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Prozessgetriebenes Datenqualitätsmanagement durch Integration von Datenqualität in bestehende Prozessmodelle

Anwendung von komplexitätsreduzierenden Mustern und ihr Einfluss auf Komplexitätsmetriken

Process-driven Data Quality Management – Integration of Data Quality into Existing Process Models

Application of Complexity-Reducing Patterns and the Impact on Complexity Metrics

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WIRTSCHAFTSINFORMATIK

Zusammenfassung

Die Bedeutung einer hohen Datenqualität und die Notwendigkeit von Datenqualität im Kontext von Geschäftsprozessen sind allgemein anerkannt. Prozessmodellierung ist für prozessgetriebenes Datenqualitätsmanagement erforderlich, welches die Datenqualität durch Neugestaltung von Prozessen zur Sammlung oder Änderung von Daten zu erhalten und zu verbessern sucht. Es existiert eine Vielzahl von Modellierungssprachen, welche von Unternehmen unterschiedlich angewendet werden. Der Zweck dieses Artikels ist es, einen kontextunabhängigen Ansatz vorzustellen, um Datenqualität in die Vielfalt der existierenden Prozessmodelle zu integrieren. Die Kommunikation der Datenqualität zwischen Stakeholdern soll unter Berücksichtigung der Prozessmodellkomplexität verbessert werden. Es wurde eine schlagwortbasierte Literaturrecherche in 74 IS-Zeitschriften und drei Konferenzen durchgeführt, in der 1.555 Artikel von 1995 an gesichtet wurden. 26 Artikel, darunter 46 Prozessmodelle, wurden im Detail untersucht. Die Literaturrecherche zeigt die Notwendigkeit einer kontextunabhängigen und sichtbaren Integration von Datenqualität in Prozessmodelle. Zunächst wird die Integration innerhalb eines Modells aufgezeigt. Dann folgt die Integration datenqualitätsorientierter Prozessmodelle mit anderen existierenden Prozessmodellen. Da Prozessmodelle hauptsächlich zur Kommunikation von Prozessen genutzt werden, werden der Einfluss der Integration von Datenqualität und die Anwendung von Mustern zur Komplexitätsreduktion sowie die Auswirkung auf die Komplexitätsmetriken des Modells betrachtet. Es bedarf weiterer Forschung zu Komplexitätsmetriken, um die Anwendbarkeit von Komplexitätsreduktionsmustern zu verbessern. Fehlende Kenntnisse über die Wechselwirkungen zwischen Metriken und fehlende Komplexitätsmetriken behindern die Einschätzung und Vorhersage der Prozessmodellkomplexität und damit die -verständlichkeit. Schließlich kann unser kontextunabhängiger Ansatz ergänzend für die Integration von Datenqualität in spezifische Prozessmodellierungssprachen genutzt werden.

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 or modify 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 derive the within-model integration, that is, enhancement of existing process models with data quality characteristics. Second, they derive the across-model integration, that is, 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 applicability of complexity reduction patterns. Missing knowledge about interdependency between metrics and missing complexity metrics impede assessment and prediction of process model complexity and thus understandability. Finally, our context-independent approach can be used complementarily to data quality integration focusing on specific process modeling languages.

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Notes

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  1. Wirtschaftsinformatik und Information Systems Quality, Universität zu Köln, Albertus-Magnus-Platz, 50923, Köln, Deutschland

    Paul Glowalla &  Ali Sunyaev

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  1. Paul Glowalla
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  2. Ali Sunyaev
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Correspondence to Ali Sunyaev.

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Angenommen nach zwei Überarbeitungen durch Prof. Dr. Buxmann.

This article is also available in English via http://www.springerlink.com and http://www.bise-journal.org: Glowalla P, Sunyaev A (2013) Process-Driven Data Quality Management through Integration of Data Quality into Existing Process Models. Application of Complexity-Reducing Patterns and the Impact on Complexity Metrics. Bus Inf Syst Eng. doi: 10.1007/s12599-013-0297-x.

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Glowalla, P., Sunyaev, A. Prozessgetriebenes Datenqualitätsmanagement durch Integration von Datenqualität in bestehende Prozessmodelle. Wirtschaftsinf 55, 435–452 (2013). https://doi.org/10.1007/s11576-013-0391-1

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