Paul Glowalla

ERP system fit – an explorative task and data quality perspective

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 r...

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

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.

Process-driven data quality management: A critical review on the application of process modeling languages

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.

Process-Driven Data Quality Management Through Integration of Data Quality into Existing Process Models

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 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 complexity metrics impede assessment and prediction of process model complexity and thus understandability. Finally, our context-independent approach can be used complementarily for data quality integration with specific process modeling languages.