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  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
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dc.contributor.authorMeierhofer, Jürg-
dc.contributor.authorStadelmann, Thilo-
dc.contributor.authorCieliebak, Mark-
dc.date.accessioned2019-06-19T14:01:13Z-
dc.date.available2019-06-19T14:01:13Z-
dc.date.issued2019-
dc.identifier.isbn978-3-030-11820-4de_CH
dc.identifier.isbn978-3-030-11821-1de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/17339-
dc.description.abstractData 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.de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofApplied data science : lessons learned for the data-driven businessde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectSmart servicede_CH
dc.subjectData productde_CH
dc.subjectService designde_CH
dc.subjectValue proposition designde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.titleData productsde_CH
dc.typeBuchbeitragde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
zhaw.publisher.placeChamde_CH
dc.identifier.doi10.1007/978-3-030-11821-1_4de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end61de_CH
zhaw.pages.start47de_CH
zhaw.parentwork.editorBraschler, Martin-
zhaw.parentwork.editorStadelmann, Thilo-
zhaw.parentwork.editorStockinger, Kurt-
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewEditorial reviewde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedInformation Engineeringde_CH
zhaw.webfeedService Engineeringde_CH
zhaw.webfeedNatural Language Processingde_CH
zhaw.webfeedMachine Perception and Cognitionde_CH
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Meierhofer, J., Stadelmann, T., & Cieliebak, M. (2019). Data products. In M. Braschler, T. Stadelmann, & K. Stockinger (Eds.), Applied data science : lessons learned for the data-driven business (pp. 47–61). Springer. https://doi.org/10.1007/978-3-030-11821-1_4
Meierhofer, J., Stadelmann, T. and Cieliebak, M. (2019) ‘Data products’, in M. Braschler, T. Stadelmann, and K. Stockinger (eds) Applied data science : lessons learned for the data-driven business. Cham: Springer, pp. 47–61. Available at: https://doi.org/10.1007/978-3-030-11821-1_4.
J. Meierhofer, T. Stadelmann, and M. Cieliebak, “Data products,” in Applied data science : lessons learned for the data-driven business, M. Braschler, T. Stadelmann, and K. Stockinger, Eds. Cham: Springer, 2019, pp. 47–61. doi: 10.1007/978-3-030-11821-1_4.
MEIERHOFER, Jürg, Thilo STADELMANN und Mark CIELIEBAK, 2019. Data products. In: Martin BRASCHLER, Thilo STADELMANN und Kurt STOCKINGER (Hrsg.), Applied data science : lessons learned for the data-driven business. Cham: Springer. S. 47–61. ISBN 978-3-030-11820-4
Meierhofer, Jürg, Thilo Stadelmann, and Mark Cieliebak. 2019. “Data Products.” In Applied Data Science : Lessons Learned for the Data-Driven Business, edited by Martin Braschler, Thilo Stadelmann, and Kurt Stockinger, 47–61. Cham: Springer. https://doi.org/10.1007/978-3-030-11821-1_4.
Meierhofer, Jürg, et al. “Data Products.” Applied Data Science : Lessons Learned for the Data-Driven Business, edited by Martin Braschler et al., Springer, 2019, pp. 47–61, https://doi.org/10.1007/978-3-030-11821-1_4.


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