Article: Database Quality Dimensions Journal: Journal of Business and Management (JBM) 2000 Vol.7 No.1 pp.101 - 115 Abstract: To ensure a quality database application, should the emphasis during model development be on the application of quality assurance metrics (designing it right)? It's hard to argue against this point, but there is a significant amount of research and anecdotal evidence that suggests that a large number of organizational database applications fail or are unusable. A quality process does not necessarily lead to a usable database product. Databases are a critical element of virtually all conventional and e-business applications. A database should be evaluated in production based on certain quantitative and information-preserving transformation measures, such as data quality, data integrity, normalization, and performance. However, there are also many examples of database applications that are in most ways 'well-formed' with high data quality but lack semantic or cognitive fidelity (the right design). Additionally, determining and implementing the proper set of database behaviors can be an elusive task. Whether the database meets the expectations of its end-users is only one aspect of overall database quality. This paper expands on the growing body of literature in the area of data quality by proposing additions to a hierarchy of database quality dimensions that includes model and behavioral factors in addition to the process and data factors. Inderscience Publishers - linking academia, business and industry through research

Title: Database Quality Dimensions

Authors: John Hoxmier

Addresses: Author address listing can be found in the "About the Authors" section at the end of the article.

Abstract: To ensure a quality database application, should the emphasis during model development be on the application of quality assurance metrics (designing it right)? It's hard to argue against this point, but there is a significant amount of research and anecdotal evidence that suggests that a large number of organizational database applications fail or are unusable. A quality process does not necessarily lead to a usable database product. Databases are a critical element of virtually all conventional and e-business applications. A database should be evaluated in production based on certain quantitative and information-preserving transformation measures, such as data quality, data integrity, normalization, and performance. However, there are also many examples of database applications that are in most ways 'well-formed' with high data quality but lack semantic or cognitive fidelity (the right design). Additionally, determining and implementing the proper set of database behaviors can be an elusive task. Whether the database meets the expectations of its end-users is only one aspect of overall database quality. This paper expands on the growing body of literature in the area of data quality by proposing additions to a hierarchy of database quality dimensions that includes model and behavioral factors in addition to the process and data factors.

Keywords: Database; quality assurance; data quality; normalization; performance; e-business; data integrity; semantic fidelity; cognitive fidelity.

DOI: 10.1504/JBM.2000.141063

Journal of Business and Management, 2000 Vol.7 No.1, pp.101 - 115

Published online: 05 Sep 2024 *

Title: Database Quality Dimensions

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