Strategies for data quality monitoring in business processes

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Abstract

The relevance of data quality is continuously increasing in modern enterprises. This is due to the fact that poor data quality has often a negative impact on the business effectiveness and efficiency. Errors, missing or out-of-date data might cause the failure of the business processes and consequently the loss of time and money. In such a scenario, the adoption of tools and methods able to detect and correct process data errors is desirable. In this paper we propose the quality aware process redesign as a quality improvement method. In particular, the business process is analyzed and modified at design time in order to include Data Quality blocks that are components responsible for the error detection and repair and thus for improving the process reliability. Note that Data Quality blocks can be added to the process workflow using different configurations. This paper aims to describe and compare such configurations. Furthermore, since each configuration impacts in different ways on the process quality and performance, we provide some guidelines for the selection of the configuration able to satisfy the business requirements.

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Cappiello, C., Pernici, B., & Villani, L. (2015). Strategies for data quality monitoring in business processes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9051, pp. 226–238). Springer Verlag. https://doi.org/10.1007/978-3-319-20370-6_18

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