Thomas LaRock, Head Geek™, SolarWinds
Tech pros rely on traditional monitoring to determine whether their systems are operating correctly through the aggregation and displaying of data. However, to convey this essential information, an abundance of alerts are generated, translating to a constant stream of noise. While some of these are vital towards addressing a specific problem, the majority are not. Sifting through the ‘not’ is a process that can become a job all on its own.
Complex IT infrastructures have microservice architectures that allow tech pros to observe, monitor, and analyse their organisations’ cloud environments efficiently. Being able to receive the alerts without the noise (and the resulting alert fatigue) is vital for enabling them to find a signal.
Integrating SolarWinds® Hybrid Cloud Observability allows tech pros to filter seasonal trends using time-series analysis so the end-user can receive guidance on exactly where action is required. Organisations can then gain end-to-end oversight of their service delivery and component dependencies. Hybrid Cloud Observability brings real-time control into multi-cloud environments because it integrates with AIOps (artificial intelligence for IT operations) and machine learning (ML).
The embedded AIOps and ML within observability can analyse the noise and the deluge of accumulated data so tech pros can move quickly and effectively to manage services that support customers and employees.
In short: Observability transforms hours of analysis into seconds.
Don’t Replace, Improve
Observability will not replace traditional monitoring. Instead, observability uses the information gathered through monitoring as a critical element to its services.
Traditional monitoring uses metrics-oriented dashboards to assess telemetry data against manual or basic, statistically relevant thresholds. It’s typically focused on a specific network, cloud, infrastructure, or application element so tech pros can identify anomalies, investigate problems, and discover a solution.
But monitoring has its limits. It doesn’t offer cross-domain correlation, service delivery insight, operational dependencies, or predictability. To make things worse, monitoring silos will inevitably develop over time. This is where observability can help.
Observability analyses the collected data and compares it to expected outcomes and objectives. With this data, tech pros are better equipped to understand the state of their infrastructure and applications.
Proactive Versus Reactive
AIOps and ML will allow observability to go further by delivering predictive analytics. An observability solution will detect a potential problem proactively, before it occurs, then automatically respond to it independently.
However, when a tech pro needs to be involved, they can be alerted. In this case, embedded AIOps and ML will provide the necessary insights, automated analytics, and actionable intelligence through cross-domain data correlation, massive real-time and historical metrics, logs, and trace data. This is where you spot the signal, making the end solution easier to find.
Proactivity is maximised across Observation through the reduction of operational noise, allowing tech pros —including DevOps and security teams—to discover issues and anomalies when they should be—before the disruption has occurred. Having shifted away from a reactive stance to a proactive one, teams can then better automate tasks and advance closed-loop operational management, reporting, and capacity planning efficiencies—across IT domains.
Thanks to AIOps and ML, observability advances business agility by allowing tech pros to identify problems and discover deficiencies. They then characterise and predict impactful business service, component, and activity-state changes.
Integrated observability is scalable, has lower administrative overhead, optimises IT efficiency, eliminates redundant tools, and therefore helps reduce costs.
Observability allows teams to visualise and continuously analyse business service and component relationships, deviations, and dependencies. As a result, they also see improvements in performance, compliance, and resilience.
A Next Generation Solution
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 provide dynamic protection against these and other difficulties.
But don’t think of observability as one more “thing” or another technology thrown into the stack. Instead, it’s a next-generation, integrated IT infrastructure, application, and database performance management solution. Like butter, observability makes everything better.
Observability—with integrated AIOps and ML—enables organisations to holistically understand and manage IT service delivery with ease. By promoting continuous improvements in performance and reliability, observability is a worthwhile, cost-effective option for IT organisations of any size. Observability enhances the customer experience across complex, diverse, distributed hybrid and cloud environments and will undoubtedly take traditional monitoring practices to the next level.
