Start Date
11-8-2016
Description
Many organizations are implementing analytics projects. Unfortunately, most big data projects fail, and the reasons are not well understood (LaValle et al. 2013). This paper studies the chartering phase of a predictive analytics project to further our understanding of big data projects. We conduct a qualitative study at a large supply chain company. We employ a variety of qualitative research techniques, including participant observation and interviews of key project personnel. In particular, we conduct a case study of the company’s predictive analytics journey and the challenges it encountered. These include corporate governance, vendor relationships, and lack of data. This research adds insight into why companies implement predictive analytics projects and the challenges they face. The study makes several contributions to the IS literature by adding a qualitative perspective to the big data field, which is dominated by quantitative studies, and focusing on the chartering phase of project implementation.
Recommended Citation
Chipidza, Wallace; George, Jordana; and Koch, Hope, "Chartering Predictive Analytics: A Case Study" (2016). AMCIS 2016 Proceedings. 6.
https://aisel.aisnet.org/amcis2016/Decision/Presentations/6
Chartering Predictive Analytics: A Case Study
Many organizations are implementing analytics projects. Unfortunately, most big data projects fail, and the reasons are not well understood (LaValle et al. 2013). This paper studies the chartering phase of a predictive analytics project to further our understanding of big data projects. We conduct a qualitative study at a large supply chain company. We employ a variety of qualitative research techniques, including participant observation and interviews of key project personnel. In particular, we conduct a case study of the company’s predictive analytics journey and the challenges it encountered. These include corporate governance, vendor relationships, and lack of data. This research adds insight into why companies implement predictive analytics projects and the challenges they face. The study makes several contributions to the IS literature by adding a qualitative perspective to the big data field, which is dominated by quantitative studies, and focusing on the chartering phase of project implementation.