Pricing of Data Products in Data Marketplaces

  • Samuel A. FrickerEmail author
  • Yuliyan V. Maksimov
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 304)


Mobile computing and the Internet of Things promises massive amounts of data for big data analytic and machine learning. A data sharing economy is needed to make that data available for companies that wish to develop smart systems and services. While digital markets for trading data are emerging, there is no consolidated understanding of how to price data products and thus offer data vendors incentives for sharing data. This paper uses a combined keyword search and snowballing approach to systematically review the literature on the pricing of data products that are to be offered on marketplaces. The results give insights into the maturity and character of data pricing. They enable practitioners to select a pricing approach suitable for their situation and researchers to extend and mature data pricing as a topic.


Data pricing Data marketplace Systematic literature review 



The presented work was funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 732204 (Bonseyes) and by the Swiss State Secretariat for Education‚ Research and Innovation (SERI) under contract number 16.0159. The opinions expressed and arguments employed herein do not necessarily reflect the official views of these funding bodies.


  1. 1.
    Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)CrossRefzbMATHGoogle Scholar
  2. 2.
    LeCun, Y., Bengio, Y., Hinto, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  3. 3.
    Schomm, F., Stahl, F., Vossen, G.: Marketplaces for data: an initial survey. ACM SIGMOD Record 42(1), 15–26 (2013)CrossRefGoogle Scholar
  4. 4.
    Schwab, K., et al.: Personal data: the emergence of a new asset class. World Economic Forum (2011)Google Scholar
  5. 5.
    Koutsopoulos, I., Gionis, A., and Halkidi, M.: Auctioning data for learning. In: IEEE 15th International Conference on Data Mining Workshops, Sydney, Australia (2015)Google Scholar
  6. 6.
    Muschalle, A., Stahl, F., Löser, A., Vossen, G.: Pricing approaches for data markets. In: International Workshop on Business Intelligence for the Real-Time Enterprise (2012)Google Scholar
  7. 7.
    Kittlaus, H.-B., Clough, P.: Software Product Management and Pricing. Springer, Heidelberg (2009). doi: 10.1007/978-3-540-76987-3 Google Scholar
  8. 8.
    Wohlin, C.: Guidelines for snowballing in systematic literature studies and a replication in software engineering. In: 18th International Conference on Evaluation and Assessment in Software Engineering (2013)Google Scholar
  9. 9.
    Kitchenham, B., Charters, S.: Guidelines for performing systematic literature reviews in software engineering. In: EBSE Technical Report (2007)Google Scholar
  10. 10.
    Petersen, K., Feldt, R., Mujtaba, S., Mattson, M.: Systematic mapping studies in software engineering. In: 12th International Conference on Evaluation and Assessment in Software Engineering (2008)Google Scholar
  11. 11.
    Ivarsson, M., Gorschek, T.: Technology transfer decision support in requirements engineering research: a systematic review of REj. Requirements Eng. 14(3), 155–175 (2009)CrossRefGoogle Scholar
  12. 12.
    Frank, R., Cartwright, E.: Microeconomics and Behaviour. McGraw-Hill Education, London (2013)Google Scholar
  13. 13.
    Nagle, T.T., Hogan, J.E.: The Strategy and Tactics of Pricing: A Guide to Growing More Profitably. Pearson Prentice Hall, Upper Saddle River (2006)Google Scholar
  14. 14.
    Sarkar, P.: Data as a Service - Framework for Providing Re-Usable Enterprise Data Services. Wiley, Hoboken (2015)CrossRefGoogle Scholar
  15. 15.
    Battini, C., Scannapieco, M.: Data Quality: Concepts, Methodologies and Techniques. Springer, Heidelberg (2010). doi: 10.1007/3-540-33173-5 Google Scholar
  16. 16.
    Hsieh, H.F., Shannon, S.E.: Three approaches to qualitative content analysis. Qual. Health Res. 15(9), 1277–1288 (2005)CrossRefGoogle Scholar
  17. 17.
    Nurdiani, I., Börstler, J., Fricker, S.: The impact of agile and lean practices on project constraints: a tertiary study. J. Syst. Softw. 119, 162–183 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.i4Ds Centre for Requirements EngineeringUniversity of Applied Sciences Northwestern Switzerland (FHNW)WindischSwitzerland
  2. 2.Software Engineering Research Laboratory (SERL-Sweden)Blekinge Institute of TechnologyKarlskronaSweden

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