As IT environments continue to expand in both size and complexity, IT teams are faced with a growing array of events, risks, and alerts. The speed in which IT teams address anomalies is measured by MTTR: Mean Time to Respond, Repair, Resolve, and Recover. The goal, of course, is to address issues as quickly as possible to prevent outages and downtime.
When IT operations are highly siloed and manual, it can lead to an increase in MTTR, as IT leaders must switch between several different monitoring tools to identify and resolve the root cause of incidents. According to recent research, half of organizations work with disparate tools and siloed data, while almost 40% struggle to automate complex repair workflows due to poor visibility and a lack of critical context. In turn, IT environments are left in bad health and IT teams are left with increased costs, downtime, and workloads.
AIOps—the application of machine learning (ML) and artificial intelligence (AI) towards automating operations—addresses these concerns by unleashing lightning-fast MTTR. It also enables IT teams to increase their trust in automation, laying the foundation for a shift to machine-assisted IT operations in line with the journey to AIOps maturity and Autonomic IT. Let’s take a closer look at how AIOps can reduce MTTR and improve IT operations writ large.
The Path to Faster MTTR
The path to using AIOps to reduce MTTR is clear. To start, the ScienceLogic AI Platform consolidates tools to provide comprehensive observability. By busting silos and relying on machines to collect and analyze data from multiple sources—from the end-user across multiple clouds and data centers—IT teams can eliminate time-consuming and error-prone manual efforts. Additionally, having data centralized makes it easier for IT teams to understand, prioritize, and even anticipate issues before they impact end users.
Once data is aggregated, ML and AI can be used for anomaly detection. Skylar Automated Root Cause Analysis (RCA), part of the Skylar AI suite of solutions, for instance, uses machine learning to identify unusual patterns that do not conform to expected behavior, including unknown unknowns. This provides always-on, unsupervised monitoring that flags anomalies in real-time and differentiates between minor issues and potential service-impacting incidents.
Skylar Automated RCA also uses AI and ML to automatically ingest and run log analysis on millions to billions of log files across applications. This allows issues to be addressed up to ten times faster. Coupled with another solution from the Skylar AI suite, Skylar Advisor, IT teams can accelerate the time it takes to understand the circumstances by reviewing a summary and recommended actions for mitigating what went wrong is offered in laymen’s terms, ensuring that whatever’s broken actually gets fixed.
The Benefits of AIOps
When addressing anomalies and reducing MTTR with AIOps, the benefits extend beyond just speed. For example, there’s a huge cost benefit to being able to minimize MTTR. The degradation of operations can be extremely expensive; research shows the average cost of unplanned downtime can easily reach $5,600 and $9,000 per minute. Thus, using AI to eliminate manual processes and minimize downtime also creates tremendous cost savings.
Additionally, the complete visibility enabled by integrated data and comprehensive monitoring lets IT teams more easily modernize and simplify tech sprawl. Having end-to-end service visibility makes it easier to assess service impacts when they arise and makes it easier to improve network configurations for faster resolution. By automating critical tasks across multi-vendor architecture, IT teams can increase availability, security, and compliance.
Automating monitoring and RCA to streamline MTTR also frees IT professionals from manual work, allowing them to focus more on innovation, and represents an important step towards achieving Autonomic IT’s truly self-healing, self-optimizing IT ecosystems that seamlessly support business operations. The guidance provided by Skylar Advisor can also help enable more junior engineers to tackle more complex issues, upleveling their skill set.
The bottom line is that machines can provide the always-on supervision and attention to detail that exceeds human capabilities—and free humans up for higher-value work. The ScienceLogic AI Platform, along with Skylar AI, can help IT teams minimize time-consuming, manual processes that impede MTTR. The platform has been shown to reduce manual work by up to 75% and reduce MTTR by 93%, in turn enabling millions of dollars of cost savings. That’s because it eliminates data siloes, enables end-to-end visibility, and automatically pinpoints issues and anomalies as they arise. Teams can understand what’s happening quickly, even if they don’t speak log.
If you’re interested in cracking the code to lightning-fast MTTR, request a demo today.
