What is Advanced Network Analytics? Cutting Through the Hype

Shamus McGillicuddy

Everyone wants to talk about how analytics is the future of network engineering and operations. The phrase "network analytics" is used by vendors of various stripes to imply that a particular technology is smarter and better than the average solution.

But what is it? What does the term network analytics mean to the enterprise network infrastructure professionals?

Enterprise Management Associates (EMA) recently published survey-based research on this topic. We sought out individuals who were actively involved in so-called network analytics initiatives. To find these people, we presented random IT professionals with a broad definition of a network analytics initiative.

"The implementation of technologies and strategies aimed at extracting previously unrealized insight and value from data produced by and collected from network infrastructure and network traffic. Network analytics supports network and IT engineering and operations use cases. It can also benefit the business via business process optimization and other opportunities."

To qualify for our research survey, IT professionals had to work for an organization that was engaged in an initiative that fit the above definition, and survey respondents had to be directly involved in such an initiative.

Our first goal with this research was to let survey respondents tell me what network analytics means to them. And that’s where the following question comes in.

What network analytics technology strategies does your organization currently use or plan to use?

The average respondent selected slightly more than two of the following multiple-choice options, which suggests that network pros are taking multiple paths toward a network analytics future.

1.Analytics features embedded in network infrastructure (58%)

2. Analytics features embedded in network operations software (56%)

3. Packaged solutions from pure-play network analytics vendors (55%)

4. Packaged solutions from pure-play general analytics vendors (34%)

5. Standalone analytics stacks (homegrown or otherwise) applied to a network data store (18%)

We also asked them to select the one option above that was their primary approach to network analytics. Analytics features embedded in network operations software was the top choice (40%), analytics embedded in infrastructure was second (30%), and pure-play network analytics vendors was third (18%).

This data tells us that IT shops are consuming network analytics from some very familiar vendors. In fact, you might already have your network analytics solution.

Network monitoring tools increasingly leverage new analytics and machine learning algorithms to make their tools more intelligent and useful

Network management software vendors and network hardware vendors are delivering analytics technologies within their existing product portfolios. We’ve seen indications of this for a few years now. Network monitoring tools, for instance, increasingly leverage new analytics and machine learning algorithms to make their tools more intelligent and useful.

Network infrastructure vendors are embedding analytics everywhere, particularly in the data center and the WAN. Nearly every enterprise-grade software-defined WAN (SD-WAN) solution leverages analytics in some way, particularly those with sophisticated traffic steering capabilities and extensive network automation capabilities.

Yes, there are clearly specialized network analytics solutions out there for you to buy, and many enterprises are doing so. But you don’t need to add another vendor to your roster to access the benefits of network analytics today. You may not even have to buy a new product. Your existing vendors are enriching the technology you have already deployed.

The network hardware you already have installed may get smarter in the next few years thanks to new analytics features. The network management and monitoring tools that you have also will get smarter. If it hasn’t happened already, you can expect to see your existing solutions enhanced by process analytics, predictive analytics, data mining, trending, and emulation. And you will also see your vendors leverage machine learning to make their products smarter.

As this industry trend unfolds and embedded analytics proliferate in hardware and management software, some network managers may see gaps in their engineering and operations processes that a solution from a pure-play network analytics vendor can fill. More than half of the IT professionals we surveyed have apparently already responded to this issue by engaging with such analytics vendors.

This research tells us that network analytics isn’t some shiny new thing that you need to buy. It’s more about an evolution and enrichment of existing technology. As you navigate your way around the hype machine, keep that in mind.

Related Links

Hot Topics

The Latest

When AI Becomes the Corporate OS

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

The $600 Billion Wake Up Call

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Breaking Down Agentic AI Fragmentation and Complexity with Governance

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud Spend Is Rising and Cloud Optimization Is Key to Funding AI and Protecting Margins

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

Building AI Agents That Build Trust

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

Keys to Building a Partner Ecosystem That Scales

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 ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

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 ...

AI Deepfakes: Rethinking Trust in the Workplace

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 ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

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 ...

Why ITOps Need Right-Sized AI, Not Bigger Models

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 ...

What is Advanced Network Analytics? Cutting Through the Hype

Shamus McGillicuddy

Everyone wants to talk about how analytics is the future of network engineering and operations. The phrase "network analytics" is used by vendors of various stripes to imply that a particular technology is smarter and better than the average solution.

But what is it? What does the term network analytics mean to the enterprise network infrastructure professionals?

Enterprise Management Associates (EMA) recently published survey-based research on this topic. We sought out individuals who were actively involved in so-called network analytics initiatives. To find these people, we presented random IT professionals with a broad definition of a network analytics initiative.

"The implementation of technologies and strategies aimed at extracting previously unrealized insight and value from data produced by and collected from network infrastructure and network traffic. Network analytics supports network and IT engineering and operations use cases. It can also benefit the business via business process optimization and other opportunities."

To qualify for our research survey, IT professionals had to work for an organization that was engaged in an initiative that fit the above definition, and survey respondents had to be directly involved in such an initiative.

Our first goal with this research was to let survey respondents tell me what network analytics means to them. And that’s where the following question comes in.

What network analytics technology strategies does your organization currently use or plan to use?

The average respondent selected slightly more than two of the following multiple-choice options, which suggests that network pros are taking multiple paths toward a network analytics future.

1.Analytics features embedded in network infrastructure (58%)

2. Analytics features embedded in network operations software (56%)

3. Packaged solutions from pure-play network analytics vendors (55%)

4. Packaged solutions from pure-play general analytics vendors (34%)

5. Standalone analytics stacks (homegrown or otherwise) applied to a network data store (18%)

We also asked them to select the one option above that was their primary approach to network analytics. Analytics features embedded in network operations software was the top choice (40%), analytics embedded in infrastructure was second (30%), and pure-play network analytics vendors was third (18%).

This data tells us that IT shops are consuming network analytics from some very familiar vendors. In fact, you might already have your network analytics solution.

Network monitoring tools increasingly leverage new analytics and machine learning algorithms to make their tools more intelligent and useful

Network management software vendors and network hardware vendors are delivering analytics technologies within their existing product portfolios. We’ve seen indications of this for a few years now. Network monitoring tools, for instance, increasingly leverage new analytics and machine learning algorithms to make their tools more intelligent and useful.

Network infrastructure vendors are embedding analytics everywhere, particularly in the data center and the WAN. Nearly every enterprise-grade software-defined WAN (SD-WAN) solution leverages analytics in some way, particularly those with sophisticated traffic steering capabilities and extensive network automation capabilities.

Yes, there are clearly specialized network analytics solutions out there for you to buy, and many enterprises are doing so. But you don’t need to add another vendor to your roster to access the benefits of network analytics today. You may not even have to buy a new product. Your existing vendors are enriching the technology you have already deployed.

The network hardware you already have installed may get smarter in the next few years thanks to new analytics features. The network management and monitoring tools that you have also will get smarter. If it hasn’t happened already, you can expect to see your existing solutions enhanced by process analytics, predictive analytics, data mining, trending, and emulation. And you will also see your vendors leverage machine learning to make their products smarter.

As this industry trend unfolds and embedded analytics proliferate in hardware and management software, some network managers may see gaps in their engineering and operations processes that a solution from a pure-play network analytics vendor can fill. More than half of the IT professionals we surveyed have apparently already responded to this issue by engaging with such analytics vendors.

This research tells us that network analytics isn’t some shiny new thing that you need to buy. It’s more about an evolution and enrichment of existing technology. As you navigate your way around the hype machine, keep that in mind.

Related Links

Hot Topics

The Latest

When AI Becomes the Corporate OS

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

The $600 Billion Wake Up Call

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Breaking Down Agentic AI Fragmentation and Complexity with Governance

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud Spend Is Rising and Cloud Optimization Is Key to Funding AI and Protecting Margins

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

Building AI Agents That Build Trust

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

Keys to Building a Partner Ecosystem That Scales

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 ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

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 ...

AI Deepfakes: Rethinking Trust in the Workplace

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 ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

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 ...

Why ITOps Need Right-Sized AI, Not Bigger Models

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 ...