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Document Type

State of the Art

Abstract

The importance of high data quality and the need to consider data quality in the context of business processes are well acknowledged. Process modeling is mandatory for process-driven data quality management, which seeks to improve and sustain data quality by redesigning processes that create ormodify data. A variety of process modeling languages exist, which organizations heterogeneously apply. The purpose of this article is to present a context-independent approach to integrate data quality into the variety of existing process models. The authors aim to improve communication of data quality issues across stakeholders while considering process model complexity. They build on a keyword-based literature review in 74 IS journals and three conferences, reviewing 1,555 articles from 1995 onwards. 26 articles, including 46 process models, were examined in detail. The literature review reveals the need for a context-independent and visible integration of data quality into process models. First, the authors present the enhancement of existing process models with data quality characteristics. Second, they present the integration of a data-quality-centric process model with existing process models. Since process models are mainly used for communicating processes, they consider the impact of integrating data quality and the application of patterns for complexity reduction on the models’ complexity metrics. There is need for further research on complexity metrics to improve the applicability of complexity reduction patterns. Lacking knowledge about interdependency between metrics and missing complexitymetrics impede assessment and prediction of process model complexity and thus understandability. Finally, our contextindependent approach can be used complementarily for data quality integration with specific process modeling languages.

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