What do particle physics experiments at CERN and modern enterprise AI platforms have in common?
At first glance, not much. One explores the fundamental laws of the universe, while the other powers business decisions across logistics, finance, HR, transportation, and supply chains. Yet both face the same challenge: transforming massive volumes of data into information that people and increasingly AI can trust.
Having worked with petabytes of scientific data generated at CERN and later designing enterprise data platforms integrating more than 100 source systems, I've learned that while technologies evolve, the core principles remain remarkably consistent. Trust, governance, data quality, lineage, and reproducibility are just as essential in enterprise AI as they are in scientific research.
In this session, I'll share the journey of building and evolving an Azure-based enterprise data platform that brings together more than 100 business systems into a single, trusted foundation. We'll explore the architectural decisions behind scalable ingestion, reusable integration patterns, metadata-driven governance, self-service analytics, and preparing data platforms for AI-driven applications.
Rather than focusing on the latest AI models or cloud features, this session is about the foundations that make AI successful in the real world. Through practical examples and lessons learned, you'll see why organizations that invest in trusted data today are the ones best positioned to adopt copilots, intelligent agents, and the next generation of AI-powered business solutions tomorrow.
Whether you're modernizing an existing platform or building one from scratch, you'll leave with practical architectural patterns, real-world lessons, and proven principles that scale from petabytes of scientific data at CERN to complex enterprise environments integrating more than 100 interconnected source systems.
Because before organizations can build intelligent AI solutions, they first need to build a data platform those solutions can trust.
