Relevance is not a dimension of data quality. The dimensions of data quality are accuracy, completeness, consistency, and timeliness.

Data harmonization is the process of comparing data from different sources to identify and correct errors. Data validation, data verification, and data cleansing are all processes that are used to improve data quality.

Data mining is not a method for assessing data accuracy. Field verification, data profiling, and data validation are all methods that are used to assess data accuracy.

Data cleaning is the process of identifying and correcting errors in data. Data scrubbing, data cleansing, and data validation are all terms that are used to describe this process.

Data mining is not a method for assessing data completeness. Data profiling, data validation, and data completeness analysis are all methods that are used to assess data completeness.

Data consistency checking is the process of ensuring that data is consistent with other data in a dataset. Data integrity checking, data validation, and data harmonization are all processes that are used to improve data quality.

Data mining is not a method for assessing data consistency. Data profiling, data validation, and data consistency analysis are all methods that are used to assess data consistency.

Data timeliness assessment is the process of ensuring that data is up-to-date and accurate. Data currency assessment, data freshness assessment, and data accuracy assessment are all terms that are used to describe this process.

Data mining is not a method for assessing data timeliness. Data profiling, data validation, and data timeliness analysis are all methods that are used to assess data timeliness.

Data quality assessment is the overall process of assessing and evaluating the quality of data. Data quality evaluation, data quality analysis, and data quality control are all terms that are used to describe this process.

Improved data relevance is not a benefit of data quality assessment and evaluation. Improved data accuracy, improved data completeness, and improved data consistency are all benefits of data quality assessment and evaluation.

Data quality assessment is the process of identifying and prioritizing data quality issues. Data quality evaluation, data quality analysis, and data quality control are all terms that are used to describe this process.

Data mining tools are not tools for data quality assessment and evaluation. Data profiling tools, data validation tools, and data visualization tools are all tools that are used for data quality assessment and evaluation.

Data quality management is the process of developing and implementing data quality standards. Data quality control, data quality assurance, and data quality governance are all terms that are used to describe this process.

Using data quality tools is not a responsibility of data quality managers. Developing data quality standards, monitoring data quality, and improving data quality are all responsibilities of data quality managers.