What Are the Benefits of AIOps?
IT operations have only become more complex and chaotic over time with cloud migrations, the exploding number of workloads and applications, and workloads moving from one cloud to another. AIOps can help suppress this noise, which would otherwise be overwhelming to IT teams and contribute to alert fatigue.
“AIOps is an incredibly powerful use of AI and machine learning that allows IT professionals, DevOps professionals and help desk agents to do more with less,” says Cullen Childress, group vice president of product at SolarWinds.
The automation capabilities of AIOps help organizations respond more quickly and effectively to any outages, anomalies or security events. Automated detection and response also lowers operational costs. Plus, its reliance on machine learning means it will consistently improve and be able to predict security events before they even happen, giving IT teams the opportunity to be proactive in mitigating operational issues.
“Without AIOps, the alert fatigue and all of this noise is so loud that you really can’t focus on the signal,” Childress says. “With AIOps, those highly paid IT resources can focus on real problems and accelerate the discovery of an issue. That means organizations don’t need to hire more IT professionals to chase these ghosts.”
Scott Pross, vice president of technology at Monalytic, a SolarWinds company, says that he talks to state and local government IT professionals every day. “I hear the same thing: ‘I don’t have the manpower. I don’t have the time to train them. And I don’t have the resources available.’ AIOps makes it so the system can handle that,” he says.
According to Palo Alto Networks, the core purpose of AIOps platforms is to be able to use telemetry — centralizing data from multiple sources — to prevent and detect issues, then use machine learning to adapt and refine the process.
What Can State and Local Governments Use AIOps for?
There are a number of use cases for AIOps including anomaly detection, alert correlation and clustering, intelligent alerting and network monitoring. Using AI and machine learning, a software provider could develop solutions for monitoring infrastructure and reducing the number of alerts created. AIOps software can analyze anomalies in a way that won’t set off an alert unless it determines the anomaly is a threat or follows a unique pattern.
AIOps is also used for to automate and improve remediation. Once an issue is discovered and elevated, AIOps software ensures that all traces, logs, metrics, entity relationships and dependencies are appended to the incident. With that data, and with data from previous incidents, AIOps can automatically identify the root cause of the issue.
AIOps tools do this by taking data and feeding it into models that can be trained to recognize what a particular environment’s normal state is. AIOps software monitors an organization’s full-stack infrastructure, databases, applications, user experiences, logins and other data to detect when something out of the ordinary is happening.
“The system is trying to find individual data points that share qualities and then match that against what it has seen in the past,” Pross says.
With natural language processing capabilities, agencies can also use AIOps tools to improve IT service management by handling help desk requests. As IT service requests become more complex, organizations can either hire more IT staff to keep up, or they can leverage AI, machine learning and natural language processing via ChatOps (chatbots and virtual agents) to assist users automatically with low-level tickets that don’t need to be escalated to human agents.