
Blog
In a paper mill, every hour of downtime carries a steep price tag, with tens of thousands of dollars lost the moment a machine goes offline. To protect against that risk, site managers do exactly what sound operational logic dictates: they maintain robust inventories of spare parts. It's a responsible decision made at the local level, repeated across numerous manufacturing sites nationwide.

Alation AI Labs
The hardest problem in enterprise AI is not giving agents context. It is keeping that context current as the business changes underneath it.

Blog
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.

Blog
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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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.

Blog
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.

Blog
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.

Blog
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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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

Blog
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?

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