1. PM Stammtisch : Project Success Factors for BI and Big Data

In the Project Management Stammtisch on 28-September, we covered the topic “Project Success Factors for Business Intelligence (BI) and Big Data.”

The discussion was on a comparative analysis of Big Data Analytic and Business Intelligence projects from our project success study. In short, the study compared 52 demographic and project attributes. None of the organizational demographic (e.g., industry, organization size) or project demographic or efficiency factors (e.g., team size, budget, duration) items were significantly different. Also, business strategy, top management support, and client acceptance were not significantly different. Of the 39 remaining items, 18 were significantly different all in favor of Big Data Analytics. This information reflected the discussion of the session participants. Especially interesting was the role of Senior Managers in the success of the projects. The participants reflected that Senior Managers could act as a buffer between the project and top management to ensure the project maintains an agreed course of action until a successful outcome is reached.

The following diagram reflects the differences in the project complexity, pace, technology uncertainty, and product novel between Big Data Analytic and Business Intelligence projects. Complexity and product novelty were significantly different. The diagram is based upon the Diamond model for project success from Shenhar, A., & Dvir, D. (2007).

Figure 1: Project Attribute Comparison

You can find a conference paper on the study comparison at the following location: https://annals-csis.org/proceedings/2018/drp/pdf/125.pdf.


Shenhar, A., & Dvir, D. (2007). Reinventing project management: the diamond approach to successful growth and innovation. Boston, Mass.: Harvard Business School Press.

Miller, G. J. (2018). Comparative Analysis of Big Data and BI Projects. Paper presented at the Proceedings of the 2018 Federated Conference on Computer Science and Information Systems. http://dx.doi.org/10.15439/2018F125

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