Revolutionizing Data Quality: A Scalable Approach to Intelligent Data Observability

Revolutionizing Data Quality: A Scalable Approach to Intelligent Data Observability

Authors- Professor Dr. Renuka Devi M, Vignesh Lokesh

Abstract--In today’s digital landscape, maintaining data accuracy, reliability, and availability is vital for effective decision-making and analytics. Data Observability provides a structured methodology for monitoring and ensuring data quality across pipelines. This paper delves into the core principles, methodologies, and tools of Data Observability, emphasizing its significance in preventing data failures, ensuring compliance, and enhancing operational efficiency. We propose framework that leverages machine learning techniques to detect anomalies and optimize data pipeline performance. The framework’s effectiveness is validated through experimental evaluations, demonstrating its capability to identify and address data inconsistencies in real-time.

DOI: /10.61463/ijset.vol.13.issue3.162