Skip to main content

Observability Tools Fall Short

Shannon Weyrick
NS1

As companies generate more data across their network footprints, they need network observability tools to help find meaning in that data for better decision-making and problem solving. It seems many companies believe that adding more tools leads to better and faster insights. Earlier this year, the research firm Enterprise Management Associates (EMA) found more than 35% of organizations used 11 or more tools for network operations, and more than 50% used six or more.

And yet, observability tools aren't meeting many companies' needs. In fact, adding more tools introduces new challenges. Only one in four companies say they are successful with their network observability tools, according to a recent EMA and NS1 survey of IT stakeholders, and just 15.2% can identify and fix every network issue before it harms the organization.

Observability strategies are being held back both by the strategies surrounding tool adoption, and the capabilities of the tools themselves. Companies are responding to increased data in ways that add complexity and cost, and networking teams aren't obtaining immediate insight from their observability tools, which leaves them unable to quickly find or remediate network issues.

Let's review the data surrounding these shortcomings:

More Data and More Tools Bring Growing Pains

Increasingly complex networks are now generating more data — 85% of firms report that they have recently increased the amount of data they collect — and many companies are eager to take advantage of this increase. But companies can quickly run out of quota or storage space, resulting in either short retention times or substantial cost increases, and 43.5% of respondents say that data storage is now a major challenge.

Networking teams often respond to more data with more tools because their current ones aren't sufficient. More than 50% of respondents said they don't believe they have a single network observability tool that can fully answer any network question. Yet adding more tools often requires expensive customization, according to 54% of respondents, and even once set up is done, 46% say that conflicts between observability tools are a major problem.

Actionable Insights Remain a Work in Progress

Networking teams need observability tools to provide them with immediate insight so they can take action, but in practice, getting insights often requires excessive time and effort. Only one-third of respondents say obtaining a global view of network operations is very easy, and four in five say they are not fully satisfied with the ability to obtain insights from the tools they use. It's no surprise that 84.8% of respondents cannot detect every network issue before problems arise, and 88.8% cannot remediate every issue before problems occur.

Another significant problem is the high rate of false alarms — tool alerts that are ultimately meaningless but require investigation anyway. Remarkably, respondents report that 53% of all alerts are false alarms. This represents a tremendous time sink that likely contributes to three in four respondents saying they are not fully satisfied with their network tooling.

For companies overwhelmed by data storage and a failure to obtain insight, it may be worth deploying observability agents on the edge where data is generated. Such agents can analyze data in real time, so networking teams can bypass the challenges associated with backhauling potentially unused raw data and obtain real-time insight for rapid issue detection and remediation.

Moving forward, it is essential for the people who build network observability tools to understand what networking teams need. This includes deep but dynamically defined data collection with meaningful insights, especially regarding network and application performance, network security, and the end-user experience.

Shannon Weyrick is VP of Research at NS1
APM

Hot Topics

The Latest

64% of enterprise networking teams use internally developed software or scripts for network automation, but 61% of those teams spend six or more hours per week debugging and maintaining them, according to From Scripts to Platforms: Why Homegrown Tools Dominate Network Automation and How Vendors Can Help, my latest EMA report ...

Cloud computing has transformed how we build and scale software, but it has also quietly introduced one of the most persistent challenges in modern IT: cost visibility and control ... So why, after more than a decade of cloud adoption, are cloud costs still spiraling out of control? The answer lies not in tooling but in culture ...

CEOs are committed to advancing AI solutions across their organization even as they face challenges from accelerating technology adoption, according to the IBM CEO Study. The survey revealed that executive respondents expect the growth rate of AI investments to more than double in the next two years, and 61% confirm they are actively adopting AI agents today and preparing to implement them at scale ...

Image
IBM

 

A major architectural shift is underway across enterprise networks, according to a new global study from Cisco. As AI assistants, agents, and data-driven workloads reshape how work gets done, they're creating faster, more dynamic, more latency-sensitive, and more complex network traffic. Combined with the ubiquity of connected devices, 24/7 uptime demands, and intensifying security threats, these shifts are driving infrastructure to adapt and evolve ...

Image
Cisco

The development of banking apps was supposed to provide users with convenience, control and piece of mind. However, for thousands of Halifax customers recently, a major mobile outage caused the exact opposite, leaving customers unable to check balances, or pay bills, sparking widespread frustration. This wasn't an isolated incident ... So why are these failures still happening? ...

Cyber threats are growing more sophisticated every day, and at their forefront are zero-day vulnerabilities. These elusive security gaps are exploited before a fix becomes available, making them among the most dangerous threats in today's digital landscape ... This guide will explore what these vulnerabilities are, how they work, why they pose such a significant threat, and how modern organizations can stay protected ...

The prevention of data center outages continues to be a strategic priority for data center owners and operators. Infrastructure equipment has improved, but the complexity of modern architectures and evolving external threats presents new risks that operators must actively manage, according to the Data Center Outage Analysis 2025 from Uptime Institute ...

As observability engineers, we navigate a sea of telemetry daily. We instrument our applications, configure collectors, and build dashboards, all in pursuit of understanding our complex distributed systems. Yet, amidst this flood of data, a critical question often remains unspoken, or at best, answered by gut feeling: "Is our telemetry actually good?" ... We're inviting you to participate in shaping a foundational element for better observability: the Instrumentation Score ...

We're inching ever closer toward a long-held goal: technology infrastructure that is so automated that it can protect itself. But as IT leaders aggressively employ automation across our enterprises, we need to continuously reassess what AI is ready to manage autonomously and what can not yet be trusted to algorithms ...

Much like a traditional factory turns raw materials into finished products, the AI factory turns vast datasets into actionable business outcomes through advanced models, inferences, and automation. From the earliest data inputs to the final token output, this process must be reliable, repeatable, and scalable. That requires industrializing the way AI is developed, deployed, and managed ...

Observability Tools Fall Short

Shannon Weyrick
NS1

As companies generate more data across their network footprints, they need network observability tools to help find meaning in that data for better decision-making and problem solving. It seems many companies believe that adding more tools leads to better and faster insights. Earlier this year, the research firm Enterprise Management Associates (EMA) found more than 35% of organizations used 11 or more tools for network operations, and more than 50% used six or more.

And yet, observability tools aren't meeting many companies' needs. In fact, adding more tools introduces new challenges. Only one in four companies say they are successful with their network observability tools, according to a recent EMA and NS1 survey of IT stakeholders, and just 15.2% can identify and fix every network issue before it harms the organization.

Observability strategies are being held back both by the strategies surrounding tool adoption, and the capabilities of the tools themselves. Companies are responding to increased data in ways that add complexity and cost, and networking teams aren't obtaining immediate insight from their observability tools, which leaves them unable to quickly find or remediate network issues.

Let's review the data surrounding these shortcomings:

More Data and More Tools Bring Growing Pains

Increasingly complex networks are now generating more data — 85% of firms report that they have recently increased the amount of data they collect — and many companies are eager to take advantage of this increase. But companies can quickly run out of quota or storage space, resulting in either short retention times or substantial cost increases, and 43.5% of respondents say that data storage is now a major challenge.

Networking teams often respond to more data with more tools because their current ones aren't sufficient. More than 50% of respondents said they don't believe they have a single network observability tool that can fully answer any network question. Yet adding more tools often requires expensive customization, according to 54% of respondents, and even once set up is done, 46% say that conflicts between observability tools are a major problem.

Actionable Insights Remain a Work in Progress

Networking teams need observability tools to provide them with immediate insight so they can take action, but in practice, getting insights often requires excessive time and effort. Only one-third of respondents say obtaining a global view of network operations is very easy, and four in five say they are not fully satisfied with the ability to obtain insights from the tools they use. It's no surprise that 84.8% of respondents cannot detect every network issue before problems arise, and 88.8% cannot remediate every issue before problems occur.

Another significant problem is the high rate of false alarms — tool alerts that are ultimately meaningless but require investigation anyway. Remarkably, respondents report that 53% of all alerts are false alarms. This represents a tremendous time sink that likely contributes to three in four respondents saying they are not fully satisfied with their network tooling.

For companies overwhelmed by data storage and a failure to obtain insight, it may be worth deploying observability agents on the edge where data is generated. Such agents can analyze data in real time, so networking teams can bypass the challenges associated with backhauling potentially unused raw data and obtain real-time insight for rapid issue detection and remediation.

Moving forward, it is essential for the people who build network observability tools to understand what networking teams need. This includes deep but dynamically defined data collection with meaningful insights, especially regarding network and application performance, network security, and the end-user experience.

Shannon Weyrick is VP of Research at NS1
APM

Hot Topics

The Latest

64% of enterprise networking teams use internally developed software or scripts for network automation, but 61% of those teams spend six or more hours per week debugging and maintaining them, according to From Scripts to Platforms: Why Homegrown Tools Dominate Network Automation and How Vendors Can Help, my latest EMA report ...

Cloud computing has transformed how we build and scale software, but it has also quietly introduced one of the most persistent challenges in modern IT: cost visibility and control ... So why, after more than a decade of cloud adoption, are cloud costs still spiraling out of control? The answer lies not in tooling but in culture ...

CEOs are committed to advancing AI solutions across their organization even as they face challenges from accelerating technology adoption, according to the IBM CEO Study. The survey revealed that executive respondents expect the growth rate of AI investments to more than double in the next two years, and 61% confirm they are actively adopting AI agents today and preparing to implement them at scale ...

Image
IBM

 

A major architectural shift is underway across enterprise networks, according to a new global study from Cisco. As AI assistants, agents, and data-driven workloads reshape how work gets done, they're creating faster, more dynamic, more latency-sensitive, and more complex network traffic. Combined with the ubiquity of connected devices, 24/7 uptime demands, and intensifying security threats, these shifts are driving infrastructure to adapt and evolve ...

Image
Cisco

The development of banking apps was supposed to provide users with convenience, control and piece of mind. However, for thousands of Halifax customers recently, a major mobile outage caused the exact opposite, leaving customers unable to check balances, or pay bills, sparking widespread frustration. This wasn't an isolated incident ... So why are these failures still happening? ...

Cyber threats are growing more sophisticated every day, and at their forefront are zero-day vulnerabilities. These elusive security gaps are exploited before a fix becomes available, making them among the most dangerous threats in today's digital landscape ... This guide will explore what these vulnerabilities are, how they work, why they pose such a significant threat, and how modern organizations can stay protected ...

The prevention of data center outages continues to be a strategic priority for data center owners and operators. Infrastructure equipment has improved, but the complexity of modern architectures and evolving external threats presents new risks that operators must actively manage, according to the Data Center Outage Analysis 2025 from Uptime Institute ...

As observability engineers, we navigate a sea of telemetry daily. We instrument our applications, configure collectors, and build dashboards, all in pursuit of understanding our complex distributed systems. Yet, amidst this flood of data, a critical question often remains unspoken, or at best, answered by gut feeling: "Is our telemetry actually good?" ... We're inviting you to participate in shaping a foundational element for better observability: the Instrumentation Score ...

We're inching ever closer toward a long-held goal: technology infrastructure that is so automated that it can protect itself. But as IT leaders aggressively employ automation across our enterprises, we need to continuously reassess what AI is ready to manage autonomously and what can not yet be trusted to algorithms ...

Much like a traditional factory turns raw materials into finished products, the AI factory turns vast datasets into actionable business outcomes through advanced models, inferences, and automation. From the earliest data inputs to the final token output, this process must be reliable, repeatable, and scalable. That requires industrializing the way AI is developed, deployed, and managed ...