Data quality and due process in large interorganizational record systems
Communications of the ACM
Methodology for allocating resources for data quality enhancement
Communications of the ACM
Envisioning information
Accuracy and relevance and the quality of data
Data quality control theory and pragmatics
Data quality: management and technology
Data quality: management and technology
Enterprise architecture planning: developing a blueprint for data, applications and technology
Enterprise architecture planning: developing a blueprint for data, applications and technology
DoD legacy systems: reverse engineering data requirements
Communications of the ACM
The notion of data and its quality dimensions
Information Processing and Management: an International Journal
Requirements-driven data engineering
Information and Management
Estimating and improving the quality of information in a MIS
Communications of the ACM
Management Information Systems, International: Managerial End User Perspective
Management Information Systems, International: Managerial End User Perspective
The Data Modeling Handbook: A Best-Practice Approach to Building Quality Data Models
The Data Modeling Handbook: A Best-Practice Approach to Building Quality Data Models
Data Models
Business systems planning and business Information control study: a comparison
IBM Systems Journal
Information Resources Management: Improving the Focus
Information Resources Management Journal
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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.