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Reports of APM's Death Have Been Greatly Exaggerated

Recently, Art Wittmann at InformationWeek claimed that the APM industry is dying. He wrote, “App performance management is seen as less important than it was two years ago, partly because vendors haven’t kept up.” And he was armed with ample data to support his view.

Looking at survey results from hundreds of APM customers, InformationWeek’s data suggests that the high cost and lengthy implementation process of APM is a driving factor in the fall of the industry: insufficient expertise to use the product (50%), high cost (41%), and taking too much staff time to do it right (32%). Interestingly, while the dissatisfaction with APM has increased, the rate of daily outages continues to rise, from 8% in 2010 to 10% today.

The question I pose is this – is there something else to be interpreted from this data? I would argue it is not APM as a whole that is dying but rather legacy APM solutions. The increase in daily outages suggests that APM is more important than ever before but that the industry itself isn't keeping up.

Legacy APM systems have several well-documented problems that have lead to user dissatisfaction for years. These products, which require configuration at each component for correct monitoring, come with high costs and long implementation cycles.

For APM to succeed, the industry must focus on deployment efficiency: actual install effort, supporting infrastructure effort including sufficient, scalable server space; initial configuration effort; and maintenance configuration effort. Initial configuration effort must be improved and rules and self-learning should reduce or eliminate maintenance configuration effort.

If these problems disappear, APM tools are much more attractive again. The survey respondents’ complaints about insufficient expertise (50%) and too much time (32%) are effectively mitigated by auto detection and self-learning.

Wittmann also believes that APM tools have failed to keep up with complexity – and that it is too difficult to set up APM tools in a service-oriented design. Again, the common theme here is ease of use. For APM to be truly helpful, the data has to be managed and presented in a way that can be used both without training for novices, and minimal training for expert users (more advanced functions).

APM is not just for developers anymore – and the industry has to adjust accordingly. IT operations, app owners and infrastructure folks need to have understandable and actionable data. In a sense, Wittmann is correct: if you rely on data from siloed monitoring tools (developer specific, web server specific, CPU monitoring, etc.), you won't gather meaningful information.

But he is too broad in his assessment. A transaction-centric approach to APM gives organizations a big-picture view of the interaction between end users, applications, and infrastructure. This view can pinpoint the source of problems quickly because you trace 100% of user transactions.

Wittmann is not wrong that legacy APM tools struggle with the growing complexity in IT, especially in the cloud. But there is reason to be optimistic about the demonstrated potential APM has for contributing to the overall success of complex IT operations. Mission-critical application deployments, and therefore the overall success of a company deploying these apps, depend on it.

ABOUT Tom Batchelor

Tom Batchelor is the Senior Solutions Architect at Correlsense and is responsible for creating innovative solutions geared specifically to the needs of clients. Prior to joining Correlsense, he worked in various pre-sales roles for OpTier and Symantec.

Related Links:

www.correlsense.com

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Reports of APM's Death Have Been Greatly Exaggerated

Recently, Art Wittmann at InformationWeek claimed that the APM industry is dying. He wrote, “App performance management is seen as less important than it was two years ago, partly because vendors haven’t kept up.” And he was armed with ample data to support his view.

Looking at survey results from hundreds of APM customers, InformationWeek’s data suggests that the high cost and lengthy implementation process of APM is a driving factor in the fall of the industry: insufficient expertise to use the product (50%), high cost (41%), and taking too much staff time to do it right (32%). Interestingly, while the dissatisfaction with APM has increased, the rate of daily outages continues to rise, from 8% in 2010 to 10% today.

The question I pose is this – is there something else to be interpreted from this data? I would argue it is not APM as a whole that is dying but rather legacy APM solutions. The increase in daily outages suggests that APM is more important than ever before but that the industry itself isn't keeping up.

Legacy APM systems have several well-documented problems that have lead to user dissatisfaction for years. These products, which require configuration at each component for correct monitoring, come with high costs and long implementation cycles.

For APM to succeed, the industry must focus on deployment efficiency: actual install effort, supporting infrastructure effort including sufficient, scalable server space; initial configuration effort; and maintenance configuration effort. Initial configuration effort must be improved and rules and self-learning should reduce or eliminate maintenance configuration effort.

If these problems disappear, APM tools are much more attractive again. The survey respondents’ complaints about insufficient expertise (50%) and too much time (32%) are effectively mitigated by auto detection and self-learning.

Wittmann also believes that APM tools have failed to keep up with complexity – and that it is too difficult to set up APM tools in a service-oriented design. Again, the common theme here is ease of use. For APM to be truly helpful, the data has to be managed and presented in a way that can be used both without training for novices, and minimal training for expert users (more advanced functions).

APM is not just for developers anymore – and the industry has to adjust accordingly. IT operations, app owners and infrastructure folks need to have understandable and actionable data. In a sense, Wittmann is correct: if you rely on data from siloed monitoring tools (developer specific, web server specific, CPU monitoring, etc.), you won't gather meaningful information.

But he is too broad in his assessment. A transaction-centric approach to APM gives organizations a big-picture view of the interaction between end users, applications, and infrastructure. This view can pinpoint the source of problems quickly because you trace 100% of user transactions.

Wittmann is not wrong that legacy APM tools struggle with the growing complexity in IT, especially in the cloud. But there is reason to be optimistic about the demonstrated potential APM has for contributing to the overall success of complex IT operations. Mission-critical application deployments, and therefore the overall success of a company deploying these apps, depend on it.

ABOUT Tom Batchelor

Tom Batchelor is the Senior Solutions Architect at Correlsense and is responsible for creating innovative solutions geared specifically to the needs of clients. Prior to joining Correlsense, he worked in various pre-sales roles for OpTier and Symantec.

Related Links:

www.correlsense.com

Hot Topics

The Latest

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...

We just surveyed 300 frontend and mobile engineers across 16 countries, and the finding that keeps sticking with me isn't the one about AI. It's this: 74% of engineering teams rate themselves in the "middle" of the observability maturity scale. Not reactive, not strategic. Stuck in the middle. They have dashboards, they have tracing, they have alerts. And yet when something goes wrong, they still can't tell you why ...

In MEAN TIME TO INSIGHT Episode 25, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses  AI's impact on the Wide Area Network (WAN) ... 

Application performance monitoring (APM) dashboards are only as useful as what they are configured to measure. The default setup covers obvious failure modes such as downtime, error spikes, and latency breaches, but it does not cover everything. Some failures produce no alerts or anomalies. The dashboard stays green while users experience a broken product. Here are six signs that is happening ...

The race to deploy AI is largely over. Most enterprises have entered it. The question now is not whether artificial intelligence is running inside the organization. The question is whether anyone is genuinely responsible for what it does. That is not a technical question. It is a leadership one. And most organizations are not yet structured to answer it honestly ...

A new analysis of 250 real-world queries across common retail tasks, such as product pricing, availability, ratings, shipping and specifications, reveals systemic inefficiency at the heart of web-based AI agents. On average, 97.9% of the data retrieved by agents from live web pages is irrelevant to the query being answered. Specifically, the average page ingested ran nearly 9,000 characters, while the average answer was just 32 characters, resulting in a noise-to-signal ratio of 278:1. Price queries were the most extreme outlier, with noise rates approaching 99.5%. That's not a rounding error. That's a structural problem ...

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...