Abstract
This chapter provides an in-depth discussion about a core concept in data quality management: data quality dimensions. Dimensions provide a framework through which we can understand the core capabilities. As the foundation for data quality rules and requirements, they play a critical role in helping answer the fundamental questions about data quality: “What do we mean by high-quality data?” “How do we detect low-quality data?” and “What action will we take when data does not meet quality standards?” This chapter will review a comprehensive set of dimensions (i.e., completeness, correctness, uniqueness, consistency, currency, validity, integrity, reasonability, precision, clarity, accessibility, timeliness, relevance, usability, trustworthiness) in the context of challenges associated with data structure and meaning, the processes for creating data, the influence of technology on quality, and the perceptions of data consumers.
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