A Taxonomy for Business Intelligence Dashboard Products

Business Intelligence (BI) dashboards integrate heterogeneous data and analytical capabilities to support decisions. However, product-centered descriptions obscure how implementations, making it difficult to compare decision scope, data, interaction, lifecycle, and assurance. This paper addresses that comparability gap by developing an evidence-based taxonomy for BI-supported dashboard implementations. To this end, a systematic literature review (SLR) was conducted to assemble the empirical evidence. The resulting taxonomy comprises 7 facets and 30 characteristics supported by reproducible coding questions. Its application distinguishes implementations using the same platform and reveals recurrent archetypes. The analysis shows that lifecycle closure and semantic assurance discriminate implementations more effectively than product prevalence, whereas predictive, prescriptive, and conversational configurations remain sparsely evidenced. The taxonomy provides researchers with a common framework for cumulative comparison and practitioners with a contingency-based procedure for defining requirements before platform selection.