Determining Critical Success Factors for Big Data Projects
Abstract
Critical success factors are activities that need to be given prime importance in order to achieve quality project success and can also help to prioritize tasks as a part of the project plan. In recent times, research on Big Data has been concentrated on effective algorithms and robust data models (Saltz & Shamshurin, 2016); however, minimal work has focused on the best methodology for executing such projects (Saltz, 2015). The exploratory nature of Big Data projects demands a more specific methodology that can handle the uncertain business requirements of such projects (Saltz, 2015). To help address this shortcoming, Big Data professionals were surveyed to determine the most significant factors in the success of related projects. Using multiple regression techniques and in particular stepwise regression, the results provide insight into related critical success factors and assist them in quickly determining the critical areas for project execution. It was found that the following factors can contribute critically to Project Success: business driven objectives, iterative approach of project execution, following project management processes, and using capabilities of tools.
Degree
M.S.
Advisors
Springer, Purdue University.
Subject Area
Management|Information Technology
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