Skip to main content

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

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

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

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...