Conceptualizing Information Quality: A Review of Information Quality Frameworks from the Last Ten Years.

Martin J. Eppler · Dörte Wittig

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Data Quality and Management Big Data and Business Intelligence Knowledge Management and Technology

Abstract

This paper analyzes seven conceptual frameworks on information quality along six criteria in two dimensions in order to identify common elements, differences, and missing components of such frameworks. The frameworks are evaluated according to analytic (or scientific) criteria and pragmatic (or operational) criteria. The analytic criteria are based on academic standards and require clear definitions of the terms used in a framework, a positioning of the framework within existing literature, and a consistent and systematic structure. The pragmatic dimension consists of criteria which make the framework applicable, namely conciseness (i.e., if the framework is memorable), whether examples are provided to illustrate the framework, and the inclusion of tools that are based on the framework. The review of the frameworks showed that information quality frameworks are either strong in their analytic dimension or in their pragmatic dimension, but rarely strong on both dimensions at the same time. The evaluation also revealed that the IQ frameworks are often domain-specific (i.e., for a specific application such as data warehouses or corporate communications), and that they rarely analyze interdependencies between the information quality criteria that are included in a framework. The paper concludes by outlining five future directions for information quality frameworks: first, the quest for more generic models. Second, the development of IQ frameworks that show interdependencies between different quality criteria. Third, the inclusion of problem areas and indicators into these frameworks (thus frameworks that go beyond simple quality criteria lists). Fourth, the development of tools which are based on an information quality framework. And lastly, the development of frameworks that are at the same time theoretical and practical.

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References

1996Beyond Accuracy: What Data Quality Means to Data Consumers 1991Towards a dynamic theory of strategy 1997Information ecology: mastering the information and knowledge environment 1997Data Quality For The Information Age 1998Modeling Information Manufacturing Systems to Determine Information Product Quality 1998Quality information and knowledge 2001Improving Data Warehouse and Business Information Quality 1996THE MULTIPLE DIMENSIONS OF INFORMATION QUALITY

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