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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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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

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New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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 ...