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Seeing Is Believing - Breaking the Infrastructure Blind Spot

Tim Flower

Cloud is quickly becoming the new normal. The majority of organizations are now running at least one cloud application, and if not, they plan to do so in the near future.

The challenge for organizations is that increased cloud usage means increased complexity, often leading to a kind of infrastructure "blind spot" which puts analytic gains at risk and can obscure more pressing issues. According to a recent Forrester report, the ability to better leverage big data and analytics in business decision-making tops the priority list for organizations adopting the cloud.

So how do companies break the blind spot and get back on track?

Out of Sight?

Many companies are now adopting multiple clouds to leverage the cost-effectiveness of public resources and the granular control of private offerings. But as the Forrester research points out, choosing this route comes with multiple challenges, especially as related to infrastructure and cost visibility: 38 percent of respondents cited difficulty tracking usage across multiple clouds, while 36 percent ran into trouble monitoring costs, and 33 percent spoke to the pain point of managing network performance/latency between clouds and to/from cloud platforms.

Simply put: As cloud networks expand, so does their complexity and existing server monitoring tools aren't up to the task — they were designed to handle finite internal environments, not the ever-changing perimeter of the cloud. Under these conditions, meaningful analytics become virtually impossible since relevant data lies beyond the reach of IT observation.

Flipping the Script

The hybrid cloud breeds complexity, which limits visibility. What's the solution for multi-cloud companies that need the best of both worlds? It all starts with end-users. Think of it like this: We monitor the individual components within our data center infrastructure to make sure we maintain availability and reliability for our end users, but this legacy approach misses a whole host of external sources that can negatively impact the end user experience.

What's more, technology assessment that is strictly data-center focused automatically puts IT teams behind the curve, since end-users experiencing network problems or engaging in risky behavior — such as the use of unsanctioned cloud applications — often don't wait around for logs and error reports to reach technology pros before trying to find their own solution or downloading another app. And when they take this course of action, IT teams have minimal if non-existent methods to effectively identify this behavior and its scope across the enterprise.

As a solution, companies are turning to real user monitoring (RUM) solutions which collect data and metrics at the end-user level directly and in real-time, allowing them to effectively "flip the script" of traditional monitoring techniques. According to the Forrester survey, 77 percent of IT managers believe implementing RUM solutions would be "very effective" or "generally effective" at solving end-user monitoring challenges.

The Analytics Advantage

By adopting hybrid and multiple cloud models, businesses have access to virtually limitless data sources, but this same abundance also creates a natural "blind spot" for IT infrastructure, forcing companies to choose between reduced complexity and better analytics, or large-scale cloud adoption and limited big data effectiveness.

To make sense of all of this data, they are adopting hybrid analytics. And the emergence of flexible, RUM-based tools may suggest a way for companies to increase their visibility without losing their edge. RUM tools enable services, costs and end-users to be monitored in real-time — even as the data they provide is used to improve analytics outcomes.

As the growth of cloud continues, new advances are enabling companies to better leverage the insights gained from these multiple sources of data. Breaking the infrastructure blindspot helps remove some of the challenges of managing a new hybrid cloud-based environment.

The Latest

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When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

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AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

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For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...

Seeing Is Believing - Breaking the Infrastructure Blind Spot

Tim Flower

Cloud is quickly becoming the new normal. The majority of organizations are now running at least one cloud application, and if not, they plan to do so in the near future.

The challenge for organizations is that increased cloud usage means increased complexity, often leading to a kind of infrastructure "blind spot" which puts analytic gains at risk and can obscure more pressing issues. According to a recent Forrester report, the ability to better leverage big data and analytics in business decision-making tops the priority list for organizations adopting the cloud.

So how do companies break the blind spot and get back on track?

Out of Sight?

Many companies are now adopting multiple clouds to leverage the cost-effectiveness of public resources and the granular control of private offerings. But as the Forrester research points out, choosing this route comes with multiple challenges, especially as related to infrastructure and cost visibility: 38 percent of respondents cited difficulty tracking usage across multiple clouds, while 36 percent ran into trouble monitoring costs, and 33 percent spoke to the pain point of managing network performance/latency between clouds and to/from cloud platforms.

Simply put: As cloud networks expand, so does their complexity and existing server monitoring tools aren't up to the task — they were designed to handle finite internal environments, not the ever-changing perimeter of the cloud. Under these conditions, meaningful analytics become virtually impossible since relevant data lies beyond the reach of IT observation.

Flipping the Script

The hybrid cloud breeds complexity, which limits visibility. What's the solution for multi-cloud companies that need the best of both worlds? It all starts with end-users. Think of it like this: We monitor the individual components within our data center infrastructure to make sure we maintain availability and reliability for our end users, but this legacy approach misses a whole host of external sources that can negatively impact the end user experience.

What's more, technology assessment that is strictly data-center focused automatically puts IT teams behind the curve, since end-users experiencing network problems or engaging in risky behavior — such as the use of unsanctioned cloud applications — often don't wait around for logs and error reports to reach technology pros before trying to find their own solution or downloading another app. And when they take this course of action, IT teams have minimal if non-existent methods to effectively identify this behavior and its scope across the enterprise.

As a solution, companies are turning to real user monitoring (RUM) solutions which collect data and metrics at the end-user level directly and in real-time, allowing them to effectively "flip the script" of traditional monitoring techniques. According to the Forrester survey, 77 percent of IT managers believe implementing RUM solutions would be "very effective" or "generally effective" at solving end-user monitoring challenges.

The Analytics Advantage

By adopting hybrid and multiple cloud models, businesses have access to virtually limitless data sources, but this same abundance also creates a natural "blind spot" for IT infrastructure, forcing companies to choose between reduced complexity and better analytics, or large-scale cloud adoption and limited big data effectiveness.

To make sense of all of this data, they are adopting hybrid analytics. And the emergence of flexible, RUM-based tools may suggest a way for companies to increase their visibility without losing their edge. RUM tools enable services, costs and end-users to be monitored in real-time — even as the data they provide is used to improve analytics outcomes.

As the growth of cloud continues, new advances are enabling companies to better leverage the insights gained from these multiple sources of data. Breaking the infrastructure blindspot helps remove some of the challenges of managing a new hybrid cloud-based environment.

The Latest

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...