Automated analysis, machine learning and higher channel depend on the foundation of reliable measurement. But as channel counts increase and automation moves engineers away from the data, the quality of test data matters more, not less. Data corrupted introduced during acquisition can rarely be corrected downstream.

This panel examines the integrity of the test data chain from sensor to conclusion. It covers signal conditioning and the common sources of corruption in tests that are expensive and infeasible to repeat, instrumentation in extreme environments, deterministic data transport for real-time and hardware-in-the-loop work, and interoperability between hardware and software that was never designed to work together. This panel will interest test engineers, instrumentation specialists and anyone being asked to place more confidence in automated analysis of data.