Description
Data Cleansing Automation utilizes machine learning algorithms to identify, correct, or remove inaccuracies and inconsistencies within large datasets. This process prepares data for reliable and efficient analysis by automating traditionally time-consuming tasks like duplicate removal, error detection, missing value imputation, and normalization. By applying AI models that learn from patterns, organizations can ensure high-quality data, facilitating accurate reporting and analytics without the extensive manual labor previously required.
