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The Importance of Baselining for End-User Experience Management

Sri Chaganty

If your business depends on mission-critical web or legacy applications, then monitoring how your end users interact with your applications is critical. The end users' experience after pressing the ENTER key or clicking SUBMIT might decide the bottom line of your enterprise.

Most monitoring solutions try to infer the end-user experience based on resource utilization. However, resource utilization cannot provide meaningful results on how the end-user is experiencing an interaction with an application. The true measurement of end-user experience is availability and response time of the application, end-to-end and hop-by-hop.

The responsiveness of the application determines the end user's experience. In order to understand the end user's experience, contextual intelligence on how the application is responding based on the time of the day, the day of the week, the week of the month and the month of the year must be measured. Baselining requires capturing these metrics across a time dimension. The base line of response time of an application at regular intervals provides the ability to ensure that the application is working as designed. It is more than a single report detailing the health of the application at a certain point in time.

"Dynamic baselining" is a technique to compare real response times against historical averages. Dynamic baselining is an effective technique to provide meaningful insight into service anomalies without requiring the impossible task of setting absolute thresholds for every transaction.
A robust user experience solution will also include application and system errors that have a significant impact on the ability of the user to complete a task. Since the user experience is often impacted by the performance of the user's device, metrics about desktop/laptop performance are required for adequate root-cause analysis.

For example, when you collect response time within the Exchange environment over a period of time, with data reflecting periods of low, average, and peak usage, you can make a subjective determination of what is acceptable performance for your system. That determination is your baseline, which you can then use to detect bottlenecks and to watch for long-term changes in usage patterns that require Ops to balance infrastructure capacity against demand to achieve the intended performance.

When you need to troubleshoot system problems, the response time baseline gives you information about the behavior of system resources at the time the problem occurred, which is useful in discovering its cause. When determining your baseline, it is important to know the types of work that are being done and the days and times when that work is done. This provides the association of the work performed with the resource usage to determine whether performance during those intervals is acceptable.

Response time baselining helps you to understand not only resource utilization issues but also availability and responsiveness of services on which the application flow is dependent upon. For example, if your Active Directory is not responding in an optimal way, the end-user experiences unintended latencies with the application's performance.

By following the baseline process, you can obtain the following information:

■ What is the real experience of the user when using any application?

■ What is "normal" behavior?

■ Is "normal" meeting service levels that drive productivity?

■ Is "normal" optimal?

■ Are deterministic answers available? Time to close a ticket, Root cause for outage, Predictive warnings, etc.

■ Who is using what, when and how much?

■ What is the experience of each individual user and a group of users?

■ Dependencies on infrastructure

■ Real-time interaction with infrastructure

■ Gain valuable information on the health of the hardware and software that is part of the application service delivery chain

■ Determine resource utilization

■ Make accurate decisions about alarm thresholds

Response time baselining empowers you to provide guaranteed service levels to your end users for every business critical application which in turns helps the bottom-line of the business.

Sri Chaganty is COO and CTO/Founder at AppEnsure.

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The Importance of Baselining for End-User Experience Management

Sri Chaganty

If your business depends on mission-critical web or legacy applications, then monitoring how your end users interact with your applications is critical. The end users' experience after pressing the ENTER key or clicking SUBMIT might decide the bottom line of your enterprise.

Most monitoring solutions try to infer the end-user experience based on resource utilization. However, resource utilization cannot provide meaningful results on how the end-user is experiencing an interaction with an application. The true measurement of end-user experience is availability and response time of the application, end-to-end and hop-by-hop.

The responsiveness of the application determines the end user's experience. In order to understand the end user's experience, contextual intelligence on how the application is responding based on the time of the day, the day of the week, the week of the month and the month of the year must be measured. Baselining requires capturing these metrics across a time dimension. The base line of response time of an application at regular intervals provides the ability to ensure that the application is working as designed. It is more than a single report detailing the health of the application at a certain point in time.

"Dynamic baselining" is a technique to compare real response times against historical averages. Dynamic baselining is an effective technique to provide meaningful insight into service anomalies without requiring the impossible task of setting absolute thresholds for every transaction.
A robust user experience solution will also include application and system errors that have a significant impact on the ability of the user to complete a task. Since the user experience is often impacted by the performance of the user's device, metrics about desktop/laptop performance are required for adequate root-cause analysis.

For example, when you collect response time within the Exchange environment over a period of time, with data reflecting periods of low, average, and peak usage, you can make a subjective determination of what is acceptable performance for your system. That determination is your baseline, which you can then use to detect bottlenecks and to watch for long-term changes in usage patterns that require Ops to balance infrastructure capacity against demand to achieve the intended performance.

When you need to troubleshoot system problems, the response time baseline gives you information about the behavior of system resources at the time the problem occurred, which is useful in discovering its cause. When determining your baseline, it is important to know the types of work that are being done and the days and times when that work is done. This provides the association of the work performed with the resource usage to determine whether performance during those intervals is acceptable.

Response time baselining helps you to understand not only resource utilization issues but also availability and responsiveness of services on which the application flow is dependent upon. For example, if your Active Directory is not responding in an optimal way, the end-user experiences unintended latencies with the application's performance.

By following the baseline process, you can obtain the following information:

■ What is the real experience of the user when using any application?

■ What is "normal" behavior?

■ Is "normal" meeting service levels that drive productivity?

■ Is "normal" optimal?

■ Are deterministic answers available? Time to close a ticket, Root cause for outage, Predictive warnings, etc.

■ Who is using what, when and how much?

■ What is the experience of each individual user and a group of users?

■ Dependencies on infrastructure

■ Real-time interaction with infrastructure

■ Gain valuable information on the health of the hardware and software that is part of the application service delivery chain

■ Determine resource utilization

■ Make accurate decisions about alarm thresholds

Response time baselining empowers you to provide guaranteed service levels to your end users for every business critical application which in turns helps the bottom-line of the business.

Sri Chaganty is COO and CTO/Founder at AppEnsure.

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 ...