Skip to main content

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

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 ...

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

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 ...