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

Uniqueness analysis is used to identify duplicate records by comparing the values of unique identifiers or key fields.

Data cleansing is the process of correcting or modifying data to ensure its accuracy and consistency.

Data deduplication is used to remove duplicate records from a dataset by identifying and eliminating them.

Data integration is the process of ensuring that data is consistent across different systems and applications.

Data governance focuses on preventing errors from entering the data in the first place by establishing policies, standards, and procedures for data management.

Data validation is the process of verifying and validating data to ensure its accuracy and completeness.

Data quality monitoring focuses on monitoring data quality and taking corrective actions when necessary.

Data standardization is the process of transforming data into a consistent format and structure.

Data quality improvement focuses on improving the overall quality of data over time by implementing continuous improvement initiatives.

Data cleansing is the process of identifying and correcting errors and inconsistencies in data.

Data quality collaboration focuses on involving stakeholders in the data quality management process.

Data transformation is the process of converting data into a format that is suitable for analysis and reporting.

Data quality standardization focuses on establishing a common understanding of data quality requirements.

Data deduplication is the process of identifying and removing duplicate or unnecessary data from a dataset.