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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.

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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

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 ...