TopQuadrant

Most enterprises don't need to 𝙨𝙩𝙖𝙧𝙩 𝗮 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗹𝗮𝘆𝗲𝗿. They need to recognize they've been 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐨𝐧𝐞 𝐟𝐨𝐫 𝐲𝐞𝐚𝐫𝐬. The business glossary your 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝘁𝗲𝗮𝗺 maintains. The reference data your 𝗠𝗗𝗠 owns. The taxonomy buried in your 𝗰𝗮𝘁𝗮𝗹𝗼𝗴. The lineage your 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝘁𝗲𝗮𝗺 tracks. The policies your 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 𝘁𝗲𝗮𝗺 writes. 𝗧𝗵𝗲𝘀𝗲 𝗮𝗿𝗲𝗻'𝘁 𝗱𝗶𝘀𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗲𝗱 𝗮𝗿𝘁𝗶𝗳𝗮𝗰𝘁𝘀. They're the early stages of an enterprise Context Layer — the layer that tells your AI systems what your business actually means by a customer, a transaction, or a risk. 𝗪𝗵𝗮𝘁'𝘀 𝘂𝘀𝘂𝗮𝗹𝗹𝘆 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗶𝘀𝗻'𝘁 𝗺𝗼𝗿𝗲 𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗮𝘀𝘀𝗲𝘁𝘀. 𝗜𝘁'𝘀 𝘁𝗵𝗲 𝘀𝗰𝗮𝗳𝗳𝗼𝗹𝗱𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝘁𝘂𝗿𝗻𝘀 𝘁𝗵𝗲𝗺 𝗶𝗻𝘁𝗼 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲:: → One authoritative source of meaning across the enterprise → Clear ownership and stewardship for context itself → Constraints and validation that make context trustworthy enough for AI to act on → Operational integration into the catalogs, models, and agents already in your stack Join us 𝗧𝗵𝘂𝗿𝘀𝗱𝗮𝘆, 𝗠𝗮𝘆 𝟮𝟴 𝗮𝘁 𝟭𝟮:𝟯𝟬 𝗣𝗠 𝗘𝗧 as Amar Doshi and Steve Hedden walk through the context maturity curve — and where most organizations actually sit on it. 🔗 𝗦𝗮𝘃𝗲 𝘆𝗼𝘂𝗿 𝘀𝗲𝗮𝘁: https://hubs.li/Q04hklwm0 #ContextLayer #DataGovernance #EnterpriseAI #SemanticLayer #KnowledgeGraph

"You don't have a knowledge problem. You have a context problem." Our Head of Product Amar Doshi will be stage today at the The Knowledge Graph Conference unpacking why this framing is resonating with business leaders in a way that "knowledge graph" or "semantic layer" often hasn't — and what it means for organizations trying to make AI actually useful. Here's the surprise for most stakeholders: the foundation is already there. Knowledge graphs, ontologies, taxonomies, metadata, reference data — most enterprises have invested in these pieces. The gap isn't technical. It's that these assets remain siloed, loosely governed, and disconnected from the systems that need them most. The result? AI initiatives that stall. Search that disappoints. Data products that don't deliver. The real opportunity isn't building more models. It's turning what you already have into authoritative context — governed, trusted, and usable across teams, systems, and AI applications. In today's session, we're showing how leading organizations are making that shift: → How governed taxonomies and ontologies become the backbone for AI and data products → How constraints like SHACL introduce the guardrails needed for consistency and explainability → How semantic assets move from isolated projects to shared, enterprise-wide infrastructure The message we want every data and AI leader to walk away with: governance — not modeling — is usually what's blocking progress. And the path to a scalable Context Layer that works for both humans and AI is more achievable than most teams realize. You're closer than you think. #KGC2026 #KnowledgeGraph #Context

Excited to see our CEO Nimit Mehta take the stage at #EDW2026 with a data governance talk that couldn't be more timely: 🎤 "Own Your Context: Interoperable, AI-Ready Data Foundations for Regulated Enterprises" As AI moves from pilots to production, regulated enterprises are running into the same wall: their data isn't ready. Fragmented systems, missing context, and brittle integrations make it nearly impossible to deploy AI responsibly — let alone defensibly to a regulator. We will dig into what it actually takes to build a data foundation that's: ✅ Interoperable across systems and standards ✅ AI-ready, with the context models need to reason reliably ✅ Governed in a way that holds up under scrutiny If you're navigating AI governance, data strategy, or compliance in financial services, healthcare, life sciences, or any other regulated industry, this is a session worth blocking on your calendar - https://hubs.li/Q04fnMCm0 Tuesday, May 5 at 2:45pm ET. See you at EDW! 👋 #Context #DataGovernance # DataQuality #AI-ready

Big things are happening at TopQuadrant. Our customer base is growing and our team is expanding. If you're passionate about building strong data foundations that can scale with AI, we want to hear from you. Explore open positions: https://hubs.li/Q04cTsBd0 #hiring #teamgrowth #builders #datafoundation #enterpriseAI

There's a reason your data doesn't agree with itself. As humans, we reconcile the discrepancies. But AI can't. And when it comes to authoritative references, you don't want it to...without a shared language. When business meaning isn't shared, AI doesn't resolve the inconsistency — it amplifies it. There's a layer missing in most enterprise data architectures. It's the one that holds everything else together: Context. Join TopQuadrant's next webinar to learn why AI needs a decision-grade Context Layer. 📅 Your Data Doesn't Agree With Itself: Why AI Needs Authoritative Context 🗓 Thursday, April 16 | 12:30 PM ET Register here → https://lnkd.in/e4Wdb8gp #Context #DataGovernance #AIReadiness #DataArchitecture

Failed AI initiatives are often diagnosed as model problems or data volume problems. In reality, they're frequently reference data problems — the absence of a shared, authoritative meaning layer that AI can reason from reliably. MDM masters your entities. But who's governing the classifications, identifiers, and controlled vocabularies that give those entities their meaning? That's the gap. And it's the one most data strategies skip entirely. New blog from TopQuadrant → https://hubs.li/Q04byDTC0 #AIReadiness #ReferenceData #DataGovernance #Context

Your AI initiative has a foundation problem. And it's not the model. It's that your data means different things in different systems. The same product code. The same risk classification. The same customer status — interpreted differently across every team, tool, and platform. Your people compensate with spreadsheets and manual fixes. Your AI can't. When business meaning isn't shared, analytics mislead, pipelines break, and AI amplifies the inconsistency instead of resolving it. There's a layer missing in most enterprise data architectures — and it's the one that holds everything else together. Join TopQuadrant's Head of Product Amar Doshi and solutions expert John M. on April 16 to learn what that layer is, why it's missing from most data stacks, and what it takes to put it in place — without replacing your existing systems. 📅 Your Data Doesn't Agree With Itself: Why AI Needs Authoritative Context 🗓 Thursday, April 16 | 12:30 PM ET Register here → https://hubs.li/Q04b4--l0 #Context #DataGovernance #AIReadiness #DataArchitecture

"Earthset" on April 6, 2026. From the iconic Earthrise of Apollo 8 — 58 years ago — to this stunning new view captured by the Artemis II crew on Monday as they swung around the far side of the Moon. A new crew. A familiar awe. NASA was among the earliest adopters of knowledge graph technology, and it's hard not to think of that pioneering spirit watching moments like this unfold. Rooting for Reid Wiseman, Victor Glover, Christina Koch, and Jeremy Hansen as they make their way home. 🌍🚀 #ArtemisII #Earthrise #NASA #KnowledgeGraph

Context is King. 👑 That was the message from the Gartner Data & Analytics Summit — not just in breakout sessions, but on the main stage of the opening keynote. Gartner's guidance to enterprise leaders was clear: make context your next critical infrastructure investment. Yet one question left most attendees stumped: "How does your system establish ground truth?" Most organizations know they need context to establish truths. Very few have built a reliable system for activating it. And...it's the missing link to successful AI deployments. We captured our full takeaways in a new blog post 👇 https://hubs.li/Q047ns8P0 #GartnerDA #EnterpriseAI #ContextLayer #TopQuadrant

Heading to Gartner Data & Analytics in Orlando next week? Connect with our Chief Product Officer Amar Doshi about building your #ContextLayer for AI. #GartnerDA #Context #AI