This article discusses the importance of data quality assessment and presents principles for developing usable data quality metrics. Data quality is a multi-dimensional concept that involves both subjective perceptions and objective measurements. Subjective assessments reflect the needs and experiences of stakeholders, while objective assessments can be task-independent or task-dependent. The article describes three functional forms for developing objective data quality metrics: simple ratio, min or max operation, and weighted average. These forms can be used to measure various data quality dimensions such as completeness, consistency, and timeliness. The article also presents an approach that combines subjective and objective assessments of data quality, and illustrates how it has been used in practice. It discusses the importance of data quality in organizations and how it can be used to improve data quality. The article also discusses the challenges of developing a single aggregate measure of data quality and the need for a comprehensive approach to data quality assessment. Finally, the article concludes that data quality assessment is an ongoing process that requires awareness of the fundamental principles underlying the development of data quality metrics.This article discusses the importance of data quality assessment and presents principles for developing usable data quality metrics. Data quality is a multi-dimensional concept that involves both subjective perceptions and objective measurements. Subjective assessments reflect the needs and experiences of stakeholders, while objective assessments can be task-independent or task-dependent. The article describes three functional forms for developing objective data quality metrics: simple ratio, min or max operation, and weighted average. These forms can be used to measure various data quality dimensions such as completeness, consistency, and timeliness. The article also presents an approach that combines subjective and objective assessments of data quality, and illustrates how it has been used in practice. It discusses the importance of data quality in organizations and how it can be used to improve data quality. The article also discusses the challenges of developing a single aggregate measure of data quality and the need for a comprehensive approach to data quality assessment. Finally, the article concludes that data quality assessment is an ongoing process that requires awareness of the fundamental principles underlying the development of data quality metrics.