Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 15

Proceedings of the 2018 Federated Conference on Computer Science and Information Systems

Comparative Analysis of Big Data and BI Projects

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 701705 ()

Full text

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.

References

  1. J. K. Pinto and D. P. Slevin, "Critical Success Factors Across the Project Life Cycle," Project Management Journal, vol. 19, no. 3, p. 67, Jun 1988.
  2. S. Olbrich, J. Pöppelbuß, and B. Niehaves, "Critical Contextual Success Factors for Business Intelligence: A Delphi Study on their relevance, variability, and controllability," in 45th Hawaii International Conf. on System Sciences, Hawaii, 2012, pp. 4148-4157.
  3. P. Géczy, "Big Data Management: Relational Framework," Review of Business & Finance Studies, vol. 6, no. 3, pp. 21-30, Aug 2015.
  4. W. Yeoh and A. Koronios, "Critical Success Factors For Business Intelligence Systems," The Journal of Computer Information Systems, vol. 50, no. 3, pp. 23-32, Jul 2010.
  5. S. Akter and S. F. Wamba, "Big data analytics in E-commerce: a systematic review and agenda for future research," Electronic Markets, vol. 26, no. 2, pp. 173-194, May 2016.
  6. T. Gilad and B. Gilad, "SMR Forum: Business Intelligence - The Quiet Revolution," Sloan Management Review (1986-1998), vol. 27, no. 4, pp. 53-61, Jun 1986.
  7. T. H. Davenport and J. Harris, Competing on Analytics: The New Science of Winning. Boston, MA, USA: Harvard Business School Press, 2007.
  8. J. Hammerbacher, "Information Platforms and the Rise of the Data Scientist," in Beautiful data: the stories behind elegant data solutions, T. Segaran and J. Hammerbacher, Eds., ed Sebastopol, CA, USA: O'Reilly Media, Inc., 2009, pp. 73-84.
  9. S. Sun, C. G. Cegielski, and Z. Li, "Amassing and Analyzing Customer Data in the Age of Big Data: A Case Study of Haier's Online-to-Offline (O2O) Business Model," Journal of Information Technology Case and Application Research, vol. 17, no. 3/4, pp. 156-165, Dec 2015.
  10. T. H. Davenport and D. J. Patil, "Data Scientist: The Sexiest Job of the 21st Century," Harvard Business Review, vol. 90, no. 5, pp. 70-76, Oct 2012.
  11. R. M. Chang, R. J. Kauffman, and Y. Kwon, "Understanding the paradigm shift to computational social science in the presence of big data," Decision Support Systems, vol. 63, no. p. 67, Jul 2014.
  12. R. Iqbal, F. Doctor, B. More, S. Mahmud, and U. Yousuf, "Big Data analytics and Computational Intelligence for Cyber–Physical Systems: Recent trends and state of the art applications," Future Generation Computer Systems, Nov 2017.
  13. R. J. Turner and R. Zolin, "Forecasting Success on Large Projects: Developing Reliable Scales to Predict Multiple Perspectives by Multiple Stakeholders Over Multiple Time Frames," Project Management Journal, vol. 43, no. 5, pp. 87-99, Oct 2012.
  14. L. A. Ika, "Project success as a topic in project management journals," Project Management Journal, vol. 40, no. 4, pp. 6--19, Dec 2009.
  15. G. J. Miller, "Decision Support Project: Project Success and Organizational Performance," DBA Thesis, Project and Program Management, SKEMA Business School, Lille, France, 2018.
  16. J. Thomas and J. Kielman, "Challenges for visual analytics," Information Visualization, vol. 8, no. 4, pp. 309-314, Jan 2009.
  17. H. Barki and J. Hartwick, "Measuring user participation, user involvement, and user attitude," MIS Quarterly, vol. 18, no. 1, pp. 59-82, March 1994.
  18. A. Shenhar and D. Dvir, Reinventing project management: The diamond approach to successful growth and innovation. Boston, MA, USA: Harvard Business School Press, 2007.
  19. S. Debortoli, O. Müller, and J. P. D. Vom Brocke, "Comparing Business Intelligence and Big Data Skills," Business & Information Systems Engineering, vol. 6, no. 5, pp. 289-300, Oct 2014.
  20. H. C. Lucas Jr, "Empirical evidence for a descriptive model of implementation," MIS Quarterly, pp. 27-42, Jun 1978.
  21. H. Barki and S. L. Huff, "Change, attitude to change, and decision support system success," Information and Management, vol. 9, no. 5, pp. 261-268, 1985.
  22. W. H. DeLone and E. R. McLean, "Information Systems Success: The Quest for the Dependent Variable," Information Systems Research, vol. 3, no. 1, pp. 60-95, Dec 1992.
  23. W. Belassi and O. I. Tukel, "A new framework for determining critical success/failure factors in projects," International Journal of Project Management, vol. 14, no. 3, pp. 141-151, Jun 1996.
  24. M. Halaweh and A. El Massry, "Conceptual Model for Successful Implementation of Big Data in Organizations," Journal of International Technology and Information Management, vol. 24, no. 2, pp. 21-34, Dec 2015.