Self-service business intelligence and analytics application scenarios: A taxonomy for differentiation

Abstract

Self-service business intelligence and analytics (SSBIA) empowers non-IT users to create reports and analyses independently. SSBIA methods and processes are discussed in the context of an increasing number of application scenarios. However, previous research on SSBIA has made distinctions among these scenarios only to a limited extent. These scenarios include a wide variety of activities ranging from simple data retrieval to the application of complex algorithms and methods of analysis. The question of which dimensions are suitable for differentiating SSBIA application scenarios remains unanswered. In this article, we develop a taxonomy to distinguish among SSBIA applications more effectively by analyzing the relevant scientific literature and current SSBIA tools as well as by conducting a case study in a company. Both researchers and practitioners can use this taxonomy to describe and analyze SSBIA scenarios in further detail. In this way, the opportunities and challenges associated with SSBIA application can be identified more clearly. In addition, we conduct a cluster analysis based on the SSBIA tools thus analyzed. We identify three archetypes that describe typical SSBIA tools. These archetypes identify the application scenarios that are addressed most frequently by SSBIA tool providers. We conclude by highlighting the limitations of this research and suggesting an agenda for future research.