno lounge, or come say hi after my theater session on Wed. Links in the first comment.

Why the enterprise context layer is having its moment. You’ve read this before: as enterprises become AI-first, the interface to data is shifting from human-driven tools and workflows to AI agents. No wonder Salesforce recently announced Headless 360, enabling autonomous agents like Claude and Copilot directly access data and logic to perform tasks without human interaction. These agents can operate across analytics, engineering, and security workflows. And every interaction requires context to deliver reliable, explainable outputs.  This creates a new infrastructure layer → The enterprise context layer, responsible for maintaining and providing relevant context at runtime. What I’d like to emphasize is why this layer matters now more than ever. Historically, layers like data catalogs and semantic layers didn’t capture significant value because they sat outside the execution path, relying on humans to interpret and apply context. As AI becomes the interface for data exploration, agents cannot rely on tribal knowledge. They need context drawn from across the entire stack, machine-readable and surfaced at runtime. This moves the context layer directly into the execution path of every interaction, transforming it from documentation into critical infrastructure. Euno is building this layer.