ABSTRACT
The current state of data availability and computational power have enabled researchers to analyze a large amount of information at unprecedented speed and scale. To do so, researchers have applied methods including text mining, network analysis, machine learning, and others. Those methods have received great attention, and their commonalities and differences have already been subject of study. However, when seen through the eye of the innovation and policy scholar, high-level patterns are shared among those. In this chapter, we focus on three data-driven analytics frameworks applied in a variety of scenarios, from the detection of emerging technologies to the linkage of technologies and social issues. The first is linking discovery when two different and disjoint topics are compared to find connecting terminologies that lead to hypothesis generation and public knowledge discovery. In the second, two knowledge representations of a single topic are compared to establish linking patterns or white spaces that can be seen as collaboration or commercialization opportunities – for instance when comparing the overlapping of academia and industry through the mining of academic articles and patents. And the third is mixed methods. This chapter discusses the implications of speeding up the knowledge discovery process for innovation and technology management and future frontiers of development for such data-driven methods.
