Three out of four companies are betting big on AI – but most are digging on shifting ground. In this $100 billion gold rush, none of these investments will pay off without data quality and strong governance – and that remains a challenge for many organizations. Not every enterprise has a solid data governance practice and maturity models vary widely. As a result, investments in innovation initiatives are at risk of failure. What are the most important data management issues to prioritize? See how your organization measures up and get ahead of the curve with Actian.

When done right, governance is a growth engine. In this talk, Jean-Georges “jgp” Perrin will show how data contracts bring precision, trust, and accountability into your data and AI pipelines—without creating bottlenecks. Using the Open Data Contract Standard (ODCS) from the Linux Foundation’s Bitol project, you’ll see how organizations can cut downstream defects, accelerate AI model onboarding, lower compliance risk, and reduce firefighting—often in just days.

O'Reilly Author and Chief Evangelist, PhD Ole Olesen-Bagneux takes a deep dive into the challenges of metadata management in enterprises, and the great potential metadata represents for Artificial Intelligence. 

The reality of metadata management is – crucially – not properly addressed in most tech literature, as well as in the guidance from technology vendors. This is not a result of suspicious intentions, but a natural outcome of what is sought communicated: How technology works. 

However, this leaves out the enterprise context, and accordingly implementations of technologies suffer. For metadata, this is a problem that limits the potential and interplay of the many metadata repositories normally found in an enterprise. 

A great perspective unfolds if we consider metadata repositories more holistically as a stack, giving improved perception of what the IT landscape of an enterprise is truly like. 

Furthermore, this approach solidifies how to craft new metamodels in knowledge graphs, because they meticulously consider the existing mappings of the IT landscape. 

This is the key to unparalleled solid context for Artificial intelligence. 

Discover:

*The reality of metadata management in enterprises

* Agentic AI for the enterprise

* Ontologies for Model Context Protocol (MCP)

Three out of four companies are betting big on AI – but most are digging on shifting ground. In this $100 billion gold rush, none of these investments will pay off without data quality and strong governance – and that remains a challenge for many organizations. Not every enterprise has a solid data governance practice and maturity models vary widely. As a result, investments in innovation initiatives are at risk of failure. What are the most important data management issues to prioritize? See how your organization measures up and get ahead of the curve with Actian.