Guide to Modern Data Cleansing: From Manual Scripts to AI Agents

Data cleansing has traditionally been a manual, time-consuming process involving the detection of issues, exporting problematic records, fixing them in spreadsheets, and reimporting them, leading to a bottleneck in data quality management. Despite advancements in detection technologies such as ML-based anomaly detection and automated profiling, the remediation process has remained largely unchanged, relying heavily on data stewards who manually fix issues without any learning or audit trail. However, the emergence of agentic data cleansing, which leverages AI agents and data contracts, offers a transformative approach by automating the detection and remediation process with human oversight, allowing data stewards to shift from manual corrections to governance roles. This new generation of data cleansing is characterized by its ability to understand context, learn from human feedback, and provide a governed, continuous improvement loop, distinguishing it from previous methods that either lacked adaptability or required extensive configuration and maintenance. As the market for data quality tools is set to grow significantly, agentic cleansing is poised to deliver measurable value by addressing the remediation gap and enabling organizations to manage data quality more effectively and efficiently.