AI-Ready Data Design: Building Systems That AI Can Actually Use

AI agents can only work with data they can understand. This course shows you how to design that clarity into your data systems. Learn how to resolve ambiguity in your data models so AI agents can navigate and interpret data correctly, then craft AI-readable metadata—clear column descriptions, metric definitions, and routing triggers—to guide query behavior. Along the way, gather insights to establish data ownership, apply governance frameworks, and deprecate redundant tables to keep datasets organized. Finally, you'll validate your work by running AI navigation tests that confirm reliability before deployment. A single end-to-end case study runs throughout, showing you how a disorganized warehouse can become an efficient, AI-friendly system. Designed for data professionals, this course equips you with the skills you need to govern data warehouses effectively in the AI age.

Learning objectives
Diagnose why AI fails in data environments by tracing incorrect query outputs back to schema issues.
Design a clean, AI-navigable data model that eliminates ambiguity through consistent naming, grain, and structure.
Write AI-readable metadata—column descriptions, metric definitions, routing triggers, and gotcha flags—that directs query behavior.
Build and validate an AI-ready data catalog using validation queries.
Apply governance and ownership frameworks to assign ownership, deprecate redundant tables, and establish review processes.
Test AI reliability before production by running a full navigation test and scoring results against a rubric.