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What Is A Knowledge Graph? A Plain-Language Guide For Data And AI Leaders

A knowledge graph is a business model: an ontology of what things exist and how they connect, populated with your real, governed data. It's the fix for a specific, well-documented failure mode: Gartner predicts that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data.⁷ Usually, it isn't the model that's the problem — it's that the data underneath has no explicit structure for the model to reason over.

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Blog

Metadata vs. Context: What's the Difference and Why It Matters for AI

Most enterprises have spent the last decade building metadata infrastructure: data catalogs, glossaries, lineage graphs. Then they deployed AI agents on top of that investment and got confidently wrong answers anyway.

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Blog

What Is Intelligent Data Management? A Definitive Guide for the AI Era

Intelligent data management is the use of AI agents, active metadata, and automated governance to continuously catalog, contextualize, and act on enterprise data — keeping it accurate, trusted, and ready for both people and AI systems to use.

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The Alation Podcast Is Back: Introducing AI Radicals

Four seasons ago, we started a podcast to talk about data culture: how organizations actually change the way they work with data, one stubborn habit at a time.

Gartner Peer Insights Customer's choice Alation 2026 Metadata management data governance and analytics platforms

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Alation Named Customers' Choice in Two 2026 Gartner Voice of the Customer Reports

Alation has been named as a Customer's Choice in the 2026 Gartner Voice of the Customer for Metadata Management Solutions1 and Voice of the Customer for Data and Analytics Governance Platforms2, with a 99% willingness-to-recommend rate, the highest of any vendor in the Data and Analytics Governance Platforms market.

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What Is an AI Model Registry? How Enterprises Track, Version, and Govern Every AI Asset

Somewhere in your organization right now, a data scientist is fine-tuning a model. A business unit is piloting an LLM-powered assistant. A vendor's AI feature just got flipped on inside a SaaS tool nobody remembers approving. Multiply that across a mid-size enterprise and you get dozens, sometimes hundreds, of AI models and agents in flight — each with its own owner, its own training data, its own risk profile.

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Blog

The Data Infrastructure Checklist for AI: What Needs to Be in Place Before Your Models Go Live

AI-ready data infrastructure requires five things before models go live: a governed catalog, verified lineage, business context agents can actually reason over, agent-specific governance, and a feedback loop that catches drift after launch. Most checklists stop at the first two. This one doesn't — because most AI failures aren't model failures. They're infrastructure failures that surface long after the pilot looked like a success. That pattern shows up at the macro level too: MIT's Project NANDA found that 95% of enterprise generative AI pilots fail to deliver measurable P&L impact, and traced the gap to organizational and integration failure rather than model quality.⁹

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Alation vs. Microsoft Purview: Architecture, Pricing, and AI Readiness Compared

Most comparisons of Alation and Microsoft Purview start with a feature matrix: governance here, compliance there, a row of green checkmarks for each. That's useful if you're filling out a spreadsheet. It's much less useful if you're trying to answer the question that matters: when you put real data, real architecture, and real users in front of this tool, does it hold up?

Introducing AIOS from Alation

Blog

A New Vision for Alation: Introducing AIOS

For customers, prospects, and analysts wondering what changes to expect from us: this is the map.

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