Test and measurement data continues to grow, yet only a small fraction is analyzed due to fragmented formats, inconsistent quality, and limited accessibility across engineering environments. This talk explores how ASAM ODS provides a standardized, future proof foundation for structuring, accessing, and enriching test data to make it AI and simulation ready. Attendees will learn how modern data plugins, metadata modeling, and open Python based APIs streamline analysis workflows and ensure consistent data semantics. The session highlights practical steps to unlock hidden value in existing measurement data, eliminate data silos, and accelerate scalable AI driven virtual testing.