The Context Crisis: Why Your Data Stack Understands Syntax But Not Semantics

Your data infrastructure is extraordinary. Infinite scale, billions of events, thousands of AI decisions per second. Yet when something breaks, you're still asking the same questions you asked ten years ago: what happened, where, and when.

The problem isn't your stack. It's that observability is stuck at the syntax layer. You know a table is stale. You don't know it feeds the pricing engine 47 sales reps depend on for live quotes.

This session introduces semantic context as the missing layer in modern data observability, and the concept of context graphs: not just lineage that maps how data moves, but intelligence that maps what data means to the business. The shift from "this asset is broken" to "this decision is wrong."

The stakes are highest in AI. When models act automatically, a technically valid dataset with the wrong business context causes as much damage as corrupted data. Most observability tools cannot see the difference.

What you will leave with:

• Why lineage captures syntax and misses semantics, and what that gap is costing you
• How context graphs model what technical metadata cannot
• The three context types every data platform needs: technical, operational, and semantic
• A framework for building semantic awareness into your observability strategy
• Real examples of semantic context preventing revenue leakage and AI bias

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