Modern data systems are built around layers. We have storage layers, transformation layers, semantic layers, and increasingly feature stores and model layers. But as systems begin to influence automated decisions, another layer becomes necessary: an evidence layer.

Logs and dashboards show what happened. An evidence layer explains why it happened and how the system arrived there. Without it, traceability becomes reactive, and accountability becomes difficult under scrutiny.

This session explores the concept of the evidence layer as a structural component of data architecture. We will examine how lineage, assumptions, transformations, and decision paths can be organized in a way that supports defensibility, transparency, and long-term system integrity.

Attendees will leave with a practical framework for thinking about evidence as an architectural design choice rather than a reporting afterthought.