Automating Data Observability Metrics with Splunk ML AI: A Technical Analysis - Prabhu Govindasamy Varadaraj, (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025)
This technical article demonstrates the implementation of Splunk's Machine Learning (ML) and Artificial Intelligence (AI) capabilities in automating data observability metric evaluation. The integration of ML/AI algorithms enables organizations to enhance system performance through proactive issue detection and automated response mechanisms. By leveraging Splunk's comprehensive toolset, including the Machine Learning Toolkit (MLTK) and IT Service Intelligence (ITSI), organizations can transform traditional monitoring practices into dynamic, predictive solutions. The article illustrates how automated observability platforms streamline incident detection, reduce operational costs, and improve overall system reliability. Through real-world implementations, the effectiveness of ML-driven observability in managing complex, distributed systems becomes evident, showcasing the potential for enhanced operational efficiency and reduced mean time to resolution. The transition from reactive to proactive monitoring represents a fundamental shift in system management practices, enabling organizations to maintain comprehensive visibility across their technological infrastructure while optimizing resource utilization.
Keywords: Data Observability; Machine Learning Automation; Predictive Analytics; System Performance Optimization; Incident Detection