Dimensions of Data Quality
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, ...
Get Meeting the Challenges of Data Quality Management now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
