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Steering the road of data quality with a dashboard: assessment of the Data Quality approach in Shell International Exploration & Production

Hoorn, Mark van der (2009) Steering the road of data quality with a dashboard: assessment of the Data Quality approach in Shell International Exploration & Production.

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Abstract:‘REMOVED’ For proper data quality management Wang et al. argue, by means of their so-called Total Data Quality Management theory, to implement continuous improvement cycles. With the iterative process of Define, Measure, Analyze and Improve, this methodology should ensure reliable delivery of high-quality information products. This is important as highquality data has become a strategic resource to make well-informed decisions in today’s fast changing business environments. In fact, this calls for a theoretical framework that can help companies to address and manage their data quality issues more sustainably. Despite the availability of the Total Data Quality Management (TDQM), it does not operationalize its variables in terms of implementation and management. After all, data quality managers should be able to monitor and steer their processes with a set of instruments and controls. Therefore, this study proposes a research model that operationalizes the TDQM method and extends it with management dashboard theory. Namely, this latter enables the efficient integration of information systems and corresponding performance indicators to support managers monitoring and steering their data quality throughout the continuous improvement cycles. For the theoretical model is a corresponding benchmark developed. It is based on statements from scientific literature and used to assess the current practice in Shell International Exploration & Production (SIEP). The outcomes of this benchmark bring a number of recommendations to improve the data quality approach in SIEP. The implement and validate these recommendations, a set of requirements is translated into a working prototype. The interactive character and strong visualization capabilities of this tool allow analyzing data quality in different formats and views. ‘REMOVED’ After implementation of the requirements in a full-functioning prototype, its effectiveness and efficiency are validated with a quasi experiment. The Technology Acceptance Model is used to validate the prototype against. This causal model is relevant as it links system features to cognitive responses and actual usage of the prototype in the end. Empirical testing in the SIEP business shows that the prototype is a significant improvement over the current situation. In other words, this research contributes to SIEP by delivering a prototype and to the academic world by delivering a validated research model that integrates the TDQM theory with a management dashboard.
Item Type:Essay (Master)
Royal Dutch Shell
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:85 business administration, organizational science
Programme:Industrial Engineering and Management MSc (60029)
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