AI Augmented ETL Pipelines for Automated Data Quality Anomaly Detection and Governance
DOI:
https://doi.org/10.22399/ijcesen.4124Keywords:
AI-augmented ETL, Anomaly detection, Data governance, Machine learning pipelines, Data quality assuranceAbstract
As enterprise businesses rely more and more on real-time, high-volume data pipelines, it has become important to ensure data quality, integrity, and compliance. Traditional Extract, Transform, Load (ETL) processes can be deficient in anomaly detection and enforcement of governance, particularly at scale and speed. This article investigates the new AI-enriched ETL pipelines and how they can become a game-changer in ensuring regulatory compliance and automation of data quality assurance. Incorporating machine learning models in data workflows, organizations will enable their processes to dynamically identify anomalies, implement data governance verification, categorize sensitive data, and establish audit traces. Case studies across various industries show an increase in the accurate detection of targets, fewer cases of false positives, and better rates of compliance. The paper also addresses such topics as architectural considerations, major issues related to federated learning (including model drift and explainability), and future research in federated learning and explainable AI. These smart pipelines focus on a transition from reactive observing to highly autonomous, data management structures.
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