Operationalizing Data Quality with Active Metadata

Operationalizing data quality involves making quality improvements accessible and actionable, largely through the use of metadata to establish standards and map out improvement activities. This process is often complicated by fragmented data across systems and departments, leading to inconsistencies and inaccuracies. A recent webinar featuring data management expert Olga Maydanchik and Select Star CEO Shinji Kim explored strategies for embedding data quality into daily operations and emphasized the importance of data literacy within organizations. They highlighted the shift from traditional metadata management, which relies on manual updates, to active metadata management, characterized by automated, real-time updates that enhance data synchronization and governance. Active metadata not only streamlines processes like data classification, error resolution, and data governance but also enables real-time tracking of schema changes, improving operational efficiency and reducing costs associated with errors. The approach supports machine learning through enhanced data categorization, simplifies root cause analysis, and ensures data observability. Olga stressed the need for continuous learning and collaboration with industry experts to foster a data-literate culture that can adapt to technological advancements in data quality management.