Demanded and imparted Big Data Competences: towards an integrative Analysis

Exploiting big data seems to be an important success factor for companies in the digital age. However, recent studies show that there is a short supply of professionals who are able to deal with data appropriately. This is at least partly caused by a mismatch between university offerings and presumed industry needs. This study analyses two related questions. First, what competences are actually required for being a data professional? Second, what competences are imparted through data-related master`s programmes? These questions are answered by applying a topic model approach (first question) and deductive content analysis (second question). By using the same set of competence dimensions, the answers to these questions are used to discuss the overall issue of how curricula are aligned with workforce demands for data-related competences. The focus is placed on the UK market that suffers from a shortage of data professionals particularly in the financial industry. We find that companies require ‘all-rounders’ who possess strong technical, analytical, and business competences, while master`s programmes rarely impart business competences. Main contributions include an empirically derived typology of data professionals, the application of a topic model for IS research, and an analysis framework that allows universities to critically assess their offerings.