A Comprehensive Overview Of Data Quality Monitoring

The role of data quality monitoring has grown significantly as data becomes increasingly vital in the digital age. Poor data quality can lead to significant business damages such as poor application performance, compliance issues, customer churn, and revenue loss. This article provides a comprehensive overview of data quality monitoring, including strategies for continuous assessment and validation. High-quality data is characterized by six key attributes: accuracy, completeness, consistency, timeliness, validity, and integrity. Monitoring the quality of your data involves measuring, analyzing, and improving data quality to fit business purposes and meet business expectations. Key metrics to assess data quality include ratio of data to errors, number of empty values, data transformation error rate, and amount of dark data. Implementing data quality monitoring involves addressing existing issues, understanding business, data, and technical requirements, defining key metrics, relating metrics to data assets and entities, and choosing a data quality monitoring solution.