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Future government data strategies: data-driven enterprise or data steward?: Exploring definitions and challenges for the government as data enterprise

Published:16 June 2020Publication History

ABSTRACT

Comparable to the concept of a data(-driven) enterprise, the concept of a ‘government as data (-driven) enterprise’ is gaining popularity as a data strategy. However, what it implies is unclear. The objective of this paper is to clarify the concept of the government as data (-driven) enterprise, and identify the challenges and drivers that shape future data strategies. Drawing on literature review and expert interviews, this paper provides a rich understanding of the challenges for developing sound future government data strategies. Our analysis shows that two contrary data strategies dominate the debate. On the one hand is the data-driven enterprise strategy that focusses on collecting and using data to improve or enrich government processes and services (internal orientation). On the other hand, respondents point to the urgent need for governments to take on data stewardship, so other parties can use data to develop value for society (external orientation). Since these data strategies are not mutually exclusive, some government agencies will attempt to combine them, which is very difficult to pull off. Nonetheless, both strategies demand a more data minded culture. Moreover, the successful implementation of either strategy requires mature data governance – something most organisations still need to master. This research contributes by providing more depth to these strategies. The main challenge for policy makers is to decide on which strategy best fits their agency's roles and responsibilities and develop a shared roadmap with the external actors while at the same time mature on data governance.

References

  1. H. Mintzberg, “The Strategy Concept I: Five Ps For Strategy.” California Management Review. Volume: 30 issue: 1, page(s): 11-24Google ScholarGoogle Scholar
  2. “UN E-Government Survey 2018.” [Online]. Available: https://publicadministration.un.org/egovkb/en-us/Reports/UN-E-Government-Survey-2018. [Accessed: 30-Dec-2019].Google ScholarGoogle Scholar
  3. G. H. Kim, S. Trimi, and J. H. Chung, “Big-data applications in the government sector,” Communications of the ACM, vol. 57, no. 3, pp. 78–85, 2014, doi: 10.1145/2500873.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. B. Klievink, B. J. Romijn, S. Cunningham, and H. de Bruijn, “Big data in the public sector: Uncertainties and readiness,” Information Systems Frontiers, vol. 19, no. 2, pp. 267–283, Apr. 2017, doi: 10.1007/s10796-016-9686-2.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. M. Janssen and G. Kuk, “Big and Open Linked Data (BOLD) in research, policy, and practice,” Journal of Organizational Computing and Electronic Commerce, vol. 26, no. 1–2, pp. 3–13, Apr. 2016, doi: 10.1080/10919392.2015.1124005.Google ScholarGoogle ScholarCross RefCross Ref
  6. B. van Loenen, S. Kulk, and H. Ploeger, “Data protection legislation: A very hungry caterpillar. The case of mapping data in the European Union,” Government Information Quarterly, vol. 33, no. 2, pp. 338–345, Apr. 2016, doi: 10.1016/j.giq.2016.04.002.Google ScholarGoogle ScholarCross RefCross Ref
  7. European Union, “Regulation 45/2001 of the European Parliament and of the Council of 18 December 2000 on the protection of individuals with regard to the processing of personal data by the Community institutions and bodies and on the free movement of such data,” Official Journal of the European Union, vol. L 8/1, no. November 2000. p. 22, 2001.Google ScholarGoogle Scholar
  8. M. Janssen and E. Estevez, “Lean government and platform-based governance—{Doing} more with less,” Government Information Quarterly, vol. 30, pp. S1–S8, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  9. T. Filer, “Thinking about GovTech A Brief Guide for Policymakers.”Google ScholarGoogle Scholar
  10. A. Luthfi and M. Janssen, “Open data for evidence-based decision-making: Data-driven government resulting in uncertainty and polarization,” International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 3, pp. 1071–1078, 2019, doi: 10.18517/ijaseit.9.3.8846.Google ScholarGoogle ScholarCross RefCross Ref
  11. S. Mouzakitis , “Challenges and opportunities in renovating public sector information by enabling linked data and analytics,” Information Systems Frontiers, vol. 19, no. 2, pp. 321–336, Apr. 2017, doi: 10.1007/s10796-016-9687-1.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. “Driving public sector innovation using big and open linked data (BOLD) | SpringerLink.” [Online]. Available: https://link.springer.com/article/10.1007/s10796-017-9746-2. [Accessed: 09-Jan-2020].Google ScholarGoogle Scholar
  13. D. Finfgeld-Connett and E. D. Johnson, “Literature search strategies for conducting knowledge-building and theory-generating qualitative systematic reviews,” Journal of Advanced Nursing, vol. 69, no. 1, pp. 194–204, Jan. 2013, doi: 10.1111/j.1365-2648.2012.06037.x.Google ScholarGoogle ScholarCross RefCross Ref
  14. S. K. Boell and D. Cecez-Kecmanovic, “On being ‘systematic’ in literature reviews in IS,” Journal of Information Technology, vol. 30, no. 2. Palgrave Macmillan Ltd., pp. 161–173, 28-Jun-2015, doi: 10.1057/jit.2014.26.Google ScholarGoogle ScholarCross RefCross Ref
  15. A. Martín-Martín, E. Orduna-Malea, M. Thelwall, and E. Delgado López-Cózar, “Google Scholar, Web of Science, and Scopus: A systematic comparison of citations in 252 subject categories,” Journal of Informetrics, vol. 12, no. 4, pp. 1160–1177, Nov. 2018, doi: 10.1016/j.joi.2018.09.002.Google ScholarGoogle ScholarCross RefCross Ref
  16. C. Dearnley, “A reflection on the use of semi-structured interviews,” Nurse Researcher, vol. 13, no. 1, pp. 19–28, 2005, doi: 10.7748/nr2005.07.13.1.19.c5997.Google ScholarGoogle ScholarCross RefCross Ref
  17. D. W. Turner, “The Qualitative Report Qualitative Interview Design: A Practical Guide for Novice Investigators.”Google ScholarGoogle Scholar
  18. P. Åstedt-Kurki and R.-L. Heikkinen, “Two approaches to the study of experiences of health and old age: the thematic interview and the narrative method,” Journal of Advanced Nursing, vol. 20, pp. 418–421, 1994, doi: 10.1111/j.1365-2648.1994.tb02375.x.Google ScholarGoogle ScholarCross RefCross Ref
  19. L. S. Whiting, “Semi-structured interviews: guidance for novice researchers.,” Nursing Standard, vol. 22, no. 23, pp. 35–40, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  20. S. Krauss, A. Hamzah, Z. Omar, T. Suandi, I. A. Ismail, and M. Z. Zahari, “Preliminary Investigation and Interview Guide Development for Studying how Malaysian Farmers Form their Mental Models of Farming,” 2009.Google ScholarGoogle Scholar
  21. M. Berndtsson, D. Forsberg, D. Stein, and T. Svahn, “Becoming a data-driven organisation,” in 26th European Conference on Information Systems: Beyond Digitization - Facets of Socio-Technical Change, ECIS 2018, 2018, doi: 10.1007/978-3-662-60304-8.Google ScholarGoogle Scholar
  22. M. Berndtsson, D. Forsberg, D. Stein, and T. Svahn, “Becoming a data-driven organisation,” in 26th European Conference on Information Systems: Beyond Digitization - Facets of Socio-Technical Change, ECIS 2018, 2018, doi: 10.1007/978-3-662-60304-8.Google ScholarGoogle Scholar
  23. I. Alhassan, D. Sammon, and M. Daly, “Data governance activities: an analysis of the literature,” Journal of Decision Systems, vol. 25, no. sup1, pp. 64–75, Jun. 2016, doi: 10.1080/12460125.2016.1187397.Google ScholarGoogle ScholarCross RefCross Ref
  24. B. Heinrich, D. Hristova, M. Klier, A. Schiller, and M. Szubartowicz, “Requirements for Data Quality Metrics,” Journal of Data and Information Quality, vol. 9, no. 2, pp. 1–32, Jan. 2018, doi: 10.1145/3148238.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. R. Vidgen, S. Shaw, and D. B. Grant, “Management challenges in creating value from business analytics,” European Journal of Operational Research, vol. 261, no. 2, pp. 626–639, Sep. 2017, doi: 10.1016/j.ejor.2017.02.023.Google ScholarGoogle ScholarCross RefCross Ref
  26. E. Masa, P. Busch, G. Guzman, and L. Sanzogni, “Qualitative analysis of big data analytics in the oil & gas industry,” in Proceedings of the 31st International Business Information Management Association Conference, IBIMA 2018: Innovation Management and Education Excellence through Vision 2020, 2018, pp. 287–306.Google ScholarGoogle Scholar
  27. A. T. Chatfield, V. N. Shlemoon, W. Redublado, and F. Rahman, “Data scientists as game changers in big data environments,” in Proceedings of the 25th Australasian Conference on Information Systems, ACIS 2014, 2014.Google ScholarGoogle Scholar
  28. O. E. Williamson, “Transaction cost economics: How it works; where it is headed,” Economist, vol. 146, no. 1, pp. 23–58, 1998, doi: 10.1023/A:1003263908567.Google ScholarGoogle ScholarCross RefCross Ref
  1. Future government data strategies: data-driven enterprise or data steward?: Exploring definitions and challenges for the government as data enterprise

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    • Published in

      cover image ACM Other conferences
      dg.o '20: The 21st Annual International Conference on Digital Government Research
      June 2020
      389 pages
      ISBN:9781450387910
      DOI:10.1145/3396956

      Copyright © 2020 ACM

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      • Published: 16 June 2020

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