Abstract
Traditional methods of testing were based on Instrument-Centric models which included sensor and measurement capabilities inside of instruments that allowed test engineers to be able to perform measurements and collect data. Such data would then be analyzed by software to determine whether the test resulted in a pass or fail disposition. Modern technologies have significantly changed the equation into a Data-Centric model whereby data, and lots of it, are the key to gain insight into production and ensure quality. However, most test systems struggle to handle the data that is created by their systems. This paper will discuss three topics regarding data; a) Data Management — flat files, databases and the pros and cons of each, b) Data Visualization-viewing reports and charts as well as higher level analytics (pareto, cpk), and c) Data Validation — post-test limit checking and yield analysis.
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