Annals of Computer Science and Information Systems, Volume 15

DOI: http://dx.doi.org/10.15439/2018F125

Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 701–705 (2018)

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Abstract. Decision support systems such as big data, business intelligence, and analytics, offer firms capabilities to generate new revenue sources, increase productivity and outputs, and improve competitiveness. 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 the data science, computation social science, big data, business intelligence, and analytics and describes their meaning relative to decision support projects. For BI and big data projects, it identifies the critical success factors, empirically classifies the project scopes, and investigates the similarities and differences between the project types. This comparative analysis provides unique insights into the factors and criteria that influence BI and big data project success. These results should inform project sponsors and project managers of the contingency factors to consider when preparing project charters and plans.

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