Data-Driven Decision-Making In Small Organizations : A qualitative study in optimizing BI deployment in Vasaloppet

Sammanfattning: Organizations are social systems established to make decisions. Modern organizational decision-making is complex and can easily overwhelm the capacity of individuals. Because of the complexity of multi-person decisions, there is a big risk for uncertainty in decision-making. In recent years, the rise of business intelligence has enabled organizations to base their decisions on data and minimize uncertainty in their decision-making. However, deployment of business intelligence systems is characterized by complexity, making many small and medium-sized organizations fail to use such a system effectively.This thesis aims to identify and describe variables that influence successful use of a business intelligence architecture to support small organizations in making data-based decisions, what small organizations need to become data-driven in decision-making, and what measures small organizations can take to use business intelligence systems efficiently. Eight semi-structured interviews were conducted with professionals from Vasaloppet, a small organization deploying a business intelligence system. The empirical data gathered have been analyzed with a thematic approach. The thematic analysis identified four themes’ Deficiencies in organizational governance, Deficiencies in data management, Perceived workload, and Degree of matching between processes, organization, and strategy. Findings in these themes and underlying codes within these themes revealed problem areas in organizational governance when making decisions. Respondents mentioned challenges with a lack of a decision model, clear business plan, and intra-organizational understanding. When it comes to becoming data-driven, respondents said deficiency of structure for communication, lack of access to data, lack of data in decision-making, general workload, deficiencies in project results, and deficiencies in degree of matching as problematic. Based on the results of this study, guidelines are presented for small organizations to become data-driven in their decision-making.Keywords: Data-driven decision-making, business intelligence, small organizations