Predicting the Efficiency with Knowledge Discovery of a Budgeted Company: A Cuban University - Validation through Three Semesters

The efficiency analysis of a company cannot be reduced to a great number of statistical tables, despite the reliability of them. It has been shown to be a better idea to seek the “essence” using Knowledge Discovery (KD) techniques. In this paper, a simple methodology to apply KD in the efficiency analysis of a budgeted company is presented. These analyses complement those from classical OLAP and Interactive Graphics. Specifically, it is shown how to use in three steps: univariate analysis, non-supervised and supervised multivariate machine learning techniques in order to support the decision making. All these procedures are illustrated using the SIGENU database (in Spanish: Sistema de Gestión de la Nueva Universidad) of UCLV students and the efficiency measure was the student graduation on time or not. The presented methodology was elaborated in 2009 and it has been preliminary validated during three semesters of the years 2010 to 2012.