IDEAS home Printed from https://ideas.repec.org/a/nwe/iitfed/y2024i1p257-261.html

Data Types in Business Process Management: classification, comparison, and analytical potential

Author

Listed:
  • Geno Stefanov

    (University of National and World Economy, Sofia, Bulgaria)

Abstract

Business Process Management (BPM) generates and uses a variety of data types that play a key role in modeling, analyzing, optimizing, and monitoring business processes. This report provides a systematic classification and comparison of the main categories of process data used in BPM - process models (BPMN), process execution data (log data), and contextual business data. Their characteristics, sources, purposes, and applicability in various analytical scenarios are analyzed - from process mining to predictive analytics and business process optimization. A comparative summary of the advantages and limitations of each data type is presented, as well as their importance for generating knowledge and making management decisions. Finally, the main challenges related to their integration, quality, and joint use are outlined.

Suggested Citation

  • Geno Stefanov, 2025. "Data Types in Business Process Management: classification, comparison, and analytical potential," Innovative Information Technologies for Economy Digitalization (IITED), University of National and World Economy, Sofia, Bulgaria, issue 1, pages 257-261, October.
    • Handle: RePEc:nwe:iitfed:y:2024:i:1:p:257-261
    as

    Download full text from publisher

    File URL: https://www.unwe.bg/doi/iited/2025/IITED.2025.32.pdf
    Download Restriction: no
    ---><---

    More about this item

    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:nwe:iitfed:y:2024:i:1:p:257-261. 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: Vanya Lazarova (email available below). General contact details of provider: https://edirc.repec.org/data/unweebg.html .

    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.