Atlan

We have news! 🎉 Gartner® has re-released its Magic Quadrant™ for Metadata Management Solutions after five years, and Atlan has been named a Leader. Why do we believe we are recognized now? Because metadata has become non-negotiable for AI. As Gartner puts it: “By 2027, the number of organizations adopting active metadata practices will increase by more than 75% across data, analytics, and AI to accelerate automation, insight discovery, and operational efficiency.” From our perspective, Atlan stands out because of four core strengths that matter deeply for AI-ready enterprises: 🔹 Breadth of context for AI: Atlan’s Metadata Lakehouse brings together business, technical, and operational metadata to power more accurate talk-to-data and agentic use cases. 🔹 Future-proof, open architecture: Enterprises choose Atlan because it’s extensible with open APIs, an App Framework, and Iceberg-native architecture designed to evolve with new data and AI ecosystems. 🔹 Adoption beyond technical teams: Atlan’s personalized, embedded user experience helps metadata finally reach analysts, business users, governance teams, and AI builders — driving adoption that traditional systems struggled to achieve. 🔹 Fast, automation-first time to value: Atlan’s automation, DIY connectors, and advisory-led onboarding help organizations see tangible outcomes significantly faster. This recognition isn’t just about Atlan. In our opinion, it validates every data team building AI’s context layer. Read the Gartner Magic Quadrant report → https://lnkd.in/daqk5BwS

Most governance tools make you choose: use their workflows, or build everything from scratch. There's a third way: making your platform work the way you work. On Nov 26, Omar Farah is doing a live walkthrough of how teams extend Atlan to fit their reality: → Use APIs and SDKs to create assets that match your data model  → Set up webhooks that keep metadata current without manual updates  → Build automated lineage for systems that don't have out-of-the-box connectors → Use no-code utilities when you need speed over customization If you're spending hours on manual metadata work that should take minutes, this demo's for you. Register now 👇

How do you govern AI when your data, metadata, and models live in different worlds? Austin Kronz ( Data and AI Strategy Atlan) is joining Anomalo, Databricks and Lovelytics to show how unified governance actually works in practice—not theory. 🗓️ November 20 | 8 AM PT / 11 AM ET / 4 PM GMT Join the conversation: https://lnkd.in/ggW33pZh

"It was actually a little bit embarrassing" Joe DosSantos and his team at Workday built an agent for recurring revenue—their most critical metric 📊 It couldn't answer basic questions from finance. So they started building: custom semantic layers, one use case at a time. That's when reality hit: you can't scale thousands of agents with thousands of custom translations. At #ReGovern2025, Joe shares what they learned—and why conversational analytics is about the infrastructure, not just the experience. Watch the full session → https://lnkd.in/gwaGjj4C

AI is fast. AI is confident. AI will give you an answer. Without context, AI is confidently wrong. Leaders from GitLab, Elastic, Dropbox, Vimeo, and Loopback Analytics tackled the question at #ReGovern2025: what's keeping conversational analytics pilots from reaching production? Here's what they had to say: Watch the full session 👇 https://lnkd.in/dSzdfpwk

 🎬 What a fun session of JeoporData hosted by Kate Strachnyi featuring our very own Prukalpa ⚡! Data meets game show, and we’re here for it. In this special edition, Kate and Prukalpa turned insights from #ReGovern2025 into a sharp, fast-paced round of JeoporData with categories like Mind the AI Chasm, Context or Chaos, and Governance Gone Wild. Turns out, governance questions can be far more engaging than they sound, especially when they spark real conversations on how teams at Workday, CME Group, and DigiKey are making AI work in practice. 👇 Watch JeoporData with Kate & Prukalpa below, and explore how the right context helps take AI from pilot to production.

Your data teams built amazing datasets. But nobody can find them. Business users don't know what's trusted. And every domain manages governance differently. The result? Your best data sits unused while people rebuild the same thing three times. On Nov 19, Bryan Myers (Solution Engineering Manager, Atlan) is showing how data teams are fixing this → turning datasets into organized, trustworthy products that anyone can discover and use. In this 45-minute live demo, you'll see: → Domain-centric data products built natively in Atlan → Federated governance that balances autonomy with alignment → A Data Product Marketplace where business users find ready-to-use, trusted data Plus live Q&A to get your questions answered. Register now 👇

98% of GM's cloud data is classified before it ever hits production. At Re:Govern 2025, Sherri Adame from General Motors shared their "shift left" approach → moving governance to the beginning of the data lifecycle instead of post-deployment. Their playbook: → Metadata flows in at code commit → 20 essential quality checks auto-deployed for AI readiness → Completeness scores & gamified enrichment "When I started three years ago, I was hunting people down for metadata," Sherri Adame shared. "Today, teams come to us. They know if you're working with data at GM, it's governed." Watch how GM built proactive governance at scale: https://lnkd.in/dT_cz_-F

⏩ Aligning your data org with AI-era demands? Here's how roles are shifting: Data modelers → Context engineers Data architects → Knowledge graph builders AI engineers → AI evangelists DigiKey's CDAO Sridher Arumugham has been preparing his team for the future and they've already launched 70 AI projects with this foundation. 💬 His philosophy: While AI automates the manual work, data teams must focus on building the semantic layer that teaches AI what data actually means. 🎥  Watch Sridher talk about how AI is changing data teams at #ReGovern2025 https://lnkd.in/d4riYbVn

🤖How to build AI Analysts that actually work in production. Your AI guesses business meaning. And guessing breaks trust. Through Atlan AI Labs, Shubham Bhargav (Product & Engineering at Atlan) and team worked with some of our most AI-forward customers like Workday to figure out what it actually takes to build AI Analysts that teams trust in production. The result? A 5x increase in response accuracy.📈 What it took: Instead of dumping metadata into prompts, we built a structured context layer: → Rich, queryable metadata (not static docs) → Domain-specific definitions (not generic glossaries) → Continuous validation loops (not one-time setup) The AI Analysts that work in production aren't just connected to data. They're grounded in meaning. Here's a full guide ➡️ https://lnkd.in/df2KhUZ3