Data quality is a large and complex field with many dimensions. Every data quality practitioner needs a foundation of concepts, principles, and terminology that are common in quality management. Building upon that foundation, they need to understand how quality management concepts and principles are applied to data, as well as the language and terminology that specifically apply to data quality.The importance of data quality in the Utility industry is rapidly growing as new use cases for analytics are identified and pursued. Advances in data science and artificial intelligence are providing new opportunities for business improvement and innovation. Processes such as asset management, network operations, cyber security, environmental management, demand forecasting, regulatory compliance, decarbonization, green energy generation and energy storage management are examples of evolving drivers that will benefit significantly from analytics. However, analytics success that enables true business value is heavily dependent on acceptable levels of data quality.Data quality errors in core operational processes often have severe impacts. Using data for analytics, artificial intelligence, and process automation raises the stakes. Quality control, quality assurance, quality measurement, and quality improvement are proven disciplines for success in manufacturing, services, and other industries. Applying those same disciplines for data management just makes sense.If you have any questions please reach out to Leslie Cook at lcook@utilityanalytics.com.