Purpose-The purpose of this paper is to facilitate understanding of enterprise resource planning (ERP) system and data quality interdependency by presenting ERP systems' use within data quality management. Design/methodology/approach-The authors apply task technology fit (TTF) in an explorative study, conducting semi-structured expert interviews with participants in information technology strategic decision making. The authors analyzed the interviews with iterative descriptive and subsequent interpretive coding. Findings-Although considered sustainable, continuously increasing regulations challenge ERP systems. However, compliance with regulations may serve as a bridge for organizations to engage in data analysis. Organizations are embedded into evolving task environments with the need to continuously adapt their systems or the organization and the need for contextual understanding of data quality. Research limitations/implications-With ERP systems being used for administrative functions, future research might draw on extant ERP systems research from the manufacturing sector. However, for insurance-specific tasks, ERP systems and their data need to be considered in a sector-specific context with the need for further research. Practical implications-ERP systems are considered sustainable. High initial fit is desirable, but the sector's relevance for ERP system vendors might be more important for sustainability. Ensuring TTF will be an increasing challenge with increasing task non-routineness. Originality/value-Applying TTF provides guidance for fit research, while the qualitative approach accounts for a deeper understanding, especially when exploring data quality issues since deficiencies might have several root causes. The authors show that ERP systems have an impact on data quality beyond its typically examined functionality.
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 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.
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 capabilities. However, there is a gap, since process modeling languages for PDDQM are not considered. We address this research gap by providing a synthesis of the varying applications of process modeling languages for PDDQM. We conducted a keyword-based literature review in conferences as well as 74 highranked information systems and computer science journals, reviewing 1,555 articles from 1995 onwards. For practitioners, it is possible to integrate the quality perspective within broadly applied process models. For further research, we derive representational requirements for PDDQM that should be integrated within existing process modeling languages. However, there is a need for further representational analysis to examine the adequacy of upcoming process modeling languages. New or enhanced process modeling languages may substitute for PDDQM-specific process modeling languages and facilitate development of a broadly applicable and accepted process modeling language for PDDQM.
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