Model-based Analysis of Data Inaccuracy Awareness in Business Processes

Yotam Evron (), Pnina Soffer () and Anna Zamansky ()
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Yotam Evron: University of Haifa
Pnina Soffer: University of Haifa
Anna Zamansky: University of Haifa

Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, 2022, vol. 64, issue 2, No 5, 183-200

Abstract: Abstract 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.

Keywords: Business process management; Data quality; Model-based analysis (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s12599-021-00709-9

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