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Finding Your Organization’s Blind Spots

Steve Tack

How a Lifecycle Approach to APM Improves User Experience, Business Transaction Performance and Ultimately Revenues

Imagine it's Black Friday, when all of the sudden … boom! Your most critical Web application goes awry, bringing your e-commerce operation to a screeching halt on the very day flawless performance is needed most.

According to industry research cited in a Quocirca report titled “2012: The Year of Application Performance Management (APM)” a two second slowdown in response time is estimated to equal about four percent of revenue loss per visitor to an e-commerce site. So your organization probably doesn't have a lot of time to scramble to find out what is going wrong. You need to find answers – actionable information – and you need it fast.

The challenge to expeditious resolution is cutting through the vast amount of complexity that organizations are faced with today. First, there's the complexity involved in modern application delivery which is growing immensely. With applications increasingly pulling content from third-party services, and the advent of external infrastructures like the cloud, there are so many blind spots in the application delivery chain. Pinpointing the precise cause of application performance degradations can be tremendously difficult.

Second, there's the complexity that exists within organizations themselves, with so many stakeholders in application performance including developers, testers (QA), operations and line of business executives. Often, these groups are all speaking different business “languages” and concerned with different business goals.

For instance, developers speak in terms of debugging application code and don't want anything to impede their progress as they swiftly push out new products and enhancements. Operations, who wants to keep things as reliable and stable as possible, is quick to say “I told you so” whenever a problem arises. For their part, line of business speaks in numbers and they just want to know how conversions and revenues are being impacted – and when the bloodletting will stop!

This is a big mess which often results in a lot of wasted time, lack of meaningful communication and finger-pointing. One thing's for sure, “speed matters” – both in terms of actual application speed, as well as the speed at which performance problems are found, prioritized, addressed and fixed. Organizations desperately need a way to simplify all this complexity.

By now, you've more than likely heard the term DevOps, which entails bringing together developers and operations – both the pre-production and the production sides of the house. The goal is to aid in the release process, satisfying developers' requirements for speed while addressing operations' needs for stability. A key cornerstone of the DevOps movement is to commit to ongoing performance management and testing at all stages of an application lifecycle, from the earliest stages of development, to prevent problems from reaching production.

We at Compuware like to call this a lifecycle approach to performance management, since this term extends to include line of business executives. In order to successfully achieve a lifecycle approach, the proper type of APM system must be in place. First and foremost, this system must understand, from the user perspective, how applications and websites are performing. Achieving this level of insight requires monitoring of applications to go beyond the data center and start with the user perspective.

In addition, the proper type of APM system must:

- Deliver an integrated environment across stakeholders, to eliminate time spent correlating between different tools and to enable a common language of understanding. What applications are performing poorly? Why and whose problem is it? What's the impact on conversions?

- Provide automated, continuous visibility into what's going on – user behaviors, business transactions, conversion rates, etc.

- Offer actionable information for various stakeholders.

- Be application-centric as opposed to component-centric, which doesn't offer an overall view into how vital applications are performing. Additionally, be business aware, connecting IT to line of business by showing, for example, how IT is impacting the end- user experience and revenue.

In summary, it's important to remember that even the supposedly best application is never complete. Gartner estimates that only eight percent of an application's total cost of ownership (TCO) can be attributed to building, while the other 92 percent goes to ongoing enhancements, fixes and optimizationsi. A lifecycle approach to performance management, based on the right kind of APM solution, can help organizations navigate the vast complexity, ensure the strongest performing applications and make the best decisions that drive the user experience and revenues.

Steve Tack is VP of Product Management, Compuware APM.

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 ...

Finding Your Organization’s Blind Spots

Steve Tack

How a Lifecycle Approach to APM Improves User Experience, Business Transaction Performance and Ultimately Revenues

Imagine it's Black Friday, when all of the sudden … boom! Your most critical Web application goes awry, bringing your e-commerce operation to a screeching halt on the very day flawless performance is needed most.

According to industry research cited in a Quocirca report titled “2012: The Year of Application Performance Management (APM)” a two second slowdown in response time is estimated to equal about four percent of revenue loss per visitor to an e-commerce site. So your organization probably doesn't have a lot of time to scramble to find out what is going wrong. You need to find answers – actionable information – and you need it fast.

The challenge to expeditious resolution is cutting through the vast amount of complexity that organizations are faced with today. First, there's the complexity involved in modern application delivery which is growing immensely. With applications increasingly pulling content from third-party services, and the advent of external infrastructures like the cloud, there are so many blind spots in the application delivery chain. Pinpointing the precise cause of application performance degradations can be tremendously difficult.

Second, there's the complexity that exists within organizations themselves, with so many stakeholders in application performance including developers, testers (QA), operations and line of business executives. Often, these groups are all speaking different business “languages” and concerned with different business goals.

For instance, developers speak in terms of debugging application code and don't want anything to impede their progress as they swiftly push out new products and enhancements. Operations, who wants to keep things as reliable and stable as possible, is quick to say “I told you so” whenever a problem arises. For their part, line of business speaks in numbers and they just want to know how conversions and revenues are being impacted – and when the bloodletting will stop!

This is a big mess which often results in a lot of wasted time, lack of meaningful communication and finger-pointing. One thing's for sure, “speed matters” – both in terms of actual application speed, as well as the speed at which performance problems are found, prioritized, addressed and fixed. Organizations desperately need a way to simplify all this complexity.

By now, you've more than likely heard the term DevOps, which entails bringing together developers and operations – both the pre-production and the production sides of the house. The goal is to aid in the release process, satisfying developers' requirements for speed while addressing operations' needs for stability. A key cornerstone of the DevOps movement is to commit to ongoing performance management and testing at all stages of an application lifecycle, from the earliest stages of development, to prevent problems from reaching production.

We at Compuware like to call this a lifecycle approach to performance management, since this term extends to include line of business executives. In order to successfully achieve a lifecycle approach, the proper type of APM system must be in place. First and foremost, this system must understand, from the user perspective, how applications and websites are performing. Achieving this level of insight requires monitoring of applications to go beyond the data center and start with the user perspective.

In addition, the proper type of APM system must:

- Deliver an integrated environment across stakeholders, to eliminate time spent correlating between different tools and to enable a common language of understanding. What applications are performing poorly? Why and whose problem is it? What's the impact on conversions?

- Provide automated, continuous visibility into what's going on – user behaviors, business transactions, conversion rates, etc.

- Offer actionable information for various stakeholders.

- Be application-centric as opposed to component-centric, which doesn't offer an overall view into how vital applications are performing. Additionally, be business aware, connecting IT to line of business by showing, for example, how IT is impacting the end- user experience and revenue.

In summary, it's important to remember that even the supposedly best application is never complete. Gartner estimates that only eight percent of an application's total cost of ownership (TCO) can be attributed to building, while the other 92 percent goes to ongoing enhancements, fixes and optimizationsi. A lifecycle approach to performance management, based on the right kind of APM solution, can help organizations navigate the vast complexity, ensure the strongest performing applications and make the best decisions that drive the user experience and revenues.

Steve Tack is VP of Product Management, Compuware APM.

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 ...