Measuring and Ensuring Data Quality

Data quality can be measured based on industry standards, regulatory requirements, and alignment with business rules. Industry standards provide guidelines on how data should be structured based on recognized norms like ISO standards. Regulatory standards such as GDPR are also crucial to ensuring data quality. Business context is the most important aspect, as data must align with business processes. Adopting the DAMA DMBOK approach involves creating rules around different data quality dimensions. Engagement with business users and leaders from various departments is essential to capture diverse perspectives on data quality. Visualizing data quality metrics and ensuring accessibility is vital for decision-makers. Embedding data quality metrics in reports helps decision-makers assess the data's quality and make informed decisions. Taking a proactive stance on data quality by integrating it into dashboards allows for real-time monitoring and accountability within the organization.