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APM as a Cornerstone of Major IT Projects

Steve Tack

Ask an IT leader what projects are currently top-of-list and you're apt to hear one or even all of the following: cloud computing, virtualization and data center consolidation. All three efforts have a common goal – reduce the cost of compute capacity while delivering needed agility and flexibility to support shifting priorities. Regardless of the reason for the change, IT teams expect to reap many benefits when adopting the latest in computing technology. These include efficiency; higher performance and scalability to support more demanding enterprise workloads and larger groups of users; and relative speed of implementation.

But in this never-ending mission to do more with less, IT teams often lose sight of the need for careful, end-user-focused application performance management (APM). After all, the delivery of fast, reliable, high-quality applications to end users must be the ultimate measure of success for any of these projects. If these projects aren't managed with explicit user-experience objectives, IT can introduce risks that can reduce or even eliminate the potential business benefits.

The reality is that initiatives like cloud computing and virtualization can wreak havoc on the end-user experience. For instance, many third-party cloud services are opaque, meaning that businesses using public cloud services often have little insight into the overall health of the computing infrastructure, and little say in their cloud service providers’ capacity management decisions. As a result, if a particular cloud customer’s neighbor in the cloud experiences a spike in traffic, chances are the speed and availability of the cloud customer’s own business-critical, cloud-based services and applications may suffer.

The big guys in the industry have really dug deep and proved that milliseconds matter. Google found that an extra 500 milliseconds in loading time resulted in a 20 percent drop in traffic. That's just half a second to lose 1/5th of your visitors! The cost of poor performance is huge, both in terms of lost revenues and employee productivity.

Even a slight reduction in application performance can result in hundreds of thousands of dollars in lost productivity each quarter. If you move a critical application to the cloud, you must understand how end users on the other side of the cloud – whether they're employees or customers – are experiencing the application, otherwise you risk losing your expected cost-savings. APM centered on the end-user experience must therefore be central to your cloud strategy.

Private cloud and virtualized infrastructure projects also pose a serious threat to the end-user experience. In many virtualization initiatives, IT tends to treat infrastructure utilization metrics as the ultimate end-game. But, the relevant metric shouldn't be how many virtual machines have been created and to what extent are infrastructure resources being maximized. Rather, the relevant metric needs to be at what point of utilization does the end-user experience begin to degrade, such that that point should not be surpassed? Will our applications perform as well or better after virtualization? Only a commitment to APM based on the end-user experience can answer this question.

Finally, an understanding of the end-user experience helps guide smarter decisions as major projects are being carried out. Case in point are data center consolidation and other infrastructure change projects. A first critical step must be to baseline the current performance of applications and transactions. Then, IT needs to test all applications with the new configuration, as well as map all applications, storage and network changes, making data center consolidation projects very time-consuming and complex.

Once the new data center changes are in place, IT needs to continually measure the applications to ensure they perform as expected, with no impact to end users. The common antidotes to all these various phases of a consolidation project are tools that enable effective management of application infrastructure through an understanding of the end-user experience, as well as the ability to trace transaction flows across the environment. If the end-user perspective is factored in properly throughout the entire project, IT teams can better avoid re-work and re-dos that can lengthen costly “overlap” periods between new and existing infrastructure, and keep overall project costs and timeframes down.

Understanding the end-user experience is a cornerstone to helping IT teams deal with the adoption of new technologies and the management of complex projects. Today, successful IT teams are adopting a new generation of APM that is driven from the end-user experience. It's all about the end user and the view of their world of the application – not just about infrastructure – and it's built into the entire application lifecycle, from development, to testing, to production. This approach allows IT teams to deploy applications faster and resolve problems more quickly and have more confidence in application performance once in production.

This new generation of APM is achieved through a unified, automated solution that offers comprehensive coverage of the entire application delivery chain and traces every transaction in production from the end-user click all the way to the database and back at code-level depth.

Steve Tack is CTO of Compuware’s Application Performance Management Business Unit.

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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 ...

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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 ...

APM as a Cornerstone of Major IT Projects

Steve Tack

Ask an IT leader what projects are currently top-of-list and you're apt to hear one or even all of the following: cloud computing, virtualization and data center consolidation. All three efforts have a common goal – reduce the cost of compute capacity while delivering needed agility and flexibility to support shifting priorities. Regardless of the reason for the change, IT teams expect to reap many benefits when adopting the latest in computing technology. These include efficiency; higher performance and scalability to support more demanding enterprise workloads and larger groups of users; and relative speed of implementation.

But in this never-ending mission to do more with less, IT teams often lose sight of the need for careful, end-user-focused application performance management (APM). After all, the delivery of fast, reliable, high-quality applications to end users must be the ultimate measure of success for any of these projects. If these projects aren't managed with explicit user-experience objectives, IT can introduce risks that can reduce or even eliminate the potential business benefits.

The reality is that initiatives like cloud computing and virtualization can wreak havoc on the end-user experience. For instance, many third-party cloud services are opaque, meaning that businesses using public cloud services often have little insight into the overall health of the computing infrastructure, and little say in their cloud service providers’ capacity management decisions. As a result, if a particular cloud customer’s neighbor in the cloud experiences a spike in traffic, chances are the speed and availability of the cloud customer’s own business-critical, cloud-based services and applications may suffer.

The big guys in the industry have really dug deep and proved that milliseconds matter. Google found that an extra 500 milliseconds in loading time resulted in a 20 percent drop in traffic. That's just half a second to lose 1/5th of your visitors! The cost of poor performance is huge, both in terms of lost revenues and employee productivity.

Even a slight reduction in application performance can result in hundreds of thousands of dollars in lost productivity each quarter. If you move a critical application to the cloud, you must understand how end users on the other side of the cloud – whether they're employees or customers – are experiencing the application, otherwise you risk losing your expected cost-savings. APM centered on the end-user experience must therefore be central to your cloud strategy.

Private cloud and virtualized infrastructure projects also pose a serious threat to the end-user experience. In many virtualization initiatives, IT tends to treat infrastructure utilization metrics as the ultimate end-game. But, the relevant metric shouldn't be how many virtual machines have been created and to what extent are infrastructure resources being maximized. Rather, the relevant metric needs to be at what point of utilization does the end-user experience begin to degrade, such that that point should not be surpassed? Will our applications perform as well or better after virtualization? Only a commitment to APM based on the end-user experience can answer this question.

Finally, an understanding of the end-user experience helps guide smarter decisions as major projects are being carried out. Case in point are data center consolidation and other infrastructure change projects. A first critical step must be to baseline the current performance of applications and transactions. Then, IT needs to test all applications with the new configuration, as well as map all applications, storage and network changes, making data center consolidation projects very time-consuming and complex.

Once the new data center changes are in place, IT needs to continually measure the applications to ensure they perform as expected, with no impact to end users. The common antidotes to all these various phases of a consolidation project are tools that enable effective management of application infrastructure through an understanding of the end-user experience, as well as the ability to trace transaction flows across the environment. If the end-user perspective is factored in properly throughout the entire project, IT teams can better avoid re-work and re-dos that can lengthen costly “overlap” periods between new and existing infrastructure, and keep overall project costs and timeframes down.

Understanding the end-user experience is a cornerstone to helping IT teams deal with the adoption of new technologies and the management of complex projects. Today, successful IT teams are adopting a new generation of APM that is driven from the end-user experience. It's all about the end user and the view of their world of the application – not just about infrastructure – and it's built into the entire application lifecycle, from development, to testing, to production. This approach allows IT teams to deploy applications faster and resolve problems more quickly and have more confidence in application performance once in production.

This new generation of APM is achieved through a unified, automated solution that offers comprehensive coverage of the entire application delivery chain and traces every transaction in production from the end-user click all the way to the database and back at code-level depth.

Steve Tack is CTO of Compuware’s Application Performance Management Business Unit.

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 ...