The article "Data Quality Assessment" by Leo L. Pipino, Yang W. Lee, and Richard Y. Wang discusses the importance of developing usable data quality metrics for organizations. It highlights that data quality is a multi-dimensional concept, encompassing both subjective perceptions and objective measurements. The authors present three functional forms—simple ratio, min or max operation, and weighted average—to develop objective data quality metrics. These forms are applied to various dimensions such as correctness, completeness, consistency, believability, timeliness, and accessibility. The article also emphasizes the importance of combining subjective and objective assessments to improve data quality. Practical examples from companies like Global Consumer Goods, Inc. (GCG) and Data Product Manufacturing, Inc. (DPM) illustrate how these assessments can be used to identify and address data quality issues. The authors conclude that a tailored approach to data quality assessment is essential, and that a comprehensive understanding of fundamental principles is crucial for effective data quality management.The article "Data Quality Assessment" by Leo L. Pipino, Yang W. Lee, and Richard Y. Wang discusses the importance of developing usable data quality metrics for organizations. It highlights that data quality is a multi-dimensional concept, encompassing both subjective perceptions and objective measurements. The authors present three functional forms—simple ratio, min or max operation, and weighted average—to develop objective data quality metrics. These forms are applied to various dimensions such as correctness, completeness, consistency, believability, timeliness, and accessibility. The article also emphasizes the importance of combining subjective and objective assessments to improve data quality. Practical examples from companies like Global Consumer Goods, Inc. (GCG) and Data Product Manufacturing, Inc. (DPM) illustrate how these assessments can be used to identify and address data quality issues. The authors conclude that a tailored approach to data quality assessment is essential, and that a comprehensive understanding of fundamental principles is crucial for effective data quality management.