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Improving Endpoint Performance with Workspace Analytics

Jeff Kalberg

Digital transformation continues its reign as the popular business topic of the day, competing with security for the amount of attention and anxious debate it provokes. A recent Gartner survey notes that 62% of respondents said they had a management initiative or transformation program in place to become a more digital business. A telling note is that 46% of those respondents said the objective of their digital initiative is optimization, which means a large number of all organizations are more focused on delivering a high-fidelity, end-user computing experience while minimizing costs, and on how they can monitor the metrics that impact the delivery of the end-user workspace experience.

Optimization means improving the performance of your human and technology resources while keeping a watchful eye. To accomplish this, we must have clear, crisp visibility into the metrics relevant to the delivery of workspace applications to your end users and to the devices – the endpoints – they use to be productive. This ability to monitor metrics has become even more important as enterprises evolve from traditional to virtualized desktop environments, and the quantity and variety of devices used by an organization's employees continues to grow.

Workspace Analytics and Application Performance Management

To drive digital transformation – and optimization – one of the most important workspace analytics tools is application performance management which allows an organization to objectively monitor the user experience. By providing visibility into all of the components involved in remoting an application, these workspace analytics tools help organizations diagnose and proactively respond to user experience and performance issues before they become problems.

User experience metrics are a fundamental part of workspace analytics, but often the data collection process does not include the endpoint. Only when we link the client-side systems to the workspace analytics data collectors does an organization have a full understanding of application performance within virtualized environments. This richer detail gives IT the information it needs to improve endpoint performance and drive digital transformation.

Focus on Endpoint Performance

Optimization is a noble goal, one that nearly half of the Gartner respondents are treating as a priority. Understanding why the endpoint is such a focus promotes success in achieving optimization:

Many, many devices
End users are using as many as half a dozen devices in any one work week, running off a virtual environment, and often remotely. Workspace analytics can give visibility into all these moving parts, interrelating information to provide answers to problems in a timeframe and thoroughness that would be impossible without such a tool.

Hardware liberation
Moving the user application experience to a virtualized environment enables choice and the freedom to use different devices; however, with this choice comes an expectation that users will have the application performance necessary to do their job. End users like the option of varied devices if they work correctly and can deliver applications without headaches. When the virtual delivery fails, the reaction is to say "give me my PC. I have no time for devices that don't perform!" Here workspace analytics is the preventive measure to head off user frustration.

Security and performance are one
With all this endpoint device flexibility comes the increased threats of rogue applications, users going off the corporate networks while processing sensitive data, or devices that are not equipped with adequate security controls. Workspace analytics can provide insight into what's going on in the entire stack and can flag possible security issues. An exploit will impact performance; these tools are therefore valuable in risk containment.

Staff savings
Centralized endpoint management is critical to optimized performance when tens of thousands of endpoints may be in play. Also essential is managing IT staff time relating to endpoint troubleshooting issues. Analytics tools save IT time by providing detailed analysis of a single machine or user, and can help route trouble calls, including escalation where needed.

With Analytics comes Optimization

While improving their competitive edge is an often-talked about motivation for digital transformation, it's encouraging to see businesses are realizing more often that a digital business must make optimization – performance – a priority as well. If "charity begins at home" then one can say optimization begins at each and every endpoint "home" where the end user's productivity relies on IT's clear understanding of how the endpoint is performing and the ability to remedy issues in a timely fashion.

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.

Improving Endpoint Performance with Workspace Analytics

Jeff Kalberg

Digital transformation continues its reign as the popular business topic of the day, competing with security for the amount of attention and anxious debate it provokes. A recent Gartner survey notes that 62% of respondents said they had a management initiative or transformation program in place to become a more digital business. A telling note is that 46% of those respondents said the objective of their digital initiative is optimization, which means a large number of all organizations are more focused on delivering a high-fidelity, end-user computing experience while minimizing costs, and on how they can monitor the metrics that impact the delivery of the end-user workspace experience.

Optimization means improving the performance of your human and technology resources while keeping a watchful eye. To accomplish this, we must have clear, crisp visibility into the metrics relevant to the delivery of workspace applications to your end users and to the devices – the endpoints – they use to be productive. This ability to monitor metrics has become even more important as enterprises evolve from traditional to virtualized desktop environments, and the quantity and variety of devices used by an organization's employees continues to grow.

Workspace Analytics and Application Performance Management

To drive digital transformation – and optimization – one of the most important workspace analytics tools is application performance management which allows an organization to objectively monitor the user experience. By providing visibility into all of the components involved in remoting an application, these workspace analytics tools help organizations diagnose and proactively respond to user experience and performance issues before they become problems.

User experience metrics are a fundamental part of workspace analytics, but often the data collection process does not include the endpoint. Only when we link the client-side systems to the workspace analytics data collectors does an organization have a full understanding of application performance within virtualized environments. This richer detail gives IT the information it needs to improve endpoint performance and drive digital transformation.

Focus on Endpoint Performance

Optimization is a noble goal, one that nearly half of the Gartner respondents are treating as a priority. Understanding why the endpoint is such a focus promotes success in achieving optimization:

Many, many devices
End users are using as many as half a dozen devices in any one work week, running off a virtual environment, and often remotely. Workspace analytics can give visibility into all these moving parts, interrelating information to provide answers to problems in a timeframe and thoroughness that would be impossible without such a tool.

Hardware liberation
Moving the user application experience to a virtualized environment enables choice and the freedom to use different devices; however, with this choice comes an expectation that users will have the application performance necessary to do their job. End users like the option of varied devices if they work correctly and can deliver applications without headaches. When the virtual delivery fails, the reaction is to say "give me my PC. I have no time for devices that don't perform!" Here workspace analytics is the preventive measure to head off user frustration.

Security and performance are one
With all this endpoint device flexibility comes the increased threats of rogue applications, users going off the corporate networks while processing sensitive data, or devices that are not equipped with adequate security controls. Workspace analytics can provide insight into what's going on in the entire stack and can flag possible security issues. An exploit will impact performance; these tools are therefore valuable in risk containment.

Staff savings
Centralized endpoint management is critical to optimized performance when tens of thousands of endpoints may be in play. Also essential is managing IT staff time relating to endpoint troubleshooting issues. Analytics tools save IT time by providing detailed analysis of a single machine or user, and can help route trouble calls, including escalation where needed.

With Analytics comes Optimization

While improving their competitive edge is an often-talked about motivation for digital transformation, it's encouraging to see businesses are realizing more often that a digital business must make optimization – performance – a priority as well. If "charity begins at home" then one can say optimization begins at each and every endpoint "home" where the end user's productivity relies on IT's clear understanding of how the endpoint is performing and the ability to remedy issues in a timely fashion.

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.