Zhamak Dehghani

As enterprises scale their deployment of Generative AI (Gen AI), a central constraint has come into focus: the primary limitation is no longer model capability, but data infrastructure. 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. The session contrasts the architectural demands of AI with the limitations of today’s fragmented, pipeline-driven systems—systems that rely heavily on human intervention and customized orchestration. Dehghani introduces autonomous data products as the next evolution: self-contained, self-governing services that continuously sense and respond to their environment. She offers an architectural deep dive and showcases their power with real-world use cases. Attendees will learn the architecture of “Data 3.0”, and how to both use GenAI to transform to this new architecture, and how this new architecture serves GenAI agents at scale.

It’s now over six years since the emergence of the paper "How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh” by Zhamak Dehghani that had a major impact on the data and analytics industry. It highlighted major data architecture failures and called for a rethink in data architecture and in data provisioning by creating a data supply chain and democratising data engineering to enable business domain-oriented creation of reusable data products to make data products available as self-governing services. Since then, we have seen many companies adopt Data Mesh strategies, and the repositioning of some software products as well as the emergence of new ones to emphasize democratisation. But is what has happened since totally addressing the problems that Data Mesh was intending to solve? And what new problems are arising as organizations try to make data safely available to AI projects at machine-scale? In this unmissable session Big Data LDN Chair Mike Ferguson sits down with Zhamak Dehghani to talk about what has happened since Data Mesh emerged. It will look at: ● The drivers behind Data Mesh ● Revisiting Data Mesh to clarify on what a data product is and what Data Mesh is intending to solve ● Did data architecture really change or are companies still using existing architecture to implement this? ● What about technology to support this - Is Data Fabric the answer or best of breed tools? ● How critical is organisation to successful Data Mesh implementation ●…

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.

Standing at an inflection point is a magical experience. It’s where we look at what has come before, learn from it, and choose a new path. Data Mesh has motivated many organizations to stand at an inflection point of their approach to data.