Skip to main content
SpringerLink
Log in

Supporting the Development and Realization of Data-Driven Business Models with Enterprise Architecture Modeling and Management

  • Conference paper
  • First Online:
Business Information Systems (BIS 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 389))

Included in the following conference series:

Abstract

Designing and realizing data-driven business models (DDBMs) are key challenges for many enterprises and are recent research topics. While enterprise architecture (EA) modeling and management proved their potential value for supporting information technology-related projects, EA’s specific role in developing and realizing DDBMs is a new and rather unexplored research field. We conducted a systematic literature review on big data, business models, and EA to identify the potentials of EA support for developing and realizing DDBMs. We derived 42 EA concerns from the literature, structured along the dimensions of the business model canvas and the status of realization (as-is, to-be).

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

Access this chapter

Log in via an institution
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

References

  1. Brynjolfsson, E., McAfee, A.: Big data: the management revolution. Harvard Bus. Rev. 10, 1–12 (2012)

    Google Scholar 

  2. Redman, T.C.: Do your data scientists know the “why” behind their work? (2019). https://hbr.org/2019/05/do-your-data-scientists-know-the-why-behind-their-work

  3. Günther, W.A., Rezazade Mehrizi, M.H., Huysman, M., Feldberg, F.: Debating big data: a literature review on realizing value from big data. J. Strategic Inf. Syst. 26(3), 191–209 (2017)

    Article  Google Scholar 

  4. Hartmann, P.M., Zaki, M., Feldmann, N., Neely, A.: Big data for big business? A taxonomy of data-driven business models used by start-up firms. Cambridge Service Alliance (2014)

    Google Scholar 

  5. Kühne, B., Böhmann, T.: Requirements for representing data-driven business models – towards extending the Business Model Canvas. In: Twenty-Fourth Americas Conference on Information Systems, pp. 1–10. AIS, New Orleans (2018)

    Google Scholar 

  6. Vanauer, M., Bohle, C., Hellingrath, B.: Guiding the introduction of big data in organizations: a methodology with business- and data-driven ideation and enterprise architecture management-based implementation. In: 48th Hawaii International Conference on System Science, pp. 908–917. IEEE, Hawaii (2015)

    Google Scholar 

  7. Chen, H., Chiang, R.H.L., Storey, V.C.: Business intelligence and analytics: from big data to big impact. MIS Q. 36(4), 1165–1188 (2012)

    Article  Google Scholar 

  8. Engelbrecht, A., Gerlach, J., Widjaja, T.: Understanding the anatomy of data-driven business models – towards an empirical taxonomy. In: Twenty-Fourth European Conference on Information Systems, pp. 1–15. ECIS, Turkey (2016)

    Google Scholar 

  9. Bulger, M., Taylor, G., Schroeder, R.: Data-driven business models: challenges and opportunities of big data. Oxford Internet Institute (2014)

    Google Scholar 

  10. Brownlow, J., Zaki, M., Neely, A., Urmetzer, F.: Data and analytics – data-driven business models: a blueprint for innovation. Cambridge Service Alliance (2015)

    Google Scholar 

  11. Schuritz, R., Satzger, G.: Patterns of data-infused business model innovation. In: 18th IEEE Conference on Business Informatics, vol. 1, pp. 133–142. IEEE, Paris (2016)

    Google Scholar 

  12. Osterwalder, A., Pigneur, Y.: Business Model Generation: A Handbook for Visionaries, Game Changers and Challengers. Wiley, Hoboken (2010)

    Google Scholar 

  13. Winter, R., Fischer, R.: Essential layers, artifacts, and dependencies of enterprise architecture. J. Enterp. Archit. 3(2), 7–18 (2007)

    Google Scholar 

  14. Zachman, J.A.: Zachman International (2008). https://zachman.com/about-the-zachman-framework. Accessed 12 Nov 2019

  15. Federation of EA Professional Organizations: a common perspective on enterprise architecture. Architecture and Governance Magazine, pp. 1–12 (2013)

    Google Scholar 

  16. The Open Group: TOGAF. https://www.opengroup.org/togaf. Accessed 06 Oct 2019

  17. Musulin, J., Strahonja, V.: Business model grounds and links: towards enterprise architecture perspective. J. Inf. Organ. Sci. 42(2), 241–269 (2018)

    Google Scholar 

  18. Burmeister, F., Drews, P., Schirmer, I.: Towards an extended enterprise architecture meta-model for big data – a literature-based approach. In: Twenty-Fourth Americas Conference on Information Systems (AMCIS), pp. 1–10. AIS, New Orleans (2018)

    Google Scholar 

  19. vom Brocke, J., Simons, A., Niehaves, B., Reimer, K., Plattfaut, R., Cleven, A.: Reconstructing the Giant: on the importance of rigour in documenting the literature search process. In: European Conference on Information Systems, pp. 2206–2217. ECIS, Verona (2009)

    Google Scholar 

  20. Chen, H.-M., Kazman, R., Garbajosa, J., Gonzalez, E.: Big data value engineering for business model innovation. In: 50th Hawaii International Conference on System Sciences, pp. 5921–5930. IEEE, Hawaii (2017)

    Google Scholar 

  21. Uzzle, L.: Using metamodels to improve enterprise architecture. J. Enterp. Archit. 5(1), 49–61 (2009)

    Google Scholar 

  22. Kühne, B., Zolnowski, A., Böhmann, T.: Making data tangible for data-driven innovations in a business model context DSR methodology view project service dominant architecture view project. In: Twenty-Fifth Americas Conference on Information Systems, pp. 1–10. AIS, Cancun (2019)

    Google Scholar 

  23. Hunke, F., Seebacher, S., Schuritz, R., Illi, A.: Towards a process model for data-driven business model innovation. In: 19th Conference on Business Informatics, CBI, vol. 1, pp. 150–157. IEEE, Thessaloniki (2017)

    Google Scholar 

  24. Zolnowski, A., Anke, J., Gudat, J.: Towards a cost-benefit-analysis of data-driven business models. In: 13th International Conference on Wirtschaftsinformatik, pp. 181–195. WI, St. Gallen (2017)

    Google Scholar 

  25. Kearny, C., Gerber, A., Van Der Merwe, A.: Data-driven enterprise architecture and the TOGAF ADM phases. International Conference on Systems. Man, and Cybernetics, pp. 4603–4608. IEEE, Hungary (2017)

    Google Scholar 

  26. Kehrer, S., Jugel, D., Zimmermann, A.: Categorizing requirements for enterprise architecture management in big data literature. In: 20th International Enterprise Distributed Object Computing Workshop, pp. 98–105. IEEE, Vienna (2016)

    Google Scholar 

  27. Lněnička, M., Máchová, R., Komárková, J., Čermáková, I.: Components of big data analytics for strategic management of enterprise architecture. In: 12th International Conference on Strategic Management and Its Support by Information Systems, pp. 398–406. Curran Associates, Inc., Ostrava (2017)

    Google Scholar 

  28. Lnenicka, M., Komarkova, J.: Developing a government enterprise architecture framework to support the requirements of big and open linked data with the use of cloud computing. Int. J. Inform. Manag. 46, 124–141 (2019)

    Article  Google Scholar 

  29. Bouwman, H., De Reuver, M., Solaimani, S., Daas, D., Haaker, T., Janssen, W., Iske, P., Walenkamp, B.: Business models tooling and a research agenda. In: 25th Bled eConference – The First 25 Years of the Bled eConference, pp. 235–257. AIS, Bled (2012)

    Google Scholar 

  30. Petrikina, J., Drews, P., Schirmer, I., Zimmermann, K.: Integrating business models and enterprise architecture. In: 18th International Enterprise Distributed Object Computing Conference Workshops and Demonstrations Integrating, pp. 47–56. IEEE, Washington (2014)

    Google Scholar 

  31. Kühne, B., Böhmann, T.: Data-driven business models – building the bridge between data and value. In: 27th European Conference on Information Systems, pp. 1–16. ECIS, Stockholm & Uppsala (2019)

    Google Scholar 

  32. Exner, K., Stark, R., Kim, J.Y.: Data-driven business model: A methodology to develop smart services. International Conference on Engineering. Technology and Innovation, vol. 2018, pp. 146–154. IEEE, Madeira Island (2018)

    Google Scholar 

  33. Dremel, C., Wulf, J.: Towards a capability model for big data analytics. In: 13th International Conference on Wirtschaftsinformatik, pp. 1141–1155. WI, St. Gallen (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Information Systems, Leuphana University of Lüneburg, Universitätsallee 1, 21335, Lüneburg, Germany

    Faisal Rashed & Paul Drews

Authors
  1. Faisal Rashed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paul Drews
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Faisal Rashed .

Editor information

Editors and Affiliations

  1. Poznań University of Economics and Business, Poznan, Poland

    Witold Abramowicz

  2. University of Colorado, Colorado Springs, CO, USA

    Gary Klein

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rashed, F., Drews, P. (2020). Supporting the Development and Realization of Data-Driven Business Models with Enterprise Architecture Modeling and Management. In: Abramowicz, W., Klein, G. (eds) Business Information Systems. BIS 2020. Lecture Notes in Business Information Processing, vol 389. Springer, Cham. https://doi.org/10.1007/978-3-030-53337-3_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-53337-3_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-53336-6

  • Online ISBN: 978-3-030-53337-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation