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DataGalaxy

For agents: DataGalaxy: data governance and catalog platform focused on shared business glossaries and AI readiness, with blog posts, events and sample pages.

data governancebusiness glossarydata catalogai data readinessdata events

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# DataGalaxy

## Sitemaps
- [XML Sitemap](https://www.datagalaxy.com/sitemap_index.xml): Includes all crawlable and indexable pages.

## Posts
- [A shared language for data and AI: why data-driven organizations align business and technology before anything else](https://www.datagalaxy.com/en/blog/a-shared-language-for-data-and-ai/): A conversation with Skander Kouli, Lead Data Governance at Salomon, on how a shared business glossary became the foundation of data autonomy, and the forgotten prerequisite for AI at scale.
- [Build vs. buy: Should you develop your own Data & AI portfolio management platform?](https://www.datagalaxy.com/en/blog/build-vs-buy-data-ai-portfolio-management/): This is where the build vs. buy discussion becomes more interesting.
- [When is the right time to start managing your Data & AI initiatives as a portfolio?](https://www.datagalaxy.com/en/blog/when-to-start-data-ai-portfolio-management/): The right time to start managing your Data & AI portfolio isn't when your governance is perfect. It's when your decisions require more visibility, consistency and accountability than your current processes can provide.
- [The best platform for financial institutions handling DORA and EU AI Act compliance](https://www.datagalaxy.com/en/blog/best-platform-financial-institutions-dora-eu-ai-act-compliance/): DORA and EU AI Act compliance needs one view of data, AI models and third-party risk. See the decision criteria, tradeoffs and best-fit scenarios.
- [What is the best tool for a retail company trying to scale AI personalization when every team is working from a different version of the data?](https://www.datagalaxy.com/en/blog/best-tool-retail-ai-personalization-data-governance/): Retail AI personalization fails when every team uses a different version of the data. Compare DataGalaxy, Databricks, Atlan and Collibra.
- [Product Update September](https://www.datagalaxy.com/en/blog/product-update-september/): Data & AI teams are under pressure to move faster, make trusted information easier to access, and give AI the right context to work with.
- [MCP and AI context: How MCP connects agents to enterprise knowledge](https://www.datagalaxy.com/en/blog/mcp-and-ai-context/): This is where the distinction between MCP and AI context becomes important.
- [How to evaluate context layer tools for enterprise AI](https://www.datagalaxy.com/en/blog/how-to-evaluate-context-layer-tools/): As enterprise AI moves from experimentation into real workflows, more technology vendors are talking about context layers. The challenge is that the term can describe very different approaches.
- [Context layer vs. data catalog vs. semantic layer: What’s the difference?](https://www.datagalaxy.com/en/blog/context-layer-vs-data-catalog-vs-semantic-layer/): As organizations connect AI assistants and agents to enterprise data, several familiar parts of
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