Start Date
16-8-2018 12:00 AM
Description
This paper reports from an exploratory study that examines utilization of Business Intelligence and Analytics (BI&A) in Small-and-Medium-sized Enterprises (SMEs). In total, 24 semi-structured interviews of BI&A experts were conducted. The experts highlighted several critical issues that SMEs should consider: (1) to start Small, think Big was emphasized as an appropriate BI&A investment strategy for SMEs to obtain value in terms of both quick wins and long-term assets and impacts, (2) to consider BI&A investment without implementing a traditional data warehouse, and (3) to consider the automated data warehouse approach. In addition, the experts underscored to pay more attention to data governance. A recognized value framework from the literature was applied as an analytical lens to interpret the findings. We suggest modification of this framework to make it less waterfall oriented and more iterative and agile to create value from BI&A in SMEs. Future research should assess SMEs readiness and capabilities for BI&A. In addition, we need to understand the exclusive needs for decision-making in SMEs across industries.
Recommended Citation
Llave, Marilex Rea; Hustad, Eli; and Olsen, Dag H., "Creating Value from Business Intelligence and Analytics in SMEs: Insights from Experts" (2018). AMCIS 2018 Proceedings. 13.
https://aisel.aisnet.org/amcis2018/DataScience/Presentations/13
Creating Value from Business Intelligence and Analytics in SMEs: Insights from Experts
This paper reports from an exploratory study that examines utilization of Business Intelligence and Analytics (BI&A) in Small-and-Medium-sized Enterprises (SMEs). In total, 24 semi-structured interviews of BI&A experts were conducted. The experts highlighted several critical issues that SMEs should consider: (1) to start Small, think Big was emphasized as an appropriate BI&A investment strategy for SMEs to obtain value in terms of both quick wins and long-term assets and impacts, (2) to consider BI&A investment without implementing a traditional data warehouse, and (3) to consider the automated data warehouse approach. In addition, the experts underscored to pay more attention to data governance. A recognized value framework from the literature was applied as an analytical lens to interpret the findings. We suggest modification of this framework to make it less waterfall oriented and more iterative and agile to create value from BI&A in SMEs. Future research should assess SMEs readiness and capabilities for BI&A. In addition, we need to understand the exclusive needs for decision-making in SMEs across industries.