Data Digest
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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?
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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.
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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
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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.
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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.
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5 Leading Data Enrichment Tools to Improve Data Quality
Data quality has a direct impact on business decisions, yet maintaining it remains a persistent challenge. When records are incomplete, inconsistent, or outdated, critical systems lose reliability and teams lose confidence. Experian’s latest Global Data Management Research found that 85% of businesses say poor-quality customer data harms operational efficiency—a reminder that weak data foundations affect both strategy and execution.
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What Is Automated Data Curation? How AI Is Changing Metadata Management
Every data governance program eventually hits the same wall. The catalog exists. The policies are written. Stewardship roles are assigned. And still, at the end of the quarter, half the tables in your production environment have empty description fields, PII classifications vary by team, and no one is quite sure whose job it is to keep all of it current.
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Data Dictionary vs. Business Glossary: Benefits, Use Cases, and Challenges
In your organization, are you ever confused by different definitions of business terms? Do you ever wonder why the number of customers differs between two reports?
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Blog
What Is Data Lineage?
Imagine trying to run your business with no idea where your data came from, how it’s changed, or whether you can trust it. As cloud stacks grow more complex and AI systems become more dependent on accurate inputs, the risk of decisions made on faulty data multiplies.
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Blog
Data Fabric vs Data Mesh: Differences and Decision Criteria for Data and AI Leaders
Here is the uncomfortable reality behind the enterprise AI surge: Per Gartner, at least 30% of generative AI projects were abandoned after proof of concept by the end of 2025, not because the models failed, but because the data underneath them did. The average enterprise scrapped 46% of AI pilots before they reached production, with the most common reason being that teams selected a use case before verifying that the data, governance, and infrastructure requirements were in place.
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