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8 Signs You Have an IT Monitoring Visibility Gap

Mike Marks
Riverbed

When you look at key trends driving the IT market, it's clear that the end user is at the center of converged "next generation" computing services that integrate cloud, mobility, and virtualization. The average workforce user relies on at least 3 devices per day – their mobile phone while they commute to the office, their tablet as they wait in a conference room for a meeting to occur, and their desktop or laptop once they get back to the office.

And the workforce relies on a whole set of applications which may or may not be under IT's control – cloud-delivered apps like Office 365 or Salesforce.com, apps run in data centers owned by outsourcers, not to mention "Shadow IT" apps the user simply decides to download, bypassing the enterprise app store.

The opportunity is clear. IT must manage all of these technologies in a seamless way to ensure they deliver excellent service. To succeed, IT requires visibility into the end user experience as the workforce moves among these various applications and devices throughout their day.

The Challenge – The IT Monitoring Visibility Gap

If the opportunity is clear, so is the challenge to IT. Why is this a challenge? Much of the problem has to do with the siloed nature of most IT monitoring products. Most monitoring and management technology focuses on monitoring the performance and availability of the application components in the infrastructure. Application Performance Management and Systems Management tools focus on web servers, app servers, databases, and hosts. Network and Storage Management tools focus on routers, switches, gateways, and storage infrastructure. Virtual monitoring tools focus on the hypervisor and OS resources. And Mobile Device Management (MDM) and Mobile App Management (MAM) are focused on metrics and analytics having to do with mobile devices and apps, respectively.


The problem with this siloed approach to IT management is that it lacks the perspective of what the end-users, the workforce, are actually experiencing as they use applications to conduct business. These separate monitoring tools can all show "green" to the IT Ops team, indicating satisfactory component performance and availability, when in reality the workforce is still complaining because they are experiencing slow performance on their devices when executing critical business activities, like applying a credit, looking up a patient record, executing a trade, or using a mobile app in the field.

The reason the workforce is still complaining despite the fact that your data center management tools show everything green, is that you can't measure end user experience from the vantage point of the data center "looking out". You can only measure it from the end user's perspective "looking in". That's the primary reason for the "IT Monitoring Visibility Gap" – the gap between what your tools are telling you and what your users are experiencing.

Be Thankful for Those Complaining End Users

Despite the billion dollar a year market for system management tools, analysts like Forrester estimate that 70-80% of problems impacting the end users are not detected by IT. (Forrester IT is a Business Risk). So if you're in IT, you should be thankful if your users complain to you. At least you know you have a problem so you can resolve it. But what about those users who suffer in silence and don't complain to you? That's when the IT Monitoring Visibility Gap becomes really painful.

8 Signs You're Suffering from an IT Monitoring Visibility Gap

Without accurate, real-time information about how end users are actually experiencing and interacting with their applications, devices, and network, you are subject to suffering from an IT Monitoring Visibility Gap.

Mike Marks is VP of Product Marketing at Riverbed

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

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.

8 Signs You Have an IT Monitoring Visibility Gap

Mike Marks
Riverbed

When you look at key trends driving the IT market, it's clear that the end user is at the center of converged "next generation" computing services that integrate cloud, mobility, and virtualization. The average workforce user relies on at least 3 devices per day – their mobile phone while they commute to the office, their tablet as they wait in a conference room for a meeting to occur, and their desktop or laptop once they get back to the office.

And the workforce relies on a whole set of applications which may or may not be under IT's control – cloud-delivered apps like Office 365 or Salesforce.com, apps run in data centers owned by outsourcers, not to mention "Shadow IT" apps the user simply decides to download, bypassing the enterprise app store.

The opportunity is clear. IT must manage all of these technologies in a seamless way to ensure they deliver excellent service. To succeed, IT requires visibility into the end user experience as the workforce moves among these various applications and devices throughout their day.

The Challenge – The IT Monitoring Visibility Gap

If the opportunity is clear, so is the challenge to IT. Why is this a challenge? Much of the problem has to do with the siloed nature of most IT monitoring products. Most monitoring and management technology focuses on monitoring the performance and availability of the application components in the infrastructure. Application Performance Management and Systems Management tools focus on web servers, app servers, databases, and hosts. Network and Storage Management tools focus on routers, switches, gateways, and storage infrastructure. Virtual monitoring tools focus on the hypervisor and OS resources. And Mobile Device Management (MDM) and Mobile App Management (MAM) are focused on metrics and analytics having to do with mobile devices and apps, respectively.


The problem with this siloed approach to IT management is that it lacks the perspective of what the end-users, the workforce, are actually experiencing as they use applications to conduct business. These separate monitoring tools can all show "green" to the IT Ops team, indicating satisfactory component performance and availability, when in reality the workforce is still complaining because they are experiencing slow performance on their devices when executing critical business activities, like applying a credit, looking up a patient record, executing a trade, or using a mobile app in the field.

The reason the workforce is still complaining despite the fact that your data center management tools show everything green, is that you can't measure end user experience from the vantage point of the data center "looking out". You can only measure it from the end user's perspective "looking in". That's the primary reason for the "IT Monitoring Visibility Gap" – the gap between what your tools are telling you and what your users are experiencing.

Be Thankful for Those Complaining End Users

Despite the billion dollar a year market for system management tools, analysts like Forrester estimate that 70-80% of problems impacting the end users are not detected by IT. (Forrester IT is a Business Risk). So if you're in IT, you should be thankful if your users complain to you. At least you know you have a problem so you can resolve it. But what about those users who suffer in silence and don't complain to you? That's when the IT Monitoring Visibility Gap becomes really painful.

8 Signs You're Suffering from an IT Monitoring Visibility Gap

Without accurate, real-time information about how end users are actually experiencing and interacting with their applications, devices, and network, you are subject to suffering from an IT Monitoring Visibility Gap.

Mike Marks is VP of Product Marketing at Riverbed

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

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.