Collate

Sriharsha Chintalapani explains why programming will never go out of fashion. https://buff.ly/I6wfOR3 #dataenigeering #software #openmetadata #dataquality #semanticintelligence

In this month's Product Demo, Dale Kim and James Nguyen showed how Collate, the enterprise platform built on OpenMetadata, takes you from "I need data" to "here's the data I need, who owns it, and why I can trust it." Your data teams can spend hours hunting for the right datasets, and even longer figuring out if they can trust what they find. You can't succeed with your data initiatives if you're constantly afraid of garbage-in-garbage-out. They covered: * A brief demo of data discovery and trust signals in Collate * Why discovery is only the start, and what else you need to make data usable * A quick walkthrough and discussion of Data Contracts in Collate 👉🎥Watch here: https://buff.ly/FocBXng #AI #datadiscovery #dataquality #datalineage #datagovernance #dataengineering #datastewards #dateacontracts

Profiling your data is a key part of data observability. This short video will walk you through the steps to configure and run a Profiler Agent. You'll also see what those results look like.

At 50 users and 5,000 tables, metadata management saves $2M+ annually. That's just the productivity floor. It doesn't include the cost of bad decisions made on wrong data. Or regulatory exposure. Or customer churn from data quality failures. Forrester found 25% of companies lose $5M+ annually from poor data quality. 7% lose $25M or more. MIT puts the cost of bad data at 15–25% of revenue. The ROI case for metadata has always been proven. AI just made it urgent - because unlike your analysts, AI agents don't pause when a number looks suspicious. They confidently return wrong answers at scale. We built the framework to help you make the case internally. 6 levers. Sized at 25, 50, and 250 users. Conservative assumptions. Download the whitepaper 👇 🔗 https://buff.ly/VoAdrmy #MetadataROI #DataGovernance #DataQuality #SemanticIntelligence #AI #OpenMetadata #DataStrategy #CDO #DataLeaders

34% — that's how much more likely AI models are to say "definitely," "certainly," and "without doubt" when generating incorrect information. Your data infrastructure isn't causing that. But if it's built on ambiguity, it's amplifying it. Data catalogs were designed for human analysts. When "revenue" meant three things across finance, sales, and marketing, the analyst knew who to call. AI agents don't call anyone. They pick a definition and return a confident answer. What your catalog gives an AI agent: 1. Which data assets exist and where they came from 2. Who owns them and whether they passed quality checks 3. Every table with "customer" in the name What it doesn't give: 4. What "customer" means to your business 5. Which definition of "revenue" applies to this query 6. How your core business concepts relate to each other That second list is why Gartner attributes 85% of AI project failures to poor data quality. https://lnkd.in/gjXyQFF7 co-founder Suresh Srinivas made the case at Data Summit Boston for what the missing layer looks like, and why the next generation of models won't solve it for you: https://buff.ly/137kQDE #AIAgents #DataGovernance #SemanticLayer #Ontology #ContextLayer

A data steward opened Ontology Explorer for the first time. Two minutes in, she spotted it: Three definitions of Revenue (Finance, Sales, Product) with zero formal relationships between them. Every version was being used downstream, feeding different answers into the same questions. She resolved it on the canvas: → Marked Finance as the authoritative definition → Declared equivalence to the Sales and Product versions → Tagged GDPR governance once at the concept level, which propagated automatically to all 847 implementing assets That's the difference between maintaining a flat glossary and governing an ontology. 🗺️ Collate 1.13 Ontology Explorer is your entire business vocabulary as an interactive graph, with term hierarchies, cross-domain connections, and a Data Mode that traces every term to the data that implements it. Ontology Explorer surfaces coverage gaps instantly. If your catalog has 34 glossary terms with zero relationships, opening the graph makes them visible immediately. Read the details: https://buff.ly/gFxJSi0 #Ontology #KnowledgeGraph #SemanticContext #DataGovernance #Collate

Hallucinations aren't a model problem. They're a context problem. When AI systems don't have access to the right data, the right tone, and the right constraints at the right time... they guess. Next Wednesday, our Co-Founder & CEO Suresh Srinivas is breaking down how leading teams are solving this, with context-aware architecture built for production. The Context Layer: The Emerging Stack for Context-Aware AI 📅 Wed May 13 | 11 AM–12 PM PDT 🎙 Data Science Connect Joining Suresh: Andreas Blumauer from Graphwise and Donald Spaulding from Verizon. Free to attend 👇 🔗 https://buff.ly/7qGVSx3 #ContextLayerAI #DataScienceConnect #SemanticIntelligence #AIAgents #GenerativeAI #DataGovernance #OpenMetadata #AIArchitecture

Meet Bonnie Xu, tech lead for Data Productivity at OpenAI. OpenAI's internal data platform spans tens of thousands of tables and hundreds of petabytes. Any employee can go from question to insight in minutes. Not because they hired more analysts. Because they built a data agent on OpenMetadata. At Collate Summit '26, Bonnie's keynote "Inside OpenAI's Internal AI Data Agent" covers the architecture, the context layers powering accurate answers, and how OpenAI scaled AI-assisted analytics across every function. 📅 June 10. Free. Virtual. Register: https://buff.ly/zRbLuhJ #CollateSummit #DataGovernance #AIAgents #ContextLayer #OpenMetadata

Pick any table in your catalog. Try listing, in under a minute, every team that depends on it, every glossary term it maps to, and everything that breaks if the schema changes. Most people can't. That's why we built the new Knowledge Graph visualization. 🔮 Collate 1.13 renders the full relationship context of any asset in a single interactive view: → Ownership, teams, and domain context → Schema hierarchy, upstream sources, and downstream consumers → Business semantics, plus relationships inferred automatically from your metadata Built on open W3C standards (RDF, OWL, DCAT, DPROD, SKOS, PROV-O, Schema.org) for portability and interoperability. Every AI agent that queries your data, including those through MCP, reasons from the same semantic context graph of ownership, lineage, and glossaries. See your entire data ecosystem in one view instead of digging through spreadsheets and Slack threads. Read the details: https://buff.ly/FT3Qtzm #KnowledgeGraph #SemanticContext #DataGovernance #AIAgents #Collate

Suresh Srinivas just got off the Data Summit stage, where he made the case that AI doesn't fail because of bad models. It fails because the data underneath has no shared meaning. Tonight? An evening of exactly that conversation. Metadata, governance, and making AI actually work in production at the Collate + OpenMetadata Boston meetup. Good conversations don't stop when the conference ends. 🎟 Still time to join us: luma.com/ewm14xmh #DBTA #DataSummit2026 #OpenMetadata #SemanticIntelligence #DataGovernance #AI #Boston #DataEngineering #DataCommunity