Outline

Gliederung

Business Strategies for Data Monetization: Deriving Insights from Practice
Julius Baecker, Martin Engert, Matthias Pfaff and Helmut Krcmar

Bibtex

Cite as text

						@Select Types{,
							 
							 
							 
							 
							 
							Journal   = "Band-1",
							 Title= "Business Strategies for Data Monetization: Deriving Insights from Practice", 
							Author= "Julius Baecker, Martin Engert, Matthias Pfaff and Helmut Krcmar", 
							Doi= "https://doi.org/10.30844/wi_2020_j3-baecker", 
							 Abstract= "Although increases in available data have inspired companies’ interest in creating and extracting value from it, many lack the insight and guidance to assess the potential data offer. To address this issue, we conduct a systematic literature review to create a universe of 102 real-world cases from diverse industries with regard to the use of data. Based on an analysis of these cases, this paper provides a set of 12 generic strategies for monetizing data, ranging from sole asset sale to strategically opening data and guaranteeing control. This study supports business practice by aggregating the wide range of established approaches of data monetization from practice for operational purposes. It advances theoretical understanding of value capturing from data and suggests important avenues for future work in this emerging field of research.

", 
							 Keywords= "Data monetization, data-driven business models, big data, datadriven decision making", 
							}
					
Julius Baecker, Martin Engert, Matthias Pfaff and Helmut Krcmar: Business Strategies for Data Monetization: Deriving Insights from Practice. Online: https://doi.org/10.30844/wi_2020_j3-baecker (Abgerufen 23.11.24)
https://doi.org/10.30844/wi_2020_j3-baecker

Open Access

Abstract

Abstract

Although increases in available data have inspired companies’ interest in creating and extracting value from it, many lack the insight and guidance to assess the potential data offer. To address this issue, we conduct a systematic literature review to create a universe of 102 real-world cases from diverse industries with regard to the use of data. Based on an analysis of these cases, this paper provides a set of 12 generic strategies for monetizing data, ranging from sole asset sale to strategically opening data and guaranteeing control. This study supports business practice by aggregating the wide range of established approaches of data monetization from practice for operational purposes. It advances theoretical understanding of value capturing from data and suggests important avenues for future work in this emerging field of research.

Keywords

Schlüsselwörter

Data monetization, data-driven business models, big data, datadriven decision making

References

Referenzen

1. Schüritz, R., Satzger, G.: Patterns of data-infused business model innovation. IEEE 18th Conference on Business Informatics (CBI), vol. 1, pp. 133-142. IEEE, Paris, France (2016)
2. Najjar, M.S., Kettinger, W.J.: Data Monetization: Lessons from a Retailer’s Journey. MIS Quarterly Executive 12, 213-225 (2013)
3. Koutris, P., Upadhyaya, P., Balazinska, M., Howe, B., Suciu, D.: Query-based data pricing. Journal of the ACM 62, 43 (2015)
4. Wixom, B.H., Ross, J.W.: How to monetize your data. MIT Sloan Management Review 58, (2017)
5. Gartner IT Glossary: Data Monetization. https://www.gartner.com/it-glossary/datamonetization accessed 08.06.2019, (2019)
6. Moro Visconti, R., Larocca, A., Marconi, M.: Big Data-Driven value chains and digital platforms: from Value Co-Creation to Monetization. (2017)
7. Kart, L., Heudecker, N., Buytendijk, F.: Survey analysis: big data adoption in 2013 shows substance behind the hype. Gartner Report GG0255160 13, (2013)
8. McKinsey Analytics: Fueling growth through data monetization. (2018)
9. Thomas, L.D., Leiponen, A.: Big data commercialization. IEEE Engineering Management Review 44, 74-90 (2016)
10. Günther, W.A., Mehrizi, M.H.R., Huysman, M., Feldberg, F.: Debating big data: A literature review on realizing value from big data. The Journal of Strategic Information Systems 26, 191-209 (2017)
11. Woerner, S.L., Wixom, B.H.: Big data: extending the business strategy toolbox. Journal of Information Technology 30, 60-62 (2015)
12. Hartmann, P.M., Zaki, M., Feldmann, N., Neely, A.: Capturing value from big data – a taxonomy of data-driven business models used by start-up firms. International Journal of Operations and Production Management 36, 1382-1406 (2016)
13. Alfaro, E., Bressan, M., Girardin, F., Murillo, J., Someh, I., Wixom, B.H.: BBVA’s Data Monetization Journey. MIS Quarterly Executive 18, (2019)
14. Schaefer, D., Walker, J., Flynn, J.: A Data-Driven Business Model Framework for Value Capture in Industry 4.0. Advances in Manufacturing Technology XXXI: Proceedings of the 15th International Conference on Manufacturing Research, Incorporating the 32nd National Conference on Manufacturing Research, vol. 6, pp. 245-250. IOS Press, University of Greenwich, UK (2017)
15. Cheah, S., Wang, S.: Big data-driven business model innovation by traditional industries in the Chinese economy. Journal of Chinese Economic and Foreign Trade Studies 10, 229-251 (2017)
16. Herterich, M.M., Uebernickel, F., Brenner, W.: Stepwise Evolution of Capabilities for Harnessing Digital Data Streams in Data-Driven Industrial Services. MIS Quarterly Executive 15, 299-320 (2016)
17. Trabucchi, D., Buganza, T., Pellizzoni, E.: Give Away Your Digital Services: Leveraging Big Data to Capture Value. Research Technology Management 60, 43-52 (2017)
18. Sun, T., Wang, M., Liang, Z.: Predictive modeling of potential customers based on the customers clickstream data: A field study. IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 2221-2225. IEEE, Singapore (2017)
19. Bühler, J., Baur, A.W., Bick, M., Shi, J.: Big data, big opportunities: Revenue sources of social media services besides advertising. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9373, pp. 183-199 (2015)
20. Alvertis, I., Petychakis, M., Tsouroplis, R., Biliri, E., Lampathaki, F., Askounis, D., Kastrinogianins, T., Daskalopoulos, A., Michalareas, T., Robson, E., MacCarthy, D., O’Meara, C., Radziwonowicz, L., Kleinfeld, R.: Trusted, fair multi-segment business models, enabled by a user-centric, privacy-aware platform, for a data-driven era. CEUR Workshop Proceedings, vol. 1367, pp. 1-8, Stockholm, Schweden (2015)
21. Lycett, M.: ‘Datafication’: making sense of (big) data in a complex world. European Journal of Information Systems 22, 381-386 (2013)
22. Chen, H.-M., Schütz, R., Kazman, R., Matthes, F.: Amazon in the air: Innovating with big data at Lufthansa. 49th Hawaii International Conference on System Sciences (HICSS), pp. 5096-5105. IEEE, Hawaii, USA (2016)
23. Schreieck, M., Wiesche, M.: How established companies leverage IT platforms for value co-creation–Insights from banking. 25th European Conference on Information Systems (ECIS), Guimarães, Portugal (2017)
24. Magalhaes, G., Roseira, C.: Open government data and the private sector: An empirical view on business models and value creation. Government Information Quarterly (2017)
25. Linna, P., Makinen, T., Yrjonkoski, K.: Open data based value networks: Finnish examples of public events and agriculture. 40th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2017, pp. 1448- 1453, Opatija, Croatia (2017)
26. Lindman, J., Kinnari, T., Rossi, M.: Industrial open data: Case studies of early open data entrepreneurs. Proceedings of the Annual Hawaii International Conference on System Sciences, pp. 739-748, Hawaii, USA (2014)
27. Janssen, M., Zuiderwijk, A.: Infomediary Business Models for Connecting Open Data Providers and Users. Social Science Computer Review 32, 694-711 (2014)
28. Zimmermann, H.D., Pucihar, A.: Open innovation, open data and new business models. IDIMT 2015: Information Technology and Society – Interaction and Interdependence – 23rd Interdisciplinary Information Management Talks, pp. 449-458, Podebrady, Czech Republic (2015)
29. Yu, S., Yang, D.: The Role of Big Data Analysis in New Product Development. International Conference on Network and Information Systems for Computers (ICNISC), pp. 279-283. IEEE, Wuhan, China (2016)
30. Chaudhary, R., Pandey, J.R., Pandey, P.: Business model innovation through big data. International Conference on Green Computing and Internet of Things (ICGCIoT), pp. 259- 263. IEEE, Delhi, India (2015)
31. Pellegrini, T., Dirschl, C., Eck, K.: Linked data business cube: a systematic approach to semantic web business models. 18th International Academic MindTrek Conference: Media Business, Management, Content & Services, pp. 132-141. ACM, Tampere, Finland (2014)
32. Otto, B., Aier, S.: Business models in the data economy: A case study from the business partner data domain. Wirtschaftsinformatik Proceedings 2013.30. AIS Electronic Library (2013)
33. Anand, A., Sharma, R., Coltman, T.: Four steps to realizing business value from digital data streams. MIS Quarterly Executive: a research journal dedicated to improving practice 15, 259-277 (2016)
34. Grover, V., Chiang, R.H., Liang, T.-P., Zhang, D.: Creating Strategic Business Value from Big Data Analytics: A Research Framework. Journal of Management Information Systems 35, 388-423 (2018)
35. Sorescu, A.: Data-Driven Business Model Innovation. Journal of Product Innovation Management 34, 691-696 (2017)
36. Zolnowski, A., Christiansen, T., Gudat, J.: Business model transformation patterns of datadriven innovations. 24th European Conference on Information Systems, ECIS 2016, Istanbul, Turkey (2016)
37. Pousttchi, K., Hufenbach, Y.: Enabling evidence-based retail marketing with the use of payment data – the mobile payment reference model 2.0. International Journal of Business Intelligence and Data Mining 8, 19-44 (2013)
38. Elvy, S.A.: Paying for privacy and the personal data economy. Columbia Law Review 117, 1369-1460 (2017)
39. Walker, R.: From big data to big profits: Success with data and analytics. Oxford University Press (2015)
40. Schüritz, R., Seebacher, S., Dorner, R.: Capturing value from data: revenue models for data-driven services. Proceedings of the 50th Hawaii International Conference on System Sciences, (2017)
41. Weking, J., Hein, A., Böhm, M., Krcmar, H.: A hierarchical taxonomy of business model patterns. Electronic Markets 1-22 (2018)
42. Jurisch, M.C., Wolf, P., Krcmar, H.: Using the Case Survey Method for Synthesizing Case Study Evidence in Information Systems Research. Nineteenth Americas Conference on Information Systems (AMCIS), (2013)
43. Yin, R.K.: Case study research and applications: Design and methods. Sage publications (2017)
44. Wiesche, M., Jurisch, M.C., Yetton, P.W., Krcmar, H.: Grounded theory methodology in information systems research. MIS Quarterly 41, 685-701 (2017)
45. Soto Setzke, D., Böhm, M., Krcmar, H.: Platform Openness: A Systematic Literature Review and Avenues for Future Research. Fourteenth International Conference on Wirtschaftsinformatik.WI 2019, (2019)
46. Engert, M., Pfaff, M., Krcmar, H.: Adoption of Software Platforms: Reviewing Influencing Factors and Outlining Future Research. Twenty-Third Pacific Asia Conference on Information Systems: PACIS 2019, Xi’An, China (2019)
47. Schreieck, M., Wiesche, M., Krcmar, H.: Design and Governance of Platform Ecosystems- Key Concepts and Issues for Future Research. Twenty-Fourth European Conference on Information Systems: ECIS 2016, pp. ResearchPaper76 (2016)
48. Lee, S.U., Zhu, L., Jeffery, R.: A Contingency-Based Approach to Data Governance Design for Platform Ecosystems. Pacific Asia Conference on Information Systems (PACIS), (2018)

Most viewed articles

Meist angesehene Beiträge

GITO events | library.gito