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5 Steps to Enhancing Network Observability for Your NOC

Jeremy Rossbach

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences.

A successful network observability practice means improving operational efficiency through baby steps. There is no reason you need to boil the ocean here and rip and replace your current toolsets or processes. But the more network complexity you deal with (software-defined tech, work from anywhere, public network usage, cloud), the more you need to continually improve network operations to stay ahead of this complexity.

Image
Broadcom

Stage 1 The Hyper-Reactive NOC

To combat swivel chair monitoring and hyper-reactive triage due to way too many monitoring toolsets, look for ways to Integrate or consolidate toolsets. 80% of orgs report a high priority to consolidate while 72% seek tight integration in their tools. Network operations with tight integration across tools have more success with NetOps.

Stage 2 The Reactive NOC

After starting the integrations or consolidations of toolsets, start expanding on the data collected and analytical features supplied by your monitoring solution to start reducing alarm noise and see the bigger picture of network device health. 57% reported they want more unified alerting (centralized alerting) while 56% say more event correlation is needed.

Stage 3 The Proactive NOC

In stage 3, network alert noise is moderate, virtual and software-defined technologies are monitored in silos by vendor-specific tools, leaving no correlation to underlay and overlay network performance. Here you should start to embrace AI-driven network observability solutions that have domain expertise in public cloud networks, WAN overlays, WAN underlays, Wi-Fi, and data center fabrics. 95% of respondents report that they don't get all of the ISP information they need to triage effectively.

Stage 4 The Predictive NOC

Here, you are doing a great job at collecting data across on-prem and public network infrastructure for end-to-end triage of network experiences, false alerts are rare and advanced analytics (AI/ML) is enabling predictive management with baselining, and anomaly detection. Consider expanding your observability into synthetics and web testing capabilities to extend visibility into public networks and an overall broader collection of data to enable proactive monitoring. Also, look to start adopting telemetry features to stream real-time events into a centralized event mgmt/analytics/reporting and automated workflows for traffic engineering, troubleshooting and network performance optimization.

Stage 5 The Automated NOC

In stage 5, you have full visibility across private and public networks to understand network performance at every hop in the end-to-end network path, advanced analytics for alarm noise reduction, configuration management and synthetic testing to evaluate the resilience of your network and public cloud and ISP networks.

Consider "low hanging fruit" network automation use cases like network configuration roll backs to known good state, enriching alarms with powerful event data or automated escalation of issues to level 2 or level 3 engineers and architects.

A mature network observability practice for your NOC  maps a progression from fragmented toolsets with limited coverage to a more integrated, platform approach with coverage for modern, hybrid networks and advanced analytics. As your network operations teams progress along this model, you can shift from reactive postures where most of their time is spent on responding to and troubleshooting alerts to a proactive posture where you are detecting and resolving problems before the business is impacted. 

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5 Steps to Enhancing Network Observability for Your NOC

Jeremy Rossbach

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences.

A successful network observability practice means improving operational efficiency through baby steps. There is no reason you need to boil the ocean here and rip and replace your current toolsets or processes. But the more network complexity you deal with (software-defined tech, work from anywhere, public network usage, cloud), the more you need to continually improve network operations to stay ahead of this complexity.

Image
Broadcom

Stage 1 The Hyper-Reactive NOC

To combat swivel chair monitoring and hyper-reactive triage due to way too many monitoring toolsets, look for ways to Integrate or consolidate toolsets. 80% of orgs report a high priority to consolidate while 72% seek tight integration in their tools. Network operations with tight integration across tools have more success with NetOps.

Stage 2 The Reactive NOC

After starting the integrations or consolidations of toolsets, start expanding on the data collected and analytical features supplied by your monitoring solution to start reducing alarm noise and see the bigger picture of network device health. 57% reported they want more unified alerting (centralized alerting) while 56% say more event correlation is needed.

Stage 3 The Proactive NOC

In stage 3, network alert noise is moderate, virtual and software-defined technologies are monitored in silos by vendor-specific tools, leaving no correlation to underlay and overlay network performance. Here you should start to embrace AI-driven network observability solutions that have domain expertise in public cloud networks, WAN overlays, WAN underlays, Wi-Fi, and data center fabrics. 95% of respondents report that they don't get all of the ISP information they need to triage effectively.

Stage 4 The Predictive NOC

Here, you are doing a great job at collecting data across on-prem and public network infrastructure for end-to-end triage of network experiences, false alerts are rare and advanced analytics (AI/ML) is enabling predictive management with baselining, and anomaly detection. Consider expanding your observability into synthetics and web testing capabilities to extend visibility into public networks and an overall broader collection of data to enable proactive monitoring. Also, look to start adopting telemetry features to stream real-time events into a centralized event mgmt/analytics/reporting and automated workflows for traffic engineering, troubleshooting and network performance optimization.

Stage 5 The Automated NOC

In stage 5, you have full visibility across private and public networks to understand network performance at every hop in the end-to-end network path, advanced analytics for alarm noise reduction, configuration management and synthetic testing to evaluate the resilience of your network and public cloud and ISP networks.

Consider "low hanging fruit" network automation use cases like network configuration roll backs to known good state, enriching alarms with powerful event data or automated escalation of issues to level 2 or level 3 engineers and architects.

A mature network observability practice for your NOC  maps a progression from fragmented toolsets with limited coverage to a more integrated, platform approach with coverage for modern, hybrid networks and advanced analytics. As your network operations teams progress along this model, you can shift from reactive postures where most of their time is spent on responding to and troubleshooting alerts to a proactive posture where you are detecting and resolving problems before the business is impacted. 

Hot Topics

The Latest

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...