Model-based Analysis of Data Inaccuracy Awareness in Business Processes

Research output: Contribution to journalArticlepeer-review


Problem definition: Data errors in business processes can be a source for exceptions and hamper business outcomes. Relevance: The paper proposes a method for analyzing data inaccuracy issues already at process design time, in order to support process designers by identifying process parts where data errors might remain unrecognized, so decisions could be taken based on inaccurate data. Methodology: The paper follows design science, developing a method as an artifact. The conceptual basis is the notion of data inaccuracy awareness – the ability to tell whether potential discrepancies between real and IS values may exist. Results: The method was implemented on top of a Petri net modeling tool and validated in a case study performed in a large manufacturing company of safety–critical systems. Managerial implications: Anticipating consequences of data inaccuracy already during process design can help avoiding them at runtime.

Original languageEnglish
JournalBusiness and Information Systems Engineering
Issue number2
StatePublished - 2021

Bibliographical note

Funding Information:
The research was supported by the Israel Science Foundation under Grant agreements 856/13 and 550/19.

Publisher Copyright:
© 2021, Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature.


  • Business process management
  • Data quality
  • Model-based analysis

ASJC Scopus subject areas

  • Information Systems


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