Skip to main content

Newsflash: User Experience Is Bigger Than Data

Denis Goodwin

In a recent post, Why Today's APM Solutions Aren't Optimized for DevOps, I discussed the odd contradiction I’ve been noticing lately in the APM marketplace. Fragmented approaches to APM are being promoted as solutions to support the DevOps ideal of continuous integration and delivery, but the stark lack of integrated tools in these APM arsenals isn’t likely to make communication and collaboration between dev and ops any easier or more efficient.

That’s why integrated, unified APM solutions — consisting of software tools and testing functions that can fluently speak to each other and look at the same information at the same time — are the only hope for APM in a streamlined DevOps world. Unfortunately, even the best attempts at tool integration won’t solve the deeper issues of performance management if they approach it completely backwards from the start.


The Varieties of Anti-User Experience

The problem is, most vendors in the APM arena are looking at what they do from the wrong way around. Starting from the volumes of data that their tools generate and record, they woo and immerse their customers in “analytics.” Eventually, somewhere down the line, they may accidentally stumble upon the issues that are actually impacting end users.

Lo and behold! There are humans on the other side of this matrix. And what kind of experience are those users of the application having? It’s hard to say, since we can only extrapolate from our data and try to imagine what the quality of the user experience might be. But wait a minute – How does that make any sense? Shouldn’t we be looking at application speed and response time from the perspective of the people to whom it ultimately matters? Whose idea was it, anyway, to privilege data analytics over what our end users actually experience and perceive?

Data: A Supporting Character in a Story Written By User Experience

These are obviously rhetorical questions, because there’s always been a better way to engage in APM, and it begins and ends with the end-user. If monitoring and optimizing performance to deliver a streamlined end-user experience is our goal, then it should be obvious that the right way to go about it is to start with our end-users’ experience and work our way back through the software architecture from there.

At the end of the day, no matter how many sources of performance lags you’ve caught and corrected, your efforts only make a difference if they improve the user experience of your software. Your work needs to become user-centric, both in theory and in practice, if customer experience has any connection to your business and revenue goals (which it almost always, most certainly does).

Of course, monitoring server responses, stressing your system baselines with regular load tests, and analyzing the resulting data is essential to being able to manage the quality and reliability of your applications, day in and day out. I’m not arguing otherwise. Big-data analytics and code-level visibility are important concepts in the APM space, and I believe them to be critical components of any full-featured, end-to-end solution.

But the fact remains that user experience is actually bigger and more inclusive than data, because without a clear emphasis on your end-user experience, your deep-data dives may lose their meaning. The fact is, sometimes performance problems can’t even be found at the level of code and internal datacenters, but rather in more obvious user experience issues, like a slow web page caused by 3rd party content. As the old adage has it, if you focus only on the trees, you may lose sight of the forest ... and end up getting lost.

But perhaps Steve said it best:

“You’ve got to start with the customer experience and work back toward the technology – not the other way around.”
-Steve Jobs

Denis Goodwin is Director of Product Management, APM, AlertSite UXM, SmartBear Software.

The Latest

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 ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

Newsflash: User Experience Is Bigger Than Data

Denis Goodwin

In a recent post, Why Today's APM Solutions Aren't Optimized for DevOps, I discussed the odd contradiction I’ve been noticing lately in the APM marketplace. Fragmented approaches to APM are being promoted as solutions to support the DevOps ideal of continuous integration and delivery, but the stark lack of integrated tools in these APM arsenals isn’t likely to make communication and collaboration between dev and ops any easier or more efficient.

That’s why integrated, unified APM solutions — consisting of software tools and testing functions that can fluently speak to each other and look at the same information at the same time — are the only hope for APM in a streamlined DevOps world. Unfortunately, even the best attempts at tool integration won’t solve the deeper issues of performance management if they approach it completely backwards from the start.


The Varieties of Anti-User Experience

The problem is, most vendors in the APM arena are looking at what they do from the wrong way around. Starting from the volumes of data that their tools generate and record, they woo and immerse their customers in “analytics.” Eventually, somewhere down the line, they may accidentally stumble upon the issues that are actually impacting end users.

Lo and behold! There are humans on the other side of this matrix. And what kind of experience are those users of the application having? It’s hard to say, since we can only extrapolate from our data and try to imagine what the quality of the user experience might be. But wait a minute – How does that make any sense? Shouldn’t we be looking at application speed and response time from the perspective of the people to whom it ultimately matters? Whose idea was it, anyway, to privilege data analytics over what our end users actually experience and perceive?

Data: A Supporting Character in a Story Written By User Experience

These are obviously rhetorical questions, because there’s always been a better way to engage in APM, and it begins and ends with the end-user. If monitoring and optimizing performance to deliver a streamlined end-user experience is our goal, then it should be obvious that the right way to go about it is to start with our end-users’ experience and work our way back through the software architecture from there.

At the end of the day, no matter how many sources of performance lags you’ve caught and corrected, your efforts only make a difference if they improve the user experience of your software. Your work needs to become user-centric, both in theory and in practice, if customer experience has any connection to your business and revenue goals (which it almost always, most certainly does).

Of course, monitoring server responses, stressing your system baselines with regular load tests, and analyzing the resulting data is essential to being able to manage the quality and reliability of your applications, day in and day out. I’m not arguing otherwise. Big-data analytics and code-level visibility are important concepts in the APM space, and I believe them to be critical components of any full-featured, end-to-end solution.

But the fact remains that user experience is actually bigger and more inclusive than data, because without a clear emphasis on your end-user experience, your deep-data dives may lose their meaning. The fact is, sometimes performance problems can’t even be found at the level of code and internal datacenters, but rather in more obvious user experience issues, like a slow web page caused by 3rd party content. As the old adage has it, if you focus only on the trees, you may lose sight of the forest ... and end up getting lost.

But perhaps Steve said it best:

“You’ve got to start with the customer experience and work back toward the technology – not the other way around.”
-Steve Jobs

Denis Goodwin is Director of Product Management, APM, AlertSite UXM, SmartBear Software.

The Latest

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 ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...