Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Information Technology for Management: Emerging Research and Applications (AITM 2018, ISM 2018)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 346))

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Eom, S.B.: The contributions of systems science to the development of the decision support system subspecialties: an empirical investigation. Syst. Res. Behav. Sci. 17, 117 (2000). https://doi.org/10.1002/(SICI)1099-1743(200003/04)17:2%3c117:AID-SRES288%3e3.0.CO;2-E

    Article  Google Scholar 

  2. Davenport, T.H., Harris, J.: Competing on Analytics: The New Science of Winning. Harvard Business School Press, Boston (2007)

    Google Scholar 

  3. Olbrich, S., Pöppelbuß, J., Niehaves, B.: Critical contextual success factors for business intelligence: a Delphi study on their relevance, variability, and controllability. In: 45th Hawaii International Conference on System Sciences, pp. 4148–4157 (2012). https://doi.org/10.1109/HICSS.2012.187

  4. Yeoh, W., Koronios, A.: Critical success factors for business intelligence systems. J. Comput. Inf. Syst. 50, 23–32 (2010). https://doi.org/10.1080/08874417.2010.11645404

    Article  Google Scholar 

  5. Dawson, L., Van Belle, J.-P.: Critical success factors for business intelligence in the South African financial services sector. S. Afr. J. Inf. Manag. 15, 1–12 (2013). https://doi.org/10.4102/sajim.v15i1.545

    Article  Google Scholar 

  6. Lucas Jr., H.C.: Empirical evidence for a descriptive model of implementation. MIS Q. 27–42 (1978). https://doi.org/10.2307/248939

    Article  Google Scholar 

  7. Thomas, J., Kielman, J.: Challenges for visual analytics. Inf. Vis. 8, 309–314 (2009). https://doi.org/10.1057/ivs.2009.26

    Article  Google Scholar 

  8. Hammerbacher, J.: Information platforms and the rise of the data scientist. In: Segaran, T., Hammerbacher, J. (eds.) Beautiful Data: The Stories Behind Elegant Data Solutions, pp. 73–84. O’Reilly Media Inc., Sebastopol (2009)

    Google Scholar 

  9. Sun, S., Cegielski, C.G., Li, Z.: Amassing and analyzing customer data in the age of big data: a case study of Haier’s online-to-offline (O2O) business model. J. Inf. Technol. Case Appl. Res. 17, 156–165 (2015). https://doi.org/10.1080/15228053.2015.1095017

    Article  Google Scholar 

  10. Géczy, P.: Big data management: relational framework. Rev. Bus. Financ. Stud. 6, 21–30 (2015)

    Google Scholar 

  11. Akter, S., Wamba, S.F.: Big data analytics in E-commerce: a systematic review and agenda for future research. Electron. Markets 26, 173–194 (2016). https://doi.org/10.1007/s12525-016-0219-0

    Article  Google Scholar 

  12. Halaweh, M., El Massry, A.: Conceptual model for successful implementation of big data in organizations. J. Int. Technol. Inf. Manag. 24, 21–34 (2015)

    Google Scholar 

  13. Siddiqa, A., et al.: A survey of big data management: taxonomy and state-of-the-art. J. Netw. Comput. Appl. 71, 151–166 (2016). https://doi.org/10.1016/j.jnca.2016.04.008

    Article  Google Scholar 

  14. Davenport, T.H., Patil, D.J.: Data scientist: the sexiest job of the 21st Century. Harv. Bus. Rev. 90, 70–76 (2012)

    Google Scholar 

  15. Chang, R.M., Kauffman, R.J., Kwon, Y.: Understanding the paradigm shift to computational social science in the presence of big data. Decis. Support Syst. 63, 67 (2014). https://doi.org/10.1016/j.dss.2013.08.008

    Article  Google Scholar 

  16. Iqbal, R., Doctor, F., More, B., Mahmud, S., Yousuf, U.: Big data analytics and computational intelligence for cyber-physical systems: recent trends and state of the art applications. Future Gener. Comput. Syst. (2017). https://doi.org/10.1016/j.future.2017.10.021

  17. Turner, R.J., Zolin, R.: Forecasting success on large projects: developing reliable scales to predict multiple perspectives by multiple stakeholders over multiple time frames. Proj. Manag. J. 43, 87–99 (2012). https://doi.org/10.1002/pmj.21289

    Article  Google Scholar 

  18. Ika, L.A.: Project success as a topic in project management journals. Proj. Manag. J. 40, 6–19 (2009). https://doi.org/10.1002/pmj.20137

    Article  Google Scholar 

  19. Pinto, J.K., Slevin, D.P.: Critical success factors across the project life cycle. Proj. Manag. J. 19, 67 (1988)

    Google Scholar 

  20. Belassi, W., Tukel, O.I.: A new framework for determining critical success/failure factors in projects. Int. J. Proj. Manag. 14, 141–151 (1996). https://doi.org/10.1016/0263-7863(95)00064-X

    Article  Google Scholar 

  21. Ziemba, E., Kolasa, I.: Risk factors relationships for information systems projects – insight from Polish public organizations. In: Ziemba, E. (ed.) Information Technology for Management. LNBIP, vol. 243, pp. 55–76. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-30528-8_4

    Chapter  Google Scholar 

  22. Chatzoglou, P., Chatzoudes, D., Apostolopoulou, G.: Examining the antecedents and outcomes of ERP implementation success: an explanatory study. In: Ziemba, E. (ed.) AITM/ISM-2016. LNBIP, vol. 277, pp. 157–178. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-53076-5_9

    Chapter  Google Scholar 

  23. Miller, G.J.: Decision support project: project success and organizational performance. Project and program management. DBA thesis, p. 465. SKEMA Business School, Lille, France (2018)

    Google Scholar 

  24. Barki, H., Hartwick, J.: Measuring user participation, user involvement, and user attitude. MIS Q. 18, 59–82 (1994). https://doi.org/10.2307/249610

    Article  Google Scholar 

  25. Shenhar, A., Dvir, D.: Reinventing Project Management: The Diamond Approach to Successful Growth and Innovation. Harvard Business School Press, Boston (2007)

    Google Scholar 

  26. Debortoli, S., Müller, O., Vom Brocke, J.P.D.: Comparing business intelligence and big data skills. Bus. Inform. Syst. Eng. 6, 289–300 (2014). https://doi.org/10.1007/s12599-014-0344-2

    Article  Google Scholar 

  27. Barki, H., Huff, S.L.: Change, attitude to change, and decision support system success. Inf. Manag. 9, 261–268 (1985). https://doi.org/10.1016/0378-7206(85)90050-3

    Article  Google Scholar 

  28. DeLone, W.H., McLean, E.R.: Information systems success: the quest for the dependent variable. Inf. Syst. Res. 3, 60–95 (1992). https://doi.org/10.1287/isre.3.1.60

    Article  Google Scholar 

  29. Wurpts, I.C., Geiser, C.: Is adding more indicators to a latent class analysis beneficial or detrimental? Results of a Monte-Carlo study. Front. Psychol. 5, 920 (2014). https://doi.org/10.3389/fpsyg.2014.00920

    Article  Google Scholar 

  30. Eom, S.B., Lee, S.M., Ayaz, A.: Expert systems applications development research in business: a selected bibliography (1975–1989). Eur. J. Oper. Res. 68, 278–290 (1993). https://doi.org/10.1016/0377-2217(93)90309-B

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. maxmetrics, Heidelberg, Germany

    Gloria J. Miller

Authors
  1. Gloria J. Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gloria J. Miller .

Editor information

Editors and Affiliations

  1. University of Economics in Katowice, Katowice, Poland

    Ewa Ziemba

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Miller, G.J. (2019). Quantitative Comparison of Big Data Analytics and Business Intelligence Project Success Factors. In: Ziemba, E. (eds) Information Technology for Management: Emerging Research and Applications. AITM ISM 2018 2018. Lecture Notes in Business Information Processing, vol 346. Springer, Cham. https://doi.org/10.1007/978-3-030-15154-6_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-15154-6_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15153-9

  • Online ISBN: 978-3-030-15154-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation