In today’s data-driven landscape, ensuring high-quality data is paramount for organizations aiming to make informed decisions. However, maintaining data quality is a resource-intensive endeavor. A 2022 survey revealed that data professionals dedicate approximately 40% of their time to evaluating or checking data quality, underscoring the significant investment required. Moreover, Gartner estimates that poor data quality costs organizations an average of $12.9 million annually, highlighting the substantial financial implications. To address these challenges, Artificial Intelligence and Machine Learning have emerged as transformative tools, automating and enhancing various aspects of data quality management.