Most teams think data quality begins with writing validation tests.

In reality, quality starts much earlier with clear ownership, reliable pipelines, meaningful monitoring, and shared engineering practices.

This session explores how modern teams build trustworthy data products by treating data quality as an engineering discipline rather than a checklist. We'll examine common failure patterns, why traditional validation isn't enough, and how observability, metadata, testing and documentation work together to create reliable systems.

Using practical case studies and audience exercises, attendees will learn how to design workflows that detect issues earlier, reduce production incidents and build confidence in their data.