Data quality is assessed using a variety of metrics and methodologies. Common metrics include error rates, missing data percentages, and data duplication rates. Data validation techniques, such as cross-referencing with other data sources, are also employed. Additionally, various software tools and dedicated data quality management systems are used to automate the assessment and improvement of data quality.