A semiotic information quality framework: development and comparative analysis
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Research Article
A semiotic information quality framework: development and comparative analysis Rosanne Price, Graeme Shanks Faculty of Information Technology, Monash University, Victoria, Australia Correspondence: R Price, Faculty of Information Technology, Building 63, Monash University, Clayton Campus, 3800 Victoria, Australia. Tel.: þ 613 9905 2461; Fax: þ 613 9905 5159; E-mail: [email protected]
Abstract An organization depends on quality information for effective operations and decisionmaking. However, there is still no agreement as to how quality should be defined in terms of specific quality categories and criteria. Proposed information quality frameworks have limitations with respect to either consistency, resulting from a non-theoretical approach to framework development, or scope, considering only objective but not subjective information quality perspectives. In this paper, we describe a unique research approach to framework development that addresses these problems and compare it to those used previously for other frameworks. Semiotic theory, the philosophical theory of signs, is used to ensure rigor and scope. It provides a theoretical basis for framework structure – quality categories and their criteria – and for integrating objective and subjective quality views. Empirical refinement based on academic, practitioner, and end-user focus groups is then used to ensure relevance. Journal of Information Technology (2005) 20, 88–102. doi:10.1057/palgrave.jit.2000038 Keywords: information quality; data quality; semiotics; decision support
Introduction uality information and information quality management in an organization is essential for effective operations and decision-making. The proliferation of data warehouses to support decision-making further highlights an organization’s vulnerability with respect to poor data quality, especially given the widely disparate data sources, contexts, users, and data uses characterizing data warehouses and the much less predictable data usage involved in decision-making as compared to business operations. Regardless of whether conventional databases or data warehouses are used to support decision-making, it is clear that management of information quality is critical to the effectiveness of the decision support systems employed. However, management of information quality pre-supposes a clear understanding of and consensus with respect to the meaning of the term ‘information quality’. In fact, fundamental questions still remain as to how quality should be defined and the specific criteria that should be used to evaluate information quality. Addressing these research questions is an important step in establishing a basis both for developing information quality assessment mechanisms and for discussing
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related issues such as quality improvement and management. Competing views of quality from product- and servicebased perspectives focus on objective and
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