Abstract
Little guidance has been available to organizations interested in addressing the necessary dimensions of data resources management to ensure data quality in increasingly encountered situations when data usage crosses system boundaries. The basic concept of metadata quality as a foundation for data quality engineering is proposed, as well as an extended data life cycle model consisting of eight phases: metadata creation, metadata structuring, metadata refinement, data creation, data utilization, data assessment, data refinement, and data manipulation. This extended model will enable further development of life cycle phase-specific data quality engineering methods. The paper also expands the concept of applicable data quality dimensions, presenting data quality as a function of four distinct components: data value quality; data representation quality; data model quality; and data architecture quality. Each of these, in turn, is described in terms of specific data quality attributes.
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