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5 Critical Network Management Capabilities for Modern Enterprises

Jay Botelho

Gone are the days when enterprises viewed the network as an assortment of technology infrastructure and assets. It has become a critical component of modern corporate strategy in the digital age, one capable of supporting and driving business operations and growth. The consequences of any kind of IT disruption are severe.

In fact, an hour of downtime can cost businesses anywhere from $300,000 to $540,000 in total, according to Gartner. That's an average of $5,600 per minute (at the low end!). As such, today's IT teams must proactively boost network performance and reliability. Doing so, however, is easier said than done.


Network management teams routinely perform several activities to plan, deploy, upgrade, troubleshoot, maintain, and monitor the network. These processes are all tremendously data-driven and dependent on your team's visibility into and understanding of the data coming from applications, network devices and the traffic traversing the network.

There are many challenges when it comes to collecting, organizing and analyzing this data. The volume, speed and variety of network data can make it difficult and time-consuming to analyze. Today's enterprise networks are vast and intricate, and can obfuscate the data and its context. And the sheer variety of network domains and architectures today makes data analysis much more challenging, especially with specialized tools or siloed data collection.

So what can you do in the face of all this complexity to ensure network experiences and performance levels that satisfy the needs of the business?

The truth is, there's not much you can do if you lack the fundamental capabilities today's digital enterprises require.

Here are five key questions to ask that will serve as a starting point for ensuring your team is up to the task:

1. Can you monitor the entire network?

Today's enterprise IT environments span a wide range of domains, including LAN, WAN, data centers, SD-WAN, cloud, Wi-Fi, applications and distributed campuses. Do you have the visibility you need to monitor and manage the entire hybrid network from end to end, at scale?

Siloed visibility can be terminal in the long run. If you're experiencing performance issues with a specific application or site, the effects can extend across any number of other domains. With so many moving parts to monitor, and blind spots can prevent you from tracking down the root cause and preserving business-critical digital experiences.

Your team must be able to collect and correlate performance data throughout the entire hybrid network. Measuring metrics such as top network users, availability, common traffic patterns, application jitter, latency, and loss, and more will help you establish baseline and trending metrics. This will ensure you can proactively identify abnormalities that might cause downtime or performance issues that impact the business.

2. Do you measure and correlate granular network traffic analytics?

Whether users access key applications hosted in the cloud or on-premises, it's critical to correlate real-time application performance data with end-user experience analytics. This way, your team can avoid analyzing every issue (and false-positive or alarm overloads) that might come up, and focus their valuable time on solving problems that genuinely impact users.

The best way to establish this correlation is with deep, real-time processing and packet-by-packet analysis that present network transactions with performance insights, even for complex, multi-tiered applications. With this level of visibility and network domain awareness, your team should quickly isolate and resolve network performance issues.

3. Are there any application visibility gaps?

There's no way to support a seamless, high-performance digital experience without granular application visibility. Can your team effectively monitor and analyze application paths?

Are you able to discern when network devices cause application performance issues?

These are critical capabilities that require application detailed performance baselines and usage insights and packet-by-packet analysis. Any application monitoring deficiencies can dramatically extend the time it takes you to identify and resolve performance problems that degrade user experiences.

4. Can your team handle tens of thousands of devices?

Large-scale performance management across numerous devices and distributed environments is a business requirement for most enterprises today. Can your team maintain performance at this scale securely and without latency?

If not, this should be a top priority. You must also ensure you're capable of maintaining performance as device and infrastructure monitoring requirements expand due to new computing environments such as SD-WAN deployments, multi-vendor WANs and new public or private cloud implementations.

You need to be able to monitor all current environments and devices, as well as have the network visibility you'll need to support capacity planning to avoid both over- and under-provisioning resources as the business and its IT needs grow.

5. Is AIOps a priority today?

Scale-related performance is critical. If your team hasn't incorporated AIOps to detect, correlate and visualize anomalies, you're stuck in a reactive stance. How can you effectively manage the increasingly complex IT domains you're monitoring without capitalizing on machine learning (ML) to understand and leverage big data trends?

ML algorithms can support critical performance corrections, including determining which voice traffic to prioritize, when to throttle bandwidth, and whether to block a user's access. AIOps can alleviate many of the time-consuming manual components involved in network performance management by detecting any departures from baseline metrics at a level of speed and accuracy human engineers simply can't.

Questions Worth Asking

Networks have never been more complex, and the need for reliable network performance has never been greater. Demands and challenges for enterprise networks and the IT teams that support them will continue to change over time, but your desire to continually re-examine and evolve your approach should remain constant.

To better position your team and business for success in 2021, take a step back and explore the above network performance management considerations. Identify any gaps and assemble a strategy for building any key capabilities that might be absent. Doing so will help ensure you're able to effectively monitor and manage your entire network, proactively remediate performance issues and incidents, improve user experiences and support your business as it grows.

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5 Critical Network Management Capabilities for Modern Enterprises

Jay Botelho

Gone are the days when enterprises viewed the network as an assortment of technology infrastructure and assets. It has become a critical component of modern corporate strategy in the digital age, one capable of supporting and driving business operations and growth. The consequences of any kind of IT disruption are severe.

In fact, an hour of downtime can cost businesses anywhere from $300,000 to $540,000 in total, according to Gartner. That's an average of $5,600 per minute (at the low end!). As such, today's IT teams must proactively boost network performance and reliability. Doing so, however, is easier said than done.


Network management teams routinely perform several activities to plan, deploy, upgrade, troubleshoot, maintain, and monitor the network. These processes are all tremendously data-driven and dependent on your team's visibility into and understanding of the data coming from applications, network devices and the traffic traversing the network.

There are many challenges when it comes to collecting, organizing and analyzing this data. The volume, speed and variety of network data can make it difficult and time-consuming to analyze. Today's enterprise networks are vast and intricate, and can obfuscate the data and its context. And the sheer variety of network domains and architectures today makes data analysis much more challenging, especially with specialized tools or siloed data collection.

So what can you do in the face of all this complexity to ensure network experiences and performance levels that satisfy the needs of the business?

The truth is, there's not much you can do if you lack the fundamental capabilities today's digital enterprises require.

Here are five key questions to ask that will serve as a starting point for ensuring your team is up to the task:

1. Can you monitor the entire network?

Today's enterprise IT environments span a wide range of domains, including LAN, WAN, data centers, SD-WAN, cloud, Wi-Fi, applications and distributed campuses. Do you have the visibility you need to monitor and manage the entire hybrid network from end to end, at scale?

Siloed visibility can be terminal in the long run. If you're experiencing performance issues with a specific application or site, the effects can extend across any number of other domains. With so many moving parts to monitor, and blind spots can prevent you from tracking down the root cause and preserving business-critical digital experiences.

Your team must be able to collect and correlate performance data throughout the entire hybrid network. Measuring metrics such as top network users, availability, common traffic patterns, application jitter, latency, and loss, and more will help you establish baseline and trending metrics. This will ensure you can proactively identify abnormalities that might cause downtime or performance issues that impact the business.

2. Do you measure and correlate granular network traffic analytics?

Whether users access key applications hosted in the cloud or on-premises, it's critical to correlate real-time application performance data with end-user experience analytics. This way, your team can avoid analyzing every issue (and false-positive or alarm overloads) that might come up, and focus their valuable time on solving problems that genuinely impact users.

The best way to establish this correlation is with deep, real-time processing and packet-by-packet analysis that present network transactions with performance insights, even for complex, multi-tiered applications. With this level of visibility and network domain awareness, your team should quickly isolate and resolve network performance issues.

3. Are there any application visibility gaps?

There's no way to support a seamless, high-performance digital experience without granular application visibility. Can your team effectively monitor and analyze application paths?

Are you able to discern when network devices cause application performance issues?

These are critical capabilities that require application detailed performance baselines and usage insights and packet-by-packet analysis. Any application monitoring deficiencies can dramatically extend the time it takes you to identify and resolve performance problems that degrade user experiences.

4. Can your team handle tens of thousands of devices?

Large-scale performance management across numerous devices and distributed environments is a business requirement for most enterprises today. Can your team maintain performance at this scale securely and without latency?

If not, this should be a top priority. You must also ensure you're capable of maintaining performance as device and infrastructure monitoring requirements expand due to new computing environments such as SD-WAN deployments, multi-vendor WANs and new public or private cloud implementations.

You need to be able to monitor all current environments and devices, as well as have the network visibility you'll need to support capacity planning to avoid both over- and under-provisioning resources as the business and its IT needs grow.

5. Is AIOps a priority today?

Scale-related performance is critical. If your team hasn't incorporated AIOps to detect, correlate and visualize anomalies, you're stuck in a reactive stance. How can you effectively manage the increasingly complex IT domains you're monitoring without capitalizing on machine learning (ML) to understand and leverage big data trends?

ML algorithms can support critical performance corrections, including determining which voice traffic to prioritize, when to throttle bandwidth, and whether to block a user's access. AIOps can alleviate many of the time-consuming manual components involved in network performance management by detecting any departures from baseline metrics at a level of speed and accuracy human engineers simply can't.

Questions Worth Asking

Networks have never been more complex, and the need for reliable network performance has never been greater. Demands and challenges for enterprise networks and the IT teams that support them will continue to change over time, but your desire to continually re-examine and evolve your approach should remain constant.

To better position your team and business for success in 2021, take a step back and explore the above network performance management considerations. Identify any gaps and assemble a strategy for building any key capabilities that might be absent. Doing so will help ensure you're able to effectively monitor and manage your entire network, proactively remediate performance issues and incidents, improve user experiences and support your business as it grows.

Hot Topics

The Latest

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 ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...