Establishing a Systematic Data Quality Framework

Poor data quality costs businesses an average of $12.9 million annually, affecting operational efficiency, decision-making, and customer satisfaction. To mitigate these risks, companies are increasingly turning to Data Quality Frameworks (DQFs), which provide structured guidelines to ensure data remains accurate, consistent, and trustworthy throughout its lifecycle. Key components of a DQF include data quality standards, governance policies, automated tools, continuous monitoring, and ethical data practices. By implementing a robust DQF, businesses can safeguard their data and drive better outcomes.