Knowledge discovery through higher education census data

Universities have three core missions: teaching, researching, and public service. Even though the Education environment has become more marketized, to survive, the Higher Education Institutions (HEIs) must behave like non-profit organizations, prioritizing revenue creation, the public good and serving as providers of value for society through creation and dissemination of knowledge and educational development Kaplan (Kaplan, 2016; Huggins and Prokop, 2016; Huggins and Thompson, 2014). Concerning that knowledge is the main driver for future social and economic development in the Knowledge Economy and Society, this paper analyzes the data from Brazilian Higher Education Census based on HEIs, their undergraduate courses, professors, and students. It uses Self-Organizing Maps (SOM), a type of neural network which deals with a massive volume of data, to explore patterns hidden in the data. The goal of the paper is to discover knowledge innovatively in the Education Area. As a result, it assesses the HEIs internal dynamics and, according to the Resource-Based View (RBV) theory, it presents HEIs with similar, dissimilar or complementary resources. This identification raises new forms of relationships based on the combination of resources among institutions, which allow them to become more entrepreneurial and behave more collaboratively. This new knowledge plays a significant role in the implementation of competitive responses or decisions to take and contributes to advance the RBV Theory.