Paul Glowalla

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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, contextspecific definition as well as understanding of data quality (dimensions) and, thus, supports communication(More)
Data quality management (DQM) gains importance due to the increasing amount and diversity of data as well as data’s critical impact on organizational success. Enterprise resource planning (ERP) systems, which are used in most manufacturing and service organizations, provide a platform for data integration. The information-intensive insurance sector, with(More)
Highly regulated sectors face challenges on data and information quality management (DIQM) to conform to increasing regulations. With the financial service sector, as the most highly regulated industry, we are interested in current and future DIQM challenges. For a sustaining improvement, data quality should be managed process-driven. Process-driven data(More)
Data quality is critical to organizational success. In order to improve and sustain data quality in the long term, process-driven data quality management (PDDQM) seeks to redesign processes that create or modify data. Consequently, process modeling is mandatory for PDDQM. Current research examines process modeling languages with respect to representational(More)
Business intelligence (BI) becomes increasingly important and is an evolving topic in research and practice. The evolvement of BI, the flexibility of BI systems, and their context-dependency entail diverse and partially contradicting results on BI system use and outcomes. This study examines how and why BI systems are used differently and what the entailing(More)
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