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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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In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

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When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

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

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...