Sifflet is the leading data observability platform that delivers data trust at scale for analytics and AI initiatives by bridging the gap between technical and business users.  Global customers like the Penguin Random House, Carrefour, Auchan, CMA CGM and Adaptavist rely on Sifflet to reach and maintain high levels of data quality and reliability needed for business critical analytics and AI.  Sifflet is a best-in-class data observability solution for the entire organization, ranked Best estimated ROI and Fastest implementation by G2 users. 

Sifflet, the AI-augmented data observability platform built for data teams with business users in mind.

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
