Skip to main content

What's Goin' On? Identifying and Fixing Data Blind Spots

James Gillies

A major IT problem for multinationals and small organizations alike is the proliferation of data blind spots, a result of the increasing divergence among IT platforms (cloud, hosted, virtualized, PC, etc.). Most departments within organizations are affected, including technical, financial, legal, compliance and security, as well as external stakeholders.

Recent advances in end-point monitoring are going in the direction of precise user experience measurement as opposed to conventional performance/machine-level monitoring. Knowing that systems are up and running is not the same as knowing that users are getting efficient use of them. These two elements generate data streams revealing distinct realities; while an enterprise's APM tools may indicate that specific applications are humming right along, it may not reveal frequent, frustrating wait times and access problems encountered across much of internal users' usage time.

Efficiently solving this problem requires a system-wide view – across all physical, virtual or hosted platforms, that details the difficulties bedeviling each end user (i.e., employee) or group of users. Scalability also mandates that such a solution be easily deployed and have a negligible footprint – meaning that it does not itself contribute to delays. Most importantly, the data produced must be of high-quality and readily attributable.

A lightweight solution that integrates easily with existing systems, requires no reconfiguration or added equipment and keeps data within an organization’s control yields the best results while raising the fewest concerns, for all the reasons mentioned. In fact, that last issue of data control, is a significant consideration influencing buying decisions in enterprises that are under increasing pressure to avoid all unnecessary routes by which data may travel out of its protective control.

Workforce productivity and morale are concerns that reinforce the increasing urgency of obtaining end user experience data at scale, enabling better and faster decisions, whether related to security, compliance or efficiency. Every enterprise wants to detect and mitigate risks associated with internal IT usage. Yet in anticipating these situations, managers must be cognizant of the sensitivities of employees concerned about unnecessary intrusions on privacy. Such solutions should allow flexibility, providing management the ability to select what data is collected. Having privacy filters that shield employees’ identities can be a morale booster, aligning loyal employees with adept managers in protecting the enterprise.

The goal is not to win the argument over IT's effectiveness but to make it unnecessary through objective, real-time, system-wide reporting of user experience data – at scale. Achieving this level of visibility enables many key enterprise goals; it begins with finding and fixing the data blind spots and empowers faster and better decisions.

James Gillies is Head of Technical at Logfiller .

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

What's Goin' On? Identifying and Fixing Data Blind Spots

James Gillies

A major IT problem for multinationals and small organizations alike is the proliferation of data blind spots, a result of the increasing divergence among IT platforms (cloud, hosted, virtualized, PC, etc.). Most departments within organizations are affected, including technical, financial, legal, compliance and security, as well as external stakeholders.

Recent advances in end-point monitoring are going in the direction of precise user experience measurement as opposed to conventional performance/machine-level monitoring. Knowing that systems are up and running is not the same as knowing that users are getting efficient use of them. These two elements generate data streams revealing distinct realities; while an enterprise's APM tools may indicate that specific applications are humming right along, it may not reveal frequent, frustrating wait times and access problems encountered across much of internal users' usage time.

Efficiently solving this problem requires a system-wide view – across all physical, virtual or hosted platforms, that details the difficulties bedeviling each end user (i.e., employee) or group of users. Scalability also mandates that such a solution be easily deployed and have a negligible footprint – meaning that it does not itself contribute to delays. Most importantly, the data produced must be of high-quality and readily attributable.

A lightweight solution that integrates easily with existing systems, requires no reconfiguration or added equipment and keeps data within an organization’s control yields the best results while raising the fewest concerns, for all the reasons mentioned. In fact, that last issue of data control, is a significant consideration influencing buying decisions in enterprises that are under increasing pressure to avoid all unnecessary routes by which data may travel out of its protective control.

Workforce productivity and morale are concerns that reinforce the increasing urgency of obtaining end user experience data at scale, enabling better and faster decisions, whether related to security, compliance or efficiency. Every enterprise wants to detect and mitigate risks associated with internal IT usage. Yet in anticipating these situations, managers must be cognizant of the sensitivities of employees concerned about unnecessary intrusions on privacy. Such solutions should allow flexibility, providing management the ability to select what data is collected. Having privacy filters that shield employees’ identities can be a morale booster, aligning loyal employees with adept managers in protecting the enterprise.

The goal is not to win the argument over IT's effectiveness but to make it unnecessary through objective, real-time, system-wide reporting of user experience data – at scale. Achieving this level of visibility enables many key enterprise goals; it begins with finding and fixing the data blind spots and empowers faster and better decisions.

James Gillies is Head of Technical at Logfiller .

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...