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Data Products

  • Jürg MeierhoferEmail author
  • Thilo Stadelmann
  • Mark Cieliebak
Chapter

Abstract

Data science is becoming an established scientific discipline and has delivered numerous useful results so far. We are at the point in time where we begin to understand what results and insights data science can deliver; at the same time, however, it is not yet clear how to systematically deliver these results for the end user. In other words: how do we design data products in a process that has relevant guaranteed benefit for the user? Additionally, once we have a data product, we need a way to provide economic value for the product owner. That is, we need to design data-centric business models as well.

In this chapter, we propose to view the all-encompassing process of turning data insights into data products as a specific interpretation of service design. This provides the data scientist with a rich conceptual framework to carve the value out of the data in a customer-centric way and plan the next steps of his endeavor: to design a great data product.

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References

  1. Berners-Lee, T. (2006). Linked data. Blog post. https://www.w3.org/DesignIssues/LinkedData.html
  2. Caffo, B. (2015). Developing data products. MOOC at Johns Hopkins University, Coursera. https://www.coursetalk.com/providers/coursera/courses/developing-data-products
  3. Federal Assembly of the Swiss Confederation. (1992). Federal act of 19 June 1992 on data protection (FADP), paragraph 3. https://www.admin.ch/opc/en/classified-compilation/19920153/index.html
  4. Finlay, S. (2014). Predictive analytics, data mining and big data: Myths, misconceptions and methods. Basingstoke: Palgrave Macmillan.CrossRefGoogle Scholar
  5. Howard, J., Zwemer, M., & Loukides, M. (2012). Designing great data products – the drivetrain approach: A four-step process for building data products. Retrieved March 28, 2012, from https://www.oreilly.com/ideas/drivetrain-approach-data-products
  6. Jagdish, N., Sheth, B., & Newman, I. (1991). Why we buy what we buy: A theory of consumption values. Journal of Business Research, 22(2), 159–170.CrossRefGoogle Scholar
  7. Kottler, P. (2003). Marketing management. Upper Saddle River, NJ: Prentice Hall.Google Scholar
  8. Lohr, S. (2014). For big-data scientists, ‘janitor work’ is key hurdle to insights. The New York Times, blog post. https://mobile.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html
  9. Loukides, M. (2010). What is data science?, blog post. https://www.oreilly.com/ideas/what-is-data-science
  10. Loukides, M. (2011). The evolution of data products. O’Reilly Media. ISBN 978-1-449-31651-8.Google Scholar
  11. Lusch, R. F., & Vargo, S. L. (2014). Service-dominant logic. Cambridge: Cambridge University Press.Google Scholar
  12. Marr, B. (2016). Big data in practice: how 45 successful companies used big data analytics to deliver extraordinary results. Chichester: Wiley. ISBN 978-1-119-23138-7 (hbk).Google Scholar
  13. Meierhofer, J. (2017). Service value creation using data science. In E. Gumesson, C. Mele, & F. Polese (Eds.), Service dominant logic, network and systems theory and service science: Integrating three perspectives for a new service agenda. Rome: Youcanprint Self-Publishing.Google Scholar
  14. Meierhofer, J., & Meier, K. (2017). From data science to value creation. In Proceedings of International Conference on Exploring Service Science 2017.Google Scholar
  15. Osterwalder, A., Pigneur, Y., Bernarda, G., & Smith, A. (2014, November). Value proposition design. Hoboken, NJ: Wiley.Google Scholar
  16. Polaine, A., Løvlie, L., & Reason, B. (2013). Service design – From insight to implementation. Brooklyn, NY: Rosenfeld Media.Google Scholar
  17. Provost, F. P., & Fawcett, T. (2013). Data science for business. Sebastopol, CA: O’Reilly and Associates.zbMATHGoogle Scholar
  18. Scherer, J. O., Kloeckner, A. P., Duarte Ribeiro, J. L., Pezzotta, G., & Pirola, F. (2016). Product-service system (PSS) design: Using design thinking and business analytics to improve PSS design. Procedia CIRP, 47, 341–346.CrossRefGoogle Scholar
  19. Shearer, C. (2000). The CRISP-DM model: The new blueprint for data mining. J Data Warehousing, 5, 13–22.Google Scholar
  20. Siegel, E. (2013). Predictive analytics – The power to predict who will click, buy, lie, or die. Hoboken, NJ: Wiley. ISBN 978-1-118-35685-2.Google Scholar
  21. Singel, R. (2009). Netflix spilled your brokeback mountain secret, lawsuit claims. Wired, blog post. https://www.wired.com/2009/12/netflix-privacy-lawsuit
  22. Smith, J. B., & Colgate, M. (2007). Customer value creation: A practical framework. Journal of Marketing Theory and Practice, 15(1), 7–23.CrossRefGoogle Scholar
  23. Stadelmann, T., Stockinger, K., Braschler, M., Cieliebak, M., Baudinot, G., Dürr, O., & Ruckstuhl, A. (2013). Applied data science in Europe – Challenges for academia in keeping up with a highly demanded topic. European computer science summit ECSS 2013. Amsterdam: Informatics Europe.Google Scholar
  24. Stickdorn, M., & Schneider, J. (2010). This is service design thinking. Amsterdam: BIS.Google Scholar
  25. Stockinger, K., Stadelmann, T., & Ruckstuhl, R. (2016). Data Scientist als Beruf. Big Data. Edition HMD, Springer Fachmedien Wiesbaden.  https://doi.org/10.1007/978-3-658-11589-0_4.CrossRefGoogle Scholar
  26. Veeramachaneni, K. (2016, December 7). Why you’re not getting value from your data science. Harvard Business School Publishing Corporation. https://hbr.org/2016/12/why-youre-not-getting-value-from-your-data-science

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jürg Meierhofer
    • 1
    Email author
  • Thilo Stadelmann
    • 1
  • Mark Cieliebak
    • 1
  1. 1.ZHAW Zurich University of Applied SciencesWinterthurSwitzerland

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