About

Most organizations have moved their data to the cloud — but cloud migration alone doesn’t make your data platform intelligent. The real challenge lies in what comes next.

In this talk, we explore how data platforms must evolve beyond storage and analytics into true intelligence platforms. Drawing on experience building enterprise-scale data infrastructure at Morgan Stanley, we examine the six pillars of AI readiness: semantics, metadata, data quality, governance, discoverability, and performance.

We’ll walk through a practical layered architecture — from raw source systems through semantic and metrics layers to model execution — that enables AI agents and analytics applications to operate on meaning, not raw tables. Attendees will leave with a clear framework for decoupling calculations from datasets, establishing reusable metrics, and building the shared semantic foundation that modern AI applications demand.

Takeaways 

• Cloud migration is just the starting point – AI readiness requires semantics, governance, and data quality on top of your cloud foundation
• AI works on meaning, not tables – A semantic and metrics layer abstracts raw complexity, enabling consistent, reusable business logic across all consumers
• Unified platforms beat siloed pipelines – A shared layered architecture reduces duplication, accelerates AI adoption, and ensures every application draws from a single trusted source of truth