How AI Is Transforming Data Quality Management

Poor data quality can cost businesses an average of $12.9 million annually, making it crucial for organizations to invest in effective data quality management strategies. Traditional methods struggle to keep up with the constant growth in data volumes, but AI has revolutionized how companies ensure data quality by processing massive datasets, detecting patterns, and automating tasks. AI-driven solutions automate data profiling, anomaly detection, and data cleansing, making these processes more efficient and scalable. Key benefits of AI-driven data quality management include increased accuracy, cost reduction, and improved efficiency. However, organizations must overcome challenges such as data privacy and security, AI bias, and legacy system integration for a smooth transition to AI data governance.