AI-Driven Adaptive Data Cleansing: Automating Error Detection and Correction for Dynamic Datasets

This study presents an adaptive data cleansing framework powered by artificial intelligence (AI) to address the challenges of maintaining data quality in dynamic and large-scale datasets. Traditional data cleansing methods are limited in handling real-time data inconsistencies and evolving patterns, making them unsuitable for modern applications. With minimal human intervention, the proposed AI-driven algorithms autonomously detect and correct errors, including missing values, anomalies, and inconsistencies. By leveraging machine learning, pattern recognition, and statistical techniques, the framework continuously adapts to data changes, ensuring high accuracy and integrity. This research highlights the novelty of integrating AI to automate data quality management, outperforming static rule-based systems by dynamically refining cleansing strategies based on incoming data. The study demonstrates that these adaptive algorithms reduce operational costs, enhance scalability, and improve decision-making across various industries, making them critical innovations in AI, data quality, and automation.