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Why IT Teams Need Digital Experience Quality Analytics in Their Arsenal

Dave Page

Today's IT managers and engineers have an incredible arsenal of powerful tactical tools; APM, NPM, BSM, EUEM and the list goes on. Each tool does a very focused job monitoring the health and quality of a specific part of the digital supply chain. No IT organization should be without them.

The strength of these tools, their narrow, bottom-up focus, is also the cause of a real problem for businesses. These narrow tools miss issues that stem from the hand-off from one node or application to the next. The monitoring tools can't see the data falling into the gaps.

Another issue is that none of these monitoring tools can show the IT manager or engineer the consistency or quality of digital experience that is delivered to the customer or employee who sits at the end of the digital supply chain.

So even with the best performance monitoring tools, it is possible to see nothing but green lights and still have a digital product or service that simply isn't working. This experience is one that anyone responsible for IT should recognize.

With digital transformation, more parts of the digital supply chain are owned or managed by external suppliers and 3rd parties. You cannot afford to ignore the impact they can have on the end user of your digital products and services. Yet traditional monitoring tools can't be instrumented on them.

What's the solution? The answer has to lie in looking at the digital supply chain from the outside in; from the end user's perspective. You need to understand not how each element is performing but how it's performance impacts on the quality of the user's experience. You also need to see how each application or piece of the network is interacting with the next.

The end product of this outside-in view is a digital experience quality metric or score. This means more than a Single Pane of Glass (SPG) solution. After all, just having the green lights closer together won't change the fact that end product isn't working.

So how would digital experience quality analytics improve digital performance?

When you can see through the whole digital supply chain and understand the impact each part of the chain has on digital experience quality, you can also pinpoint the cause of poor experience. That is when your narrow tools such as APM and NPM come into play. When combined with an outside-in analytic, these tools become more powerful not less.

Because an outside-in digital experience analytic depends on seeing down through the whole digital supply chain, it will see 3rd party elements as well as those that are owned. This allows the IT team to manage the quality of those external suppliers in a way that is not possible with traditional performance monitoring tools.

Now clearly, digital experience quality analytics would improve the management of IT digital infrastructure and applications. The IT team will be able to better manage owned and external parts of the digital supply chain. But beyond that, digital experience quality analytics can give the IT team a metric or a score that shows how well they are delivering business critical digital services, not just whether they are up or down.

Dave Page is CEO of Actual Experience.

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 ...

Why IT Teams Need Digital Experience Quality Analytics in Their Arsenal

Dave Page

Today's IT managers and engineers have an incredible arsenal of powerful tactical tools; APM, NPM, BSM, EUEM and the list goes on. Each tool does a very focused job monitoring the health and quality of a specific part of the digital supply chain. No IT organization should be without them.

The strength of these tools, their narrow, bottom-up focus, is also the cause of a real problem for businesses. These narrow tools miss issues that stem from the hand-off from one node or application to the next. The monitoring tools can't see the data falling into the gaps.

Another issue is that none of these monitoring tools can show the IT manager or engineer the consistency or quality of digital experience that is delivered to the customer or employee who sits at the end of the digital supply chain.

So even with the best performance monitoring tools, it is possible to see nothing but green lights and still have a digital product or service that simply isn't working. This experience is one that anyone responsible for IT should recognize.

With digital transformation, more parts of the digital supply chain are owned or managed by external suppliers and 3rd parties. You cannot afford to ignore the impact they can have on the end user of your digital products and services. Yet traditional monitoring tools can't be instrumented on them.

What's the solution? The answer has to lie in looking at the digital supply chain from the outside in; from the end user's perspective. You need to understand not how each element is performing but how it's performance impacts on the quality of the user's experience. You also need to see how each application or piece of the network is interacting with the next.

The end product of this outside-in view is a digital experience quality metric or score. This means more than a Single Pane of Glass (SPG) solution. After all, just having the green lights closer together won't change the fact that end product isn't working.

So how would digital experience quality analytics improve digital performance?

When you can see through the whole digital supply chain and understand the impact each part of the chain has on digital experience quality, you can also pinpoint the cause of poor experience. That is when your narrow tools such as APM and NPM come into play. When combined with an outside-in analytic, these tools become more powerful not less.

Because an outside-in digital experience analytic depends on seeing down through the whole digital supply chain, it will see 3rd party elements as well as those that are owned. This allows the IT team to manage the quality of those external suppliers in a way that is not possible with traditional performance monitoring tools.

Now clearly, digital experience quality analytics would improve the management of IT digital infrastructure and applications. The IT team will be able to better manage owned and external parts of the digital supply chain. But beyond that, digital experience quality analytics can give the IT team a metric or a score that shows how well they are delivering business critical digital services, not just whether they are up or down.

Dave Page is CEO of Actual Experience.

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 ...