Forecasting Innovation Pathways of Big Data & Analytics

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

Research and development (R&D) leads to the creation of new science-based technology innovations. There is a need to improve understanding of how research discoveries translate into new technologies and then develop into useful innovation. Methods used to address this concern either draw on trends in large amounts of historic information or draw on expert judgment. This project will apply advanced research techniques to enhance methods to identify trends and patterns, and to help forecast innovation pathways. Such knowledge is vital to promote scientific progress by investing judiciously in high promise R&D. It also can aid in technology management to determine how best to advance a specific field of science. This project will provide a case study to improve five analytical processes, identified as vital to improve the methodology of forecasting innovation pathways. The case to be analyzed is ?big data & analytics? ? a topic of great national importance. Figuring out how to gain advantage from large data sets will impact national scientific progress, industrial productivity, and defense. At the same time, big data poses issues of privacy and security, among others. The topic of big data was selected because it is under study by the U.S. Government Accountability Office (GAO). We anticipate sharing information on methods and findings with GAO to gain insight into ways to make our methodology more useful. . This proposal extends a previously funded SciSIP project (1064146) to address a time-limited opportunity to address the GAO case.