IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6239-602-9_18.html

A Comparative Study of Data Quality Management Methods

In: Proceedings of the 2025 7th International Conference on Economic Management and Model Engineering (ICEMME 2025)

Author

Listed:
  • Gaoqi Dai

    (State Grid Energy Research Institute Co., Ltd)

  • Bingxin Zeng

    (State Grid Energy Research Institute Co., Ltd)

Abstract

Data quality management is a solution that integrates methodology, technology, business and management. Through effective data quality control measures, data management and control can be carried out to eliminate abnormal and missing data quality issues, thereby enhancing the enterprise’s ability to monetize data. In terms of data quality management, there are many theoretical frameworks related to data quality governance at home and abroad as guidelines for data quality management. This article compares different data quality governance methods and selects the appropriate one in combination with different scenarios.

Suggested Citation

  • Gaoqi Dai & Bingxin Zeng, 2026. "A Comparative Study of Data Quality Management Methods," Advances in Economics, Business and Management Research, in: Touria Benazzouz & Sandeep Saxena & Hui Nee Au Yong & Nor Zafir Md Salleh (ed.), Proceedings of the 2025 7th International Conference on Economic Management and Model Engineering (ICEMME 2025), pages 184-191, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6239-602-9_18
    DOI: 10.2991/978-94-6239-602-9_18
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:advbcp:978-94-6239-602-9_18. 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.