IDEAS home Printed from https://ideas.repec.org/a/spr/binfse/v66y2024i4d10.1007_s12599-024-00857-8.html
   My bibliography  Save this article

Data-Centric Artificial Intelligence

Author

Listed:
  • Johannes Jakubik

    (Karlsruhe Institute of Technology)

  • Michael Vössing

    (Karlsruhe Institute of Technology)

  • Niklas Kühl

    (University of Bayreuth)

  • Jannis Walk

    (Karlsruhe Institute of Technology)

  • Gerhard Satzger

    (Karlsruhe Institute of Technology)

Abstract

Data-centric artificial intelligence (data-centric AI) represents an emerging paradigm that emphasizes the importance of enhancing data systematically and at scale to build effective and efficient AI-based systems. The novel paradigm complements recent model-centric AI, which focuses on improving the performance of AI-based systems based on changes in the model using a fixed set of data. The objective of this article is to introduce practitioners and researchers from the field of Business and Information Systems Engineering (BISE) to data-centric AI. The paper defines relevant terms, provides key characteristics to contrast the paradigm of data-centric AI with the model-centric one, and introduces a framework to illustrate the different dimensions of data-centric AI. In addition, an overview of available tools for data-centric AI is presented and this novel paradigm is differenciated from related concepts. Finally, the paper discusses the longer-term implications of data-centric AI for the BISE community.

Suggested Citation

  • Johannes Jakubik & Michael Vössing & Niklas Kühl & Jannis Walk & Gerhard Satzger, 2024. "Data-Centric Artificial Intelligence," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 66(4), pages 507-515, August.
    • Handle: RePEc:spr:binfse:v:66:y:2024:i:4:d:10.1007_s12599-024-00857-8
    DOI: 10.1007/s12599-024-00857-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12599-024-00857-8
    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/s12599-024-00857-8?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. Kühl, Niklas & Schemmer, Max & Goutier, Marc & Satzger, Gerhard, 2022. "Artificial intelligence and machine learning," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 135656, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Niklas Kühl & Max Schemmer & Marc Goutier & Gerhard Satzger, 2022. "Artificial intelligence and machine learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2235-2244, December.
    3. Joshua Holstein & Max Schemmer & Johannes Jakubik & Michael Vössing & Gerhard Satzger, 2023. "Sanitizing data for analysis: Designing systems for data understanding," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-18, December.
    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. Eduard Hartwich & Alexander Rieger & Johannes Sedlmeir & Dominik Jurek & Gilbert Fridgen, 2023. "Machine economies," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-13, December.
    2. Yukthakiran Matla & Rohith Rao Yannamaneni & George Pappas, 2024. "Globalizing Food Items Based on Ingredient Consumption," Sustainability, MDPI, vol. 16(17), pages 1-22, August.
    3. Felix Xiong & Niklas Kühl & Maximilian Stauder, 2024. "Designing a computer-vision-based artifact for automated quality control: a case study in the food industry," Flexible Services and Manufacturing Journal, Springer, vol. 36(4), pages 1422-1449, December.
    4. Diebel, Christopher & Goutier, Marc & Adam, Martin & Benlian, Alexander, 2025. "When AI-Based Agents Are Proactive: Implications for Competence and System Satisfaction in Human–AI Collaboration," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 138514, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    5. Goutier, Marc & Diebel, Christopher & Adam, Martin & Benlian, Alexander, 2024. "Proactive and Reactive Help from Intelligent Agents in Identity-Relevant Tasks," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 142985, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    6. 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.
    7. Rainer Alt, 2022. "Electronic Markets on AI and standardization," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 1795-1805, December.
    8. Mark Reuver & Hosea A. Ofe & Mila Gasco-Hernandez & Boriana Rukanova & J. Ramon Gil-Garcia, 2024. "Data economy in a globalized world, opportunities and challenges for public and private organizations," Electronic Markets, Springer;IIM University of St. Gallen, vol. 34(1), pages 1-5, 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:binfse:v:66:y:2024:i:4:d:10.1007_s12599-024-00857-8. 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.