Abstract
Business analytics is often considered a silver bullet in organizational strategy; however, much remains to be discovered about the development of organizational insights and decision making during that process. In this research, we propose an investigation of business analytics from the perspective of data analysts. Developing an optimal model is virtually impossible due to uncertainty in the process, shifting the modeling goal from perfection to adequacy. We argue that clarity about the decision of an analyst that a model is “good enough,” may help organizations improve business analytics outcomes. Through the theoretical lens of cognitive stopping rules, we outline a proposal for a qualitative investigation of the modeling process. We expect this will have far-reaching impacts on research in the areas of business analytics and cognitive stopping rules. We also anticipate findings that are applicable to organizations trying to better understand the role of data analysts in their modeling process.
Recommended Citation
Torres, Russell and Gerhart, Natalie, "When is Enough, Enough? The Critical Decision to Stop Building Business Analytics Models" (2019). AMCIS 2019 Proceedings. 3.
https://aisel.aisnet.org/amcis2019/data_science_analytics_for_decision_support/data_science_analytics_for_decision_support/3
When is Enough, Enough? The Critical Decision to Stop Building Business Analytics Models
Business analytics is often considered a silver bullet in organizational strategy; however, much remains to be discovered about the development of organizational insights and decision making during that process. In this research, we propose an investigation of business analytics from the perspective of data analysts. Developing an optimal model is virtually impossible due to uncertainty in the process, shifting the modeling goal from perfection to adequacy. We argue that clarity about the decision of an analyst that a model is “good enough,” may help organizations improve business analytics outcomes. Through the theoretical lens of cognitive stopping rules, we outline a proposal for a qualitative investigation of the modeling process. We expect this will have far-reaching impacts on research in the areas of business analytics and cognitive stopping rules. We also anticipate findings that are applicable to organizations trying to better understand the role of data analysts in their modeling process.