This paper represents a team project conducted as part of ETM 530/630 Decision Making class in Winter-2017. In this project, team 4 investigated a known challenge related to big data; big data projects have high failure percentage, causing firms to lose time, money, and resources in futile efforts to gain advantages from big data insights and analytics. In this project, the reasons behind big data projects failure were explored. Leading to the development of an HDM model that can be used by firms to evaluate readiness to implement this kind of projects, and highlight/address probable causes of failure before the project even start. Hence, increasing the chances of implementing a successful project that will lead to a big data system that can deliver value to firm and provide insights and analytics that will significantly help in addressing the problem it is built to help solve. The model was evaluated by experts from the industry, and then tested against a hypothetical case, in which Portland State University readiness to implement a big data project to address a main problem facing the university was conducted. Finally, a discussion about the results of the model, experts’ evaluation, and case study were offered.
Giadedi, Abdulhakim; Eljayar, Ali; Barham, Husam; Patil, Priyanka; and Vasanth, Shreyas, "Evaluating Big Data Projects Probability of Success: A Hierarchical Decision Model" (2017). Engineering and Technology Management Student Projects. 126.