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End User Monitoring - Reports of EUM's Death Have Been Greatly Exaggerated

Larry Haig

Once upon a time (as they say) client side performance was a relatively straightforward matter. The principles were known (or at least available – thank you, Steve Souders et al), and the parameters surrounding delivery, whilst generally limited in modern terms (IE5 /Netscape, dialup connectivity anyone?) were at least reasonably predictable.

This didn't mean that enough people addressed client side performance (then or now for that matter), despite the alleged 80% of delivery time spent on the user machine, and the undoubted association between application performance and business outcomes.

From a monitoring and analysis point of view, synthetic external testing (or end user monitoring) did the job. Much has been written (not least by myself) on the need to apply best practice, and to select your tooling appropriately. The advent of “real user monitoring” (RUM) came some 10 years ago – a move at first decried, then rapidly embraced, by most of the “standalone” external test Vendors. The undoubted advantages of real user monitoring in terms of breadth of coverage and granular visibility to multiple user end points – geography, O/S, device, browser – tended for a time to mask the different, though complementary strengths of consistent, repeated performance monitoring at page or individual (eg 3rd party) object level.

Fast forward to today, though, and the situation demands a variety of approaches to cope with the extreme diverseness of delivery conditions. The rise and rise of mobile (just as one example, major UK retailer JohnLewis.com quoted over 60% of digital orders derived from mobile devices during 2015/16 peak trading) brings many challenges to Front-End Optimization (FEO) practice. These include: diversity of device types and version; browsers; and limiting connectivity conditions.

This situation is compounded by development of the applications themselves. As far as the web is concerned, monitoring challenges are introduced by, amongst other things: Single Page Applications (either full or partial); “server push content”; and mobile “WebApps” driven by service worker interactions. Mobile Applications, whether native or hybrid, present their own analysis challenges, which I will address subsequently also.

This already rich mix is further complicated by business demands for more on-site content – multimedia and other rich content, exotic fonts, and more. Increasingly large amounts of client side logic, whether as part of SPAs or otherwise, demand focused attention to avoid unacceptable performance in edge case conditions.

As if this wasn't enough, the (final!) emergence of HTTP/2 introduces both advantages and anti-patterns relative to former best practice.

The primitive simplicity of page onload navigation timing endpoints has moved from beyond irrelevance to becoming positively misleading, regardless of the type of tool used.

So, these changes require an increased subtlety of approach, combined with a range of tools to ensure that FEO recommendations are both relevant and effective.

I will provide some thoughts in subsequent blogs as to effective FEO approaches to derive maximum business benefit in each of these cases.

The bottom line is, however, that FEO is more important than ever in ensuring optimal business outcomes from digital channels.

Larry Haig is Senior Consultant at Intechnica.

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End User Monitoring - Reports of EUM's Death Have Been Greatly Exaggerated

Larry Haig

Once upon a time (as they say) client side performance was a relatively straightforward matter. The principles were known (or at least available – thank you, Steve Souders et al), and the parameters surrounding delivery, whilst generally limited in modern terms (IE5 /Netscape, dialup connectivity anyone?) were at least reasonably predictable.

This didn't mean that enough people addressed client side performance (then or now for that matter), despite the alleged 80% of delivery time spent on the user machine, and the undoubted association between application performance and business outcomes.

From a monitoring and analysis point of view, synthetic external testing (or end user monitoring) did the job. Much has been written (not least by myself) on the need to apply best practice, and to select your tooling appropriately. The advent of “real user monitoring” (RUM) came some 10 years ago – a move at first decried, then rapidly embraced, by most of the “standalone” external test Vendors. The undoubted advantages of real user monitoring in terms of breadth of coverage and granular visibility to multiple user end points – geography, O/S, device, browser – tended for a time to mask the different, though complementary strengths of consistent, repeated performance monitoring at page or individual (eg 3rd party) object level.

Fast forward to today, though, and the situation demands a variety of approaches to cope with the extreme diverseness of delivery conditions. The rise and rise of mobile (just as one example, major UK retailer JohnLewis.com quoted over 60% of digital orders derived from mobile devices during 2015/16 peak trading) brings many challenges to Front-End Optimization (FEO) practice. These include: diversity of device types and version; browsers; and limiting connectivity conditions.

This situation is compounded by development of the applications themselves. As far as the web is concerned, monitoring challenges are introduced by, amongst other things: Single Page Applications (either full or partial); “server push content”; and mobile “WebApps” driven by service worker interactions. Mobile Applications, whether native or hybrid, present their own analysis challenges, which I will address subsequently also.

This already rich mix is further complicated by business demands for more on-site content – multimedia and other rich content, exotic fonts, and more. Increasingly large amounts of client side logic, whether as part of SPAs or otherwise, demand focused attention to avoid unacceptable performance in edge case conditions.

As if this wasn't enough, the (final!) emergence of HTTP/2 introduces both advantages and anti-patterns relative to former best practice.

The primitive simplicity of page onload navigation timing endpoints has moved from beyond irrelevance to becoming positively misleading, regardless of the type of tool used.

So, these changes require an increased subtlety of approach, combined with a range of tools to ensure that FEO recommendations are both relevant and effective.

I will provide some thoughts in subsequent blogs as to effective FEO approaches to derive maximum business benefit in each of these cases.

The bottom line is, however, that FEO is more important than ever in ensuring optimal business outcomes from digital channels.

Larry Haig is Senior Consultant at Intechnica.

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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 ...