For those IT professionals responsible for modern technology infrastructure, monitoring performance and reliability has never been more important. Not only do systems need to support a myriad of operational needs, but there is also constant pressure to innovate. Whether it’s the opportunities presented by cloud computing and AI or dealing with ubiquitous security challenges, an IT team’s approach to observability plays a major role in organisational agility and competitiveness.
Part of the challenge is that legacy monitoring tools rely on static thresholds. This makes it hard to detect emerging or complex issues and operate reactivel. Not only that, but it lacks the context needed to correlate data across systems for root cause analysis. In contrast, the latest observability tools
extend this functionality to proactive troubleshooting and intelligent alerting powered by AI/ML. Observability is now geared towards wider priorities such as cloud native application monitoring, the performance of microservices and container-based workloads.
The use cases are everywhere. For security professionals, the focus is on threat detection and incident response. At the edge, observability is now a core component of effective technology implementation and management. Organisations bring these capabilities together in what, ideally, is a coherent platform. Doing so delivers actionable insights and supports fast, effective responses across complex environments.
On the edge
Look more closely at what’s happening at the infrastructure edge. Today’s distributed environments are becoming more complex. This trend is driven by organisations looking to process data closer to its source to enable faster, more reliable performance.
But these organisations have thousands or potentially millions of edge devices under their care. This means the impracticalities of legacy systems have become increasingly apparent for tech professionals with competing priorities to address and limited resources to allocate.
Here, the role of observability is to provide the performance and reliability information IT teams require across components’ operational lifecycles. The challenge is to implement a solution capable of handling the enormous volume of data generated by edge infrastructure to ensure comprehensive visibility across diverse geographic locations.
How does this work? Fundamentally, edge observability captures and then utilises telemetry data, including logs, metrics and traces, to monitor the performance state of associated applications and infrastructure. These systems not only gather data but also provide actionable insights that support holistic monitoring across the entire lifecycle of edge components, including services, hardware, applications and networks.
An example is centralised observability, which is used to maintain control over distributed systems, even though these edge technologies will be geographically dispersed. In this context, operators can still manage and respond to issues in real time, ensuring distributed systems perform as required.
The role of OpenTelemetry
Among the most important tools supporting modern observability strategies is OpenTelemetry. As an open source project, it has quickly become a standard approach for cloud native environments, giving developers and operators the ability to consistently collect and transmit telemetry data across an increasingly complex infrastructure landscape. OpenTelemetry establishes the technical groundwork needed to deliver standardised telemetry. But collecting data alone isn’t enough.
This is where observability platforms come in. By integrating capabilities such as AI-powered analytics and anomaly detection, among other features, these platforms make it possible to turn streams of telemetry into insight that informs action. The result is proactive incident resolution, better security outcomes and optimised performance across distributed systems.
Crucially, this also moves the observability conversation away from issues focused around data collection and towards much broader and more concrete business outcomes. Here, the emphasis is on enabling organisations to build resilience, maintain uptime and operate with greater efficiency at the edge and beyond.
To be truly effective, however, cloud-native edge observability must go beyond raw telemetry. On its own, this raw data risks being fragmented and difficult to interpret. Instead, it should be delivered through a platform that combines topology mapping, intelligent correlation, issue detection and automated remediation – providing a real-time view of infrastructure health that’s both comprehensive and actionable.
This matters because user expectations are higher than ever. Organisations expect their edge environments to operate seamlessly, with minimal downtime, consistent performance and effective security. Meeting these demands means observability must evolve from passive data capture to active insight delivery, empowering teams to optimise operations and resolve issues before they escalate – all as part of a culture of organisational resilience and compliance.
- Cybersecurity