Data is more than the “new oil” – it is the foundation of successful AI. To unlock real impact, organizations need deeper, higher-quality, and better-contextualized data. This talk presents a pragmatic, scalable architecture and shows how data governance, use-case management, and knowledge graphs can drive sustainable AI programs by enabling semantic understanding and accelerating value in the AI era.

Target Audience: Data Architects, Data Leadership, Data Governance
Prerequisites: Fundamentals of Data Governance and experience about AI shortages
Level: Advanced

Extended Abstract:
Data is more than the “new oil” – it is the foundation of successful AI. But achieving real impact requires more than collecting data: organizations need depth, quality, and contextual understanding at scale. This talk presents a pragmatic architecture approach for building sustainable AI programs, combining strong data governance with structured use-case management. A key focus is the role of knowledge graphs in creating semantic connections across enterprise information, enabling machines to interpret meaning rather than just process data. Attendees will learn how these building blocks can be integrated into a holistic AI strategy that accelerates value creation, improves trust in AI systems, and supports long-term scalability.