Generative AI is transforming every industry, but no prompt can compensate for unreliable data.

This session explores why data engineering remains the most important investment for successful AI initiatives. Through practical examples, we'll examine how poor metadata, inconsistent schemas, duplicate records, missing relationships, and weak governance directly impact AI outcomes.

Instead of focusing on models, we'll focus on the engineering practices that enable AI systems to deliver trustworthy and measurable results.

What you'll learn
Why data quality determines AI success
Engineering practices for trustworthy AI
Data governance essentials
Measuring AI readiness
Practical implementation roadmap