As enterprises scale their deployment of Generative AI (Gen AI), a central constraint has come into focus: while large language models and the infrastructure to support them are the focus of intensive investment fueling a remarkable stream of innovation, data management approaches and infrastructure depend on out-of-date assumptions that are stopping progress.
Existing platforms, optimized for human interpretation and batch-oriented analytics, are misaligned with the operational realities of autonomous agents that consume, reason over, and act upon data continuously at machine scale.
In this talk, Zhamak Dehghani — originator of the Data Mesh and a leading advocate for decentralized data architectures — presents a framework for data infrastructure designed explicitly for the AI-native era. She identifies the foundational capabilities required by Gen AI applications: embedded semantics, runtime computational policy enforcement, agent-centric, context-driven discovery.
Speaker
Dr. Jörg Schad
Head of Engineering @Nextdata
Dr. Jörg Schad has been working on the intersection of data management, databases, and machine learning. He is currently focused on operationalizing decentralized data management systems using Data Mesh. In his previous life, he enjoyed working with graph databases, analytics, and machine learning as CTO at ArangoDB, building data and machine learning infrastructure in healthcare at Suki AI and Mesosphere, and designing in-memory databases with SAP. Jörg obtained a Ph.D. in distributed databases and data analytics and enjoys discussing the latest trends in databases and management.
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