Data Quality Management
Data Quality Management (DQM) is a systematic approach to ensuring data is accurate, consistent, complete, reliable, and timely for its intended use. It involves processes, policies, and tools to measure, monitor, and improve data quality across an organization's data lifecycle. This methodology helps maintain data integrity, supports decision-making, and ensures compliance with regulations.
Developers should learn Data Quality Management when building data-intensive applications, data pipelines, or analytics systems to prevent errors, reduce costs from bad data, and enhance user trust. It is crucial in industries like finance, healthcare, and e-commerce where data accuracy directly impacts operations and compliance. Use cases include data validation in ETL processes, implementing data quality rules in databases, and monitoring data drift in machine learning models.