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The Power of the Pivot

A look into the benefits of combining user experience monitoring with application side analysis
Denis Goodwin

The ability to view things from the end user perspective and to drill down into the code level deep dive can be extremely powerful, and the information gathered from this ability provides DevOps teams with an instant view into the direct root cause of any user experience problem they may not otherwise have noticed.

Traditional real-user monitoring (RUM) techniques provide insight into how your user actually interacts with your website or application. Synthetic monitoring, particularly when using real browsers, provides a similar assessment of expected user experience along with the benefits of true availability monitoring, third-party impact, and consistent baselining capabilities.

Combining synthetic and RUM gives a complete view of the user experience along with high level root cause clues. RUM, by itself, can miss outages, page errors, and third-party problems. Synthetic, by itself, is really only a proxy for real-user experience and can miss problems experienced by various user populations. Using both techniques in tandem eliminates those inherent blind spots and can provide an organization with the best view of their users’ experience – both actual and potential.


But monitoring user experience only tells you half of the story. The ability to look at things from the application/back-end perspective and drill down to the code (or up to end-user transactions) is a powerful root cause identifier. By discovering problems in delivery, DevOps teams can work to prevent or minimize user impact on their software.

Application and server monitoring provide insight into relative transaction performance. Furthermore, it provides an accurate view into the root cause of user experience degradation in your own infrastructure. These tools allow developers to identify issues before code is deployed while simultaneously giving ops teams the tools to address issues and communicate to app owners in real time. Providing this flexible view of user experience and application health provides a clear view of impact and root cause, allowing dev and ops to work together prevent and minimize damaging negative user experiences. Having all of this working together at the same time will do wonders for your overall relationship with your end user.

The ability to pivot the perspective from user experience to application transaction performance can give your organization a powerful view into user experience and root cause diagnostics. Put another way, it helps to answer the “what” along with (possibly more importantly) the “why” when it comes to performance issues. When these perspectives are seamlessly tied together and are easily available to a variety of technical and business users, the result can only be APM awesomeness!

Denis Goodwin is Director of Product Management for APM at SmartBear.

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 ...

The Power of the Pivot

A look into the benefits of combining user experience monitoring with application side analysis
Denis Goodwin

The ability to view things from the end user perspective and to drill down into the code level deep dive can be extremely powerful, and the information gathered from this ability provides DevOps teams with an instant view into the direct root cause of any user experience problem they may not otherwise have noticed.

Traditional real-user monitoring (RUM) techniques provide insight into how your user actually interacts with your website or application. Synthetic monitoring, particularly when using real browsers, provides a similar assessment of expected user experience along with the benefits of true availability monitoring, third-party impact, and consistent baselining capabilities.

Combining synthetic and RUM gives a complete view of the user experience along with high level root cause clues. RUM, by itself, can miss outages, page errors, and third-party problems. Synthetic, by itself, is really only a proxy for real-user experience and can miss problems experienced by various user populations. Using both techniques in tandem eliminates those inherent blind spots and can provide an organization with the best view of their users’ experience – both actual and potential.


But monitoring user experience only tells you half of the story. The ability to look at things from the application/back-end perspective and drill down to the code (or up to end-user transactions) is a powerful root cause identifier. By discovering problems in delivery, DevOps teams can work to prevent or minimize user impact on their software.

Application and server monitoring provide insight into relative transaction performance. Furthermore, it provides an accurate view into the root cause of user experience degradation in your own infrastructure. These tools allow developers to identify issues before code is deployed while simultaneously giving ops teams the tools to address issues and communicate to app owners in real time. Providing this flexible view of user experience and application health provides a clear view of impact and root cause, allowing dev and ops to work together prevent and minimize damaging negative user experiences. Having all of this working together at the same time will do wonders for your overall relationship with your end user.

The ability to pivot the perspective from user experience to application transaction performance can give your organization a powerful view into user experience and root cause diagnostics. Put another way, it helps to answer the “what” along with (possibly more importantly) the “why” when it comes to performance issues. When these perspectives are seamlessly tied together and are easily available to a variety of technical and business users, the result can only be APM awesomeness!

Denis Goodwin is Director of Product Management for APM at SmartBear.

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 ...