Data Digest
Blog
The $25 Million Reason Georgia-Pacific Rebuilt Its Data Foundation
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.
Read now
Alation AI Labs
Your Context Layer Is Already Wrong
The hardest problem in enterprise AI is not giving agents context. It is keeping that context current as the business changes underneath it.
Read now
Filter by
Blog
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.
Read now
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
Read now
Blog
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?
Read now
Blog
A New Vision for Alation: Introducing AIOS
For customers, prospects, and analysts wondering what changes to expect from us: this is the map.
Read now
Blog
Databricks Genie vs. Alation: Why the Best AI Answers Need Both
If you're evaluating Databricks Genie alongside Alation, you're probably asking the same question most enterprise data teams ask: We already have Genie… do we really need both?
Read now
Blog
How Telecom and Mining Companies Make AI Work in Production
Recap of "AI Pilots Are Easy. These Two Leaders Figured Out the Hard Part." — Gartner Data & Analytics Summit, Sydney, June 2026
Read now
Blog
How To Comply With The EU AI Act: A Practical Guide
If a regulator or your board asked today which AI systems you have in production, which EU AI Act obligations apply to each, and whether the evidence is complete... how long would it take your team to answer?
Read now
Blog
What Is Data Lineage for AI? How Tracing Data Origins Improves Model Accuracy and Trust
Enterprises have deployed AI almost everywhere, yet most have little to show for it. McKinsey calls it the gen-AI paradox: nearly 80% of companies have deployed generative AI, but over 80% report no material impact on earnings. The bottleneck is rarely the model. It is the data foundation underneath it: disconnected, inconsistent, and poorly understood.
Read now
Blog
What Is Data Governance in Banking?
Banks manage a lot of sensitive data. When an individual opens an account, they provide information that needs protection, from name and address to social security number. The collection doesn’t stop there — insights like transactions and purchasing information help to round out customer profiles. With this data, financial institutions can improve services and make informed decisions – if they can use it safely.
Read now
1 of 23