The major advantage of Data Envelopment Analysis (DEA) is to reduce multivariate data to a single key performance indicator (KPI). However, this KPI is just one element to determine the (economic) situation of a Decision-Making Unit (DMU), e.g. a bank. Beside this, DEA offers a wide range of information about the observed DMU(s). Similarities or dissimilarities within the set of DMUs can be graphically explored by visualized representations – intuitively as well as analytically. This may discover valuable knowledge for decision support.
The purpose of this contribution is to exemplify the interpretation of the aforementioned visualization of DEA results for the Eastern European banking sector. To compute the visualized representations we apply the multivariate method of Multidimensional Scaling. In a second step, primal dimensions of graphical representations are determined. Finally and after incorporating supplementary data, interpretations of resulting latent variables are given. The proposed approach provides new insights: for decision-making units as well as authorities, i.e. for regulatory purposes.