Proactive monitoring is an IT monitoring approach in which systems, applications, and networks are continuously observed and analyzed to detect potential issues, anomalies, or performance degradations before they escalate into significant problems or outages. It involves setting up monitoring tools and practices that enable the early detection of issues and allow timely intervention to prevent disruptions and ensure optimal system performance and availability.
Proactive monitoring is the opposite of reactive monitoring. While reactive monitoring detects failures after something has gone wrong, proactive monitoring alerts IT administrators to potential issues well before the issue becomes a failure.
By avoiding business impacting failures, proactive monitoring helps to enhance service uptime and performance, increases customer trust, and enables increased efficiency in IT operations.
Proactive /prəʊˈaktɪv/
Adjective: (of a person or action) creating or controlling a situation rather than just responding to it after it has happened.
Synthetic transaction testing simulates users by using “robot users” to try and access resources or run applications. As synthetic monitoring is usually run 24×7, it may be able to detect problems before real users encounter them because it is accessing the application/IT infrastructure at times when real users may not. For example, a logon failure at 3am may be detected by synthetic monitoring. In a 9-to-5 business real users are unlikely to try to login at that time, so they would not notice the issue until they login at 9 am. This gives administrators the chance to rectify the issue long before real users notice.
However, whilst adhering to the ethos of preventing real users encountering issues, synthetic monitoring simply isn’t true “proactive monitoring” as all you have done is discover a problem that has already occurred. Indeed, if an issue occurs at 10am it is likely that the robot users encounter the problem at the same time that real users are affected.
True proactive monitoring should be able to detect problems in advance, even when users are actively accessing the applications/infrastructure being monitored. Best practice proactive monitoring usually combines Real User Monitoring (RUM) with synthetic monitoring, read more: What is Proactive Monitoring and Why it is Important (eginnovations.com).
Establishing what is “healthy” for your systems and applications is an essential step in proactive monitoring strategy.
The early identification of problem behavior is critical to heading off incidents that cause downtime and create negative perceptions of application performance impacting business operations. To identify the earliest warning signs of anomalous behavior, the observability and monitoring tools in place need a good baseline of normal health and behavior to detect the first symptoms of deviation from the normal.
AIOps based observability platforms such as eG Enterprise incorporate auto-baselining technologies which learn about the normal behavior and patterns of operation of IT systems and applications. Incorporating machine learning technologies, these systems understand correlations and dependencies at scales far beyond human operator capabilities.
Beyond this metric thresholds and alerting should be in place to ensure that the human operator is alerted to the earliest warning signs of issues. Platforms such as eG Enterprise automate the setting and deployment of thresholds and alerting but some legacy tools may require manual configuration. Advice on threshold setting and alert configuration best practice is available here: White Paper | Make IT Service Monitoring Simple & Proactive with AIOps Powered Intelligent Thresholding & Alerting (eginnovations.com).
eG Enterprise is built around an AIOps (Artificial Intelligence for IT Operations) platform providing capabilities that form the essence of eG Enterprise’s proactive root cause analysis, anomaly detection and alerting capabilities. You can read more about these capabilities, which will enable you to become more proactive:
