Data Quality Metrics for Data Warehouses (or: KPIs for KPIs)

This article discusses data quality metrics for data warehouses, which are essential for improving the reliability and usefulness of data. It introduces intrinsic and extrinsic data quality dimensions that can be used to measure various aspects of data quality. Intrinsic dimensions include accuracy, completeness, consistency, privacy and security, and freshness, while extrinsic dimensions depend on specific use cases and include relevance, reliability, timeliness, usability, and validity. The article suggests starting from the most important use cases for data in an organization to identify relevant metrics and improve data quality over time using a combination of people, process, and technology strategies.