IDEAS home Printed from https://ideas.repec.org/a/spr/jknowl/v12y2021i1d10.1007_s13132-016-0396-2.html
   My bibliography  Save this article

Data Value, Big Data Analytics, and Decision-Making

Author

Listed:
  • Jean-Louis Monino

    (Université de Montpellier)

Abstract

With the increasing integration of different technologies in a growing range of equipment and products, big data is a paradigm shift that involves data analysis, using well-known schemes, to extract patterns in hidden relationships. This is the radical change in the “business model” of a company related to the monetization of the data it collects. The biggest question for a company is no longer deciding if it should launch new products, but rather taking advantage of available (structured or unstructured) data and to know how to develop a high performance and design an appropriate mining to efficiently analyze big data and to find the useful things from it. The objective of this paper is to show that the challenges of the era of “data revolution” focus on data uses. It is linked to the rise of the intangible economy that mobilizes knowledge and highlights the importance of data. To deeply discuss this issue, this paper illustrates the buzz words related to data especially big data and open data, in order to illuminate the discussions of data valorization.

Suggested Citation

  • Jean-Louis Monino, 2021. "Data Value, Big Data Analytics, and Decision-Making," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 12(1), pages 256-267, March.
    • Handle: RePEc:spr:jknowl:v:12:y:2021:i:1:d:10.1007_s13132-016-0396-2
    DOI: 10.1007/s13132-016-0396-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13132-016-0396-2
    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/s13132-016-0396-2?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Claire Jean-Quartier & Miguel Rey Mazón & Mario Lovrić & Sarah Stryeck, 2022. "Collaborative Data Use between Private and Public Stakeholders—A Regional Case Study," Data, MDPI, vol. 7(2), pages 1-14, January.

    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:12:y:2021:i:1:d:10.1007_s13132-016-0396-2. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.