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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. 

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The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

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