Process-Driven Data Quality Management -- An Application of the Combined Conceptual Life Cycle Model

Abstract

Process-driven data quality management, which allows sustaining data quality improvements within and beyond the IS domain, is increasingly important. The emphasis on and the integration of data quality into process models allows for a detailed, context-specific definition as well as understanding of data quality (dimensions) and, thus, supports communication across stakeholders. Extant process modeling approaches lack an explicit reference from data quality dimensions to context-specific information product (IP) production. Therefore, we provide a process-driven application of the combined conceptual life cycle (CCLC) model for process exploration and data quality improvement. The paper presents an interpretive, in-depth case study in a medium-sized company, which launched a process optimization initiative to improve data quality. The results show benefits and limitations of the approach, allowing practitioners to tailor the approach to their needs. Based on our insights, suggestions for further improvements of the CCLC model for a process-driven IP production approach are provided.

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