AIOps and machine learning can cut out noise and simplify network observability

Building on traditional monitoring solutions, smart observability tools cut out the noise and boost IT efficiency, performance, resilience and proactivity

Traditional network monitoring solutions aggregate and display data that tech professionals rely on to determine whether their systems are operating within expected limits.

However, to convey this much-needed information, traditional monitoring often issues too many alerts. Although some alerts are vital, the rest are just noise. This can create unnecessary complexity as organizations start to manage diverse, complex, and distributed networks, especially in the hybrid-IT era. Filtering out the noise can be a job all on its own now.

As traditional monitoring may not no longer be adequate for new hybrid cloud environments many IT teams are facing challenges as they do not have easy visibility into their organization’s apps and infrastructure. This can impact their ability to quickly and easily conduct anomaly detection, root-cause analysis, and other critical processes in their work. They need the alerts without the noise. With less noise, tech pros can the find a signal quickly.

In addition, traditional monitoring uses metrics-oriented dashboards to assess telemetry data against manual or basic statistically relevant thresholds. This kind of monitoring typically focuses on a specific network, cloud, infrastructure, or application element so that IT teams can identify anomalies, investigate problems, and discover solutions. However, this approach has its limits. It does not offer cross-domain correlation, service delivery insight, operational dependencies, or predictability.

To make things worse, monitoring silos will develop in time. This is where observability solutions can help.

Integrating observability with intelligence

The solution to improving observability is to use ‘time series analysis’, which offers IT professionals guidance in real time, on exactly when and where action is required. This enables organizations to gain end-to-end oversight of their service delivery and component dependencies. This kind of observability solution is useful in multi-cloud environments because they leverage AIOps and ML to analyze the noise and the deluge of accumulated data to help IT teams to quickly and effectively manage services supporting customers and employees.

In short: observability transforms analysis in seconds instead of hours.

While observability solutions will not replace traditional monitoring, they use the information gathered through monitoring as a critical input. Observability solutions analyze the collected data and compares it with expected outcomes and objectives. IT professionals can then gain a  better understanding of the state of their infrastructure and applications.

Observability solutions with AIOps and ML can go even further by delivering predictive analytics. An observability platform can detect a potential problem before it occurs and automatically respond to it independently. Only when a human administrator needs to be involved will the observability solution alert the team to intervene. At this point, the embedded AIOps and ML provides the necessary insights, automated analytics, and actionable intelligence to guide people to spot the signal and the solution easily. The benefits of AIOps and ML in observability automation are:

• Throughout the observability process, operational noise is reduced, allowing tech, DevOps and security teams to become more proactive in discovering issues and anomalies.
• Tasks can be automated and tuned with advanced closed-loop operational management, reporting, and capacity planning efficiencies across IT domains.
• Enhanced business agility due to quick identification and diagnosis of problems and deficiencies. IT teams can characterize and predict impactful business service, component, and activity-state changes. Scalability and low administrative overhead are also a given.
• With IT efficiency optimized, IT eliminate redundant tools, reduce costs and become proactive instead of reactive.
• Finally, observability allows teams to visualize and continuously analyze business service and component relationships, deviations, and dependencies. As a result, they also proactively help the organization improve performance, compliance, and resilience.

The strategic benefits of observability

With hybrid remote work here to stay, and with the rise of SaaS apps and ubiquitous smart devices, the loss of connectivity can lead to poor workplace communication, a site going dark, or large-scale disruption.

AIOps and ML help observability solutions provide dynamic protection against these and other difficulties.

But observability with AIOps and ML is not one more ‘thing’ or just another technology thrown into the stack. Instead, it is a next-generation, integrated IT infrastructure, application, and database performance management solution.  

Organizations of any size can boost monitoring with intelligent observability to understand and manage IT service delivery more easily and holistically. It is also cost-effective because the benefits bring more improved performance and reliability per dollar invested. This enhances the employee and customer experience across complex, diverse, distributed hybrid and cloud environments.