AI for Continuous Data Quality Monitoring and Anomaly Detection in Data Pipelines
DOI:
https://doi.org/10.15680/IJCTECE.2025.0801002Keywords:
AI, Continuous Data Quality Monitoring, Anomaly Detection, Data Pipelines, Machine Learning, Unsupervised Learning, Deep Learning, Real-time Monitoring, Data Integrity, AutomationAbstract
In the era of big data, ensuring the quality of data as it traverses complex pipelines is paramount. Traditional manual checks are insufficient for the scale and speed of modern data workflows. Artificial Intelligence (AI) offers a transformative approach to continuous data quality monitoring and anomaly detection. By leveraging machine learning (ML) algorithms, AI can autonomously identify inconsistencies, outliers, and errors in real-time, ensuring data integrity throughout its lifecycle. This paper explores the integration of AI into data pipelines for continuous quality assurance. We examine various AI techniques, including unsupervised learning models like autoencoders and clustering algorithms, which do not require labeled data and can detect novel anomalies. Additionally, we discuss the application of deep learning models that can capture complex patterns in data. The effectiveness of these AI-driven methods is evaluated against traditional rule-based systems, highlighting improvements in accuracy, scalability, and responsiveness. Furthermore, we address the challenges associated with implementing AI in data pipelines, such as data drift, model interpretability, and the need for continuous model retraining. The paper also presents case studies demonstrating the successful deployment of AI for anomaly detection in various industries, including finance, healthcare, and e-commerce. These real-world applications underscore the potential of AI to enhance data quality monitoring and anomaly detection, leading to more reliable and efficient data-driven decision-making processes.
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