As the provisioning of timely business insights becomes increasingly relevant for organizations, real-time business intelligence (RTBI) is considered a promising vehicle to minimize the time span between elicitation, analysis, and subsequent action. However, so far, there seems no structured and systematic taxonomy in which RTBI systems can be classified and uncertainty regarding the dimensions and characteristics that constitute these systems. By analyzing extant business intelligence literature, this paper develops a taxonomy for RTBI systems to address these current impediments. Reviewing 89 studies in leading journals and conferences during the years 2000-2016, we found 29 relevant characteristics along seven dimensions for RTBI systems. Our taxonomy may serve as a foundational step to incorporate a broader theoretical perspective to integrate concepts and findings across all seven dimensions. The main contribution of the paper is in the organization and structuring of the body of knowledge in RTBI along the identified dimensions and characteristics for the advancement of the field, which is specifically relevant due to its relatively young nature. For practice, our taxonomy helps organizations to evaluate their RTBI systems or conceive the challenges of building such a system either from scratch or as an update of their existing infrastructure.
Nadj, Mario and Schieder, Christian, (2017). "TOWARDS A TAXONOMY OF REAL-TIME BUSINESS INTELLIGENCE SYSTEMS". In Proceedings of the 25th European Conference on Information Systems (ECIS), Guimarães, Portugal, June 5-10, 2017 (pp. -). ISBN 978-989-20-7655-3 Research Papers.