As with any new initiative or tool requiring significant investment, the business value of statistically-based predictive models must be demonstrated before they will see widespread adoption. The SEI Software Engineering Measurement and Analysis (SEMA) initiative has been leading research to better understand how existing analytical and statistical methods can be used successfully and how to determine the value of these methods once they have been applied to the engineering of large-scale software-reliant systems. As part of this effort, the SEI hosted a series of workshops that brought together leaders in the application of measurement and analytical methods in many areas of software and systems engineering. The workshops help identify the technical barriers organizations face when they use advanced measurement and analytical techniques, such as computer modeling and simulation. This post focuses on the technical characteristics and quantified results of models used by organizations at the workshops.