Quantitative Comparison of Big Data Analytics and Business Intelligence Project Success Factors

Abstract

Decision support systems such as big data, business intelligence (BI), and analytics offer firms capabilities to generate new revenue sources, increase productivity and outputs, and gain strategic benefits. However, the field is crowded with terminology that makes it difficult to establish reasonable project scopes and to staff and manage projects. This study clarifies the terminology around data science, computational social science, big data, business intelligence, and analytics, and defines decision support projects. The study uses quantitative methods to empirically classify the project scopes, investigate the similarities and differences between the project types, and identify the critical success factors. The results suggest BI and big data analytics projects are differentiated based on analytics competence, proprietary algorithms, and distinctive business processes. They are significantly different for 19 of the 52 items evaluated. For big data analytics projects, many of the items are correlated with strategic benefits, while for BI projects they are associated with the operational benefits of cost and revenue performance. Project complexity is driven by the project characteristics for BI projects, while the external market drives the complexity of big data analytics projects. These results should inform project sponsors and project managers of the contingency factors to consider when preparing project plans.