Citizen Data Scientist: A Design Science Research Method for the Conduct of Data Science Projects

  • Matthew T. MullarkeyEmail author
  • Alan R. Hevner
  • T. Grandon Gill
  • Kaushik Dutta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11491)


Firms are seeking to gain greater understanding of and insights into more and more massive quantities of data collected and stored in disparate public and private databases. To effectively and efficiently deploy project resources to the data science search activity and to consequently build and evaluate innovative artifacts, firms are finding that a Design Science Research (DSR) approach can extend into the Data Science (DS) project domain through an iterative, evaluative project management method for the diagnosing, design, implementation, and evolution of data science artifacts. Importantly, DSR also provides a guided, emergent search paradigm that can be integral to finding hidden insights in massive data where the problem and solution domains are both frequently poorly understood at the outset of the DS inquiry. This article examines a case for using the elaborated action design research (eADR) method to inform the DS project management (PM) approach in situ with a Fortune 100 Global Manufacturer. The innovative DS PM approach resulted in multiple innovative DS solution artifacts built and evaluated by a dozen DS PM teams at the firm over the first two years of the DS PM deployment.


Design science Data science Action design research Digital innovation Informal learning Data science project management 


  1. Gregor, S., Hevner, A.: Positioning and presenting design science research for maximum impact. Manage. Inf. Syst. Q. 37(2), 337–355 (2013)CrossRefGoogle Scholar
  2. Hevner, A., March, S., Park, J., Ram, S.: Design science research in information systems. Manage. Inf. Syst. Q. 28(1), 75–105 (2004)CrossRefGoogle Scholar
  3. Kolb, D.A.: Learning Style Inventory, Revised Edition. McBer & Company, Boston, MA (1985)Google Scholar
  4. Lohr, S.: Where STEM jobs are (and where they aren’t). New York Times, 1 November 2017.
  5. Markow, W., Braganza, S., Taska, B., Miller, S., Hughes, D.: The Quant Crunch: how demand for data science skills is disrupting the job market. Burning Glass Technologies, pp. 1–25 (2017)Google Scholar
  6. Moore S.: Gartner says more than 40 percent of data science tasks will be automated by 2020, 16 January 2017.
  7. Mullarkey, M.T., Hevner, A.R.: Entering action design research. In: Donnellan, B., Helfert, M., Kenneally, J., VanderMeer, D., Rothenberger, M., Winter, R. (eds.) DESRIST 2015. LNCS, vol. 9073, pp. 121–134. Springer, Cham (2015). Scholar
  8. Mullarkey, M.T., Hevner, A.R.: An elaborated action design research process model. Eur. J. Inf. Syst. 28(1), 6–20 (2019)CrossRefGoogle Scholar
  9. Sein, M., Henfridsson, O., Purao, S., Rossi, M., Lindgren, R.: Action design research. MIS Q. 35(1), 37–56 (2011)CrossRefGoogle Scholar
  10. Weimer, M.: Learner-centered teaching and transformative learning. In: Taylor, E.W. (ed.) The Handbook of Transformative Learning: Theory, Research, and Practice, pp. 439–454. Jossey Bass, San Francisco (2012)Google Scholar
  11. Wood, D.M.: Learning from experience through reflection. Org. Dyn. 24(3), 3648 (1996)MathSciNetGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Matthew T. Mullarkey
    • 1
    • 2
    Email author
  • Alan R. Hevner
    • 1
  • T. Grandon Gill
    • 1
  • Kaushik Dutta
    • 1
  1. 1.Information Systems and Decision Science Department, Muma College of BusinessUniversity of South FloridaTampaUSA
  2. 2.Extraordinary Research Scientist, Workwell: Research Unit for Economic & Management SciencesNorth-West UniversityPotchefstroomSouth Africa

Personalised recommendations