Most enterprise AI projects don’t fail because of the models, they fail because of the data. When teams spend 80% of their time wrangling features instead of building, innovation stalls. This session reveals how leading companies are unifying their data layers and deploying feature stores that support both predictive and generative AI workloads. Learn how open table formats like Delta Lake and Apache Iceberg form the foundation for scalable, governed, and reproducible AI systems and what it takes to finally eliminate the “feature engineering tax.” Key takeaways: → Learn how to design a unified data architecture that accelerates feature reuse and shortens time to model deployment. → Discover practical patterns for building enterprise feature stores that ensure online/offline consistency, lineage, and point-in-time accuracy. → See how unified governance frameworks enable self-service access without compromising compliance or data trust.