IDEAS home Printed from https://ideas.repec.org/a/spr/jknowl/v15y2024i1d10.1007_s13132-022-01081-z.html
   My bibliography  Save this article

Characterization of Usage Data with the Help of Data Classifications

Author

Listed:
  • Melina Panzner

    (University of Paderborn)

  • Sebastian Enzberg

    (Fraunhofer Institute for Mechatronic Systems Design)

  • Maurice Meyer

    (University of Paderborn)

  • Roman Dumitrescu

    (University of Paderborn)

Abstract

Comprehensive data understanding is a key success driver for data analytics projects. Knowing the characteristics of the data helps a lot in selecting the appropriate data analysis techniques. Especially in data-driven product planning, knowledge about the data is a necessary prerequisite because data of the use phase is very heterogeneous. However, companies often do not have the necessary know-how or time to build up solid data understanding in connection with data analysis. In this paper, we develop a methodology to organize and categorize and thus understand use phase data in a way that makes it accessible to general data analytics workflows, following a design science research approach. We first present a knowledge base that lists typical use phase data from a product planning view. Second, we develop a taxonomy based on standard literature and real data objects, which covers the diversity of the data considered. The taxonomy provides 8 dimensions that support classification of use phase data and allows to capture data characteristics from a data analytics view. Finally, we combine both views by clustering the objects of the knowledge base according to the taxonomy. Each of the resulting clusters covers a typical combination of analytics relevant characteristics occurring in practice. By abstracting from the diversity of use phase data into artifacts with manageable complexity, our approach provides guidance to choose appropriate data analysis and AI techniques.

Suggested Citation

  • Melina Panzner & Sebastian Enzberg & Maurice Meyer & Roman Dumitrescu, 2024. "Characterization of Usage Data with the Help of Data Classifications," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 88-109, March.
    • Handle: RePEc:spr:jknowl:v:15:y:2024:i:1:d:10.1007_s13132-022-01081-z
    DOI: 10.1007/s13132-022-01081-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13132-022-01081-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13132-022-01081-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Liang Hou & Roger J. Jiao, 2020. "Data-informed inverse design by product usage information: a review, framework and outlook," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 529-552, March.
    2. Robert C Nickerson & Upkar Varshney & Jan Muntermann, 2013. "A method for taxonomy development and its application in information systems," European Journal of Information Systems, Taylor & Francis Journals, vol. 22(3), pages 336-359, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Thorsten Schoormann & Julia Schweihoff & Ilka Jussen & Frederik Möller, 2023. "Classification tools for business models: Status quo, comparison, and agenda," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-36, December.
    2. Maggie Wang, Yazhu & Matook, Sabine & Dennis, Alan R., 2024. "Unintended consequences of humanoid service robots: A case study of public service organizations," Journal of Business Research, Elsevier, vol. 174(C).
    3. Jochen Wulf & Juerg Meierhofer, 2023. "Towards a Taxonomy of Large Language Model based Business Model Transformations," Papers 2311.05288, arXiv.org.
    4. Iñigo Flores Ituarte & Suraj Panicker & Hari P. N. Nagarajan & Eric Coatanea & David W. Rosen, 2023. "Optimisation-driven design to explore and exploit the process–structure–property–performance linkages in digital manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 219-241, January.
    5. Simon Scheider & Florian Lauf & Simon Geller & Frederik Möller & Boris Otto, 2023. "Exploring design elements of personal data markets," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-16, December.
    6. Nina Thornton & Martin Engert & Andreas Hein & Helmut Krcmar, 2023. "Finding new purpose for vacancies in rural areas: a taxonomy of coworking space business models," International Entrepreneurship and Management Journal, Springer, vol. 19(3), pages 1395-1423, September.
    7. Patrick Zschech, 2023. "Beyond descriptive taxonomies in data analytics: a systematic evaluation approach for data-driven method pipelines," Information Systems and e-Business Management, Springer, vol. 21(1), pages 193-227, March.
    8. Jinjuan Duan & Mark Evans & Karl Hurn & Ian Storer & Zhewen Bai, 2024. "A creative industrial design framework of the taxonomy for Chinese indigenous materials and relevant crafts," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    9. Daniel Kirste & Niclas Kannengie{ss}er & Ricky Lamberty & Ali Sunyaev, 2023. "How Automated Market Makers Approach the Thin Market Problem in Cryptoeconomic Systems," Papers 2309.12818, arXiv.org, revised Sep 2023.
    10. Marcel Fassnacht & Jannis Leimstoll & Carina Benz & Daniel Heinz & Gerhard Satzger, 2024. "Data sharing practices: The interplay of data, organizational structures, and network dynamics," Electronic Markets, Springer;IIM University of St. Gallen, vol. 34(1), pages 1-25, December.
    11. Gregory Vial, 2023. "A Complex Adaptive Systems Perspective of Software Reuse in the Digital Age: An Agenda for IS Research," Information Systems Research, INFORMS, vol. 34(4), pages 1728-1743, December.
    12. Jens Passlick & Lukas Grützner & Michael Schulz & Michael H. Breitner, 2023. "Self-service business intelligence and analytics application scenarios: A taxonomy for differentiation," Information Systems and e-Business Management, Springer, vol. 21(1), pages 159-191, March.
    13. Rene Abraham & Johannes Schneider & Jan vom Brocke, 2023. "A taxonomy of data governance decision domains in data marketplaces," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-13, December.
    14. Navitha Singh Sewpersadh, 2023. "Disruptive business value models in the digital era," Journal of Innovation and Entrepreneurship, Springer, vol. 12(1), pages 1-27, December.
    15. Luz Parrondo, 2023. "Cryptoassets: Definitions and accounting treatment under the current International Financial Reporting Standards framework," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 30(4), pages 208-227, October.
    16. Potenciano Menci, Sergio & Valarezo, Orlando, 2024. "Decoding design characteristics of local flexibility markets for congestion management with a multi-layered taxonomy," Applied Energy, Elsevier, vol. 357(C).
    17. Puschmann, Thomas & Huang-Sui, Marine, 2024. "A taxonomy for decentralized finance," International Review of Financial Analysis, Elsevier, vol. 92(C).
    18. Oliver Werth & Davinia Rodríguez Cardona & Albert Torno & Michael H. Breitner & Jan Muntermann, 2023. "What determines FinTech success?—A taxonomy-based analysis of FinTech success factors," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-22, December.
    19. Julia Schweihoff & Anzelika Lipovetskaja & Ilka Jussen-Lengersdorf & Frederik Möller, 2024. "Stuck in the middle with you: Conceptualizing data intermediaries and data intermediation services," Electronic Markets, Springer;IIM University of St. Gallen, vol. 34(1), pages 1-26, December.
    20. Janina Seutter & Kristin Kutzner & Maren Stadtländer & Dennis Kundisch & Ralf Knackstedt, 2023. "“Sorry, too much information”—Designing online review systems that support information search and processing," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-19, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jknowl:v:15:y:2024:i:1:d:10.1007_s13132-022-01081-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.