Artificial Intelligence is changing how organizations interact with their data.
But AI systems can only reason over data that is structured, meaningful, and semantically consistent.

This raises a surprising question:

Are classic data warehouse design principles more relevant than ever?

In this session we explore how the principles from The Data Warehouse Toolkit — including star schemas, conformed dimensions, and business-oriented modeling — form the foundation for modern ontology-driven AI platforms.

Using capabilities from Microsoft Fabric such as Semantic Models, Data Agents, Graph relationships, and Fabric IQ, we will see how dimensional models can evolve into semantic knowledge layers that AI systems can reason over.

You will learn how modern architectures combine:

Dimensional modeling

Business ontologies

Semantic layers

AI agents and copilots

to build AI-ready data platforms.

The result is a powerful architectural pattern where data warehouses become knowledge platforms for intelligent systems.

Key Takeaways

Attendees will learn:

• Why Kimball dimensional modeling is critical for AI-ready data architectures
• How ontologies complement dimensional models
• How semantic layers enable AI agents to reason about business data
• How Microsoft Fabric provides the components to implement this architecture
• How to design AI-ready enterprise data platforms

This session is for Data Engineers, BI/Analytics Architects, CTO / Decision Makers