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APM and Viewpoints - Part 2

Terry Critchley

An important aspect of performance (and other) monitoring is where the observer stands when looking at the IT scenario. Each participant has a different view of what is bad performance - network, database, web, system, user personnel, management and external people - customers, regulatory bodies etc. These are what I call viewpoints, a popular concept in IT architecture design methods.

Start with APM and Viewpoints - Part 1

Operations Viewpoint

Operations people, but not the business user and others, will be desperately interested in:

■ % Utilizations

■ Wait times

■ Disk space used

■ Disk I/O Throughput

■ Disk I/O response time

■ Memory % used

■ Page rate

■ etc. etc.

End User Viewpoint

The previous factors are meaningless to the user of the application, who is more interested in:

■ Response times ( which depends on overall latency, percentiles, variations but they are not interested in that detail)

■ Variability of that response; large variations equal poor productivity via irritation and loss of concentration

■ Throughput of work where applicable

■ Availability

■ Other "speed" factors relating to their work

Business Manager Viewpoint

This viewpoint might reflect that of the end user is some respects, but will often be even more general:

■ What is the time between receipt of an order, shipment, invoicing and reconciliation?

■ Is the customer satisfied with this?

■ Can we speed up the processes without excessive cost?

■ Other business aspects

There are other people who will have different requirements and perspectives of performance: service desk, external customers, especially website users, and possibly regulatory bodies. They are important and in performance life, one size does not fit all.

The Outcome

When considering performance management, which is more than simply monitoring, the differing requirements (viewpoints) of various stakeholders needs to be taken into account. It is often difficult to retrofit analysis of performance data to cater for people not considered at the design stage. You may be asked by the CEO, out of the blue: "Why do we take 2 days to issue an invoice after shipment while competitor X takes one?"

Role of the SLA

Whose level of service (quality of service, QoS) are we talking about? Basically, all the types of person outlined above. This (rather these) QoS are usually formalized in a Service Level Agreement or SLA. This will dictate what needs to be measured and analyzed:

"If you can't measure it or derive it, you can't report it."

"A service-level agreement (SLA) is a contract between a service provider and its internal or external customers that documents what services the provider will furnish and defines the performance standards the provider is obligated to meet." [WhatIs.com].

The trick here is to marry these viewpoints which means translating the operational data into service level agreement (SLA) terms and hence into stakeholder perspective, another word for viewpoint All this is complicated when one moves from the relatively simple classical IT environment to the mixed web and application environments, rendered even more difficult to fathom by virtualization and clouds.

The Endpoint

There is no reason why external customers shouldn't be part of any SLA drawn up if the APM setup is designed to cover all important stakeholders.

In addition, it should be transparent to the stakeholders outside operations whether the system runs native, virtualized, in a cloud or in a series of school exercise books. The APM design with these differing viewpoints in mind is the key aspect of this.

Dr. Terry Critchley is the Author of "Making It in IT", "High Performance IT Services" and “High Availability IT Services”.

This blog was created from extracts from Terry Critchley's book: High Performance IT Services [ August 25 2016]

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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 ...

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 ...

APM and Viewpoints - Part 2

Terry Critchley

An important aspect of performance (and other) monitoring is where the observer stands when looking at the IT scenario. Each participant has a different view of what is bad performance - network, database, web, system, user personnel, management and external people - customers, regulatory bodies etc. These are what I call viewpoints, a popular concept in IT architecture design methods.

Start with APM and Viewpoints - Part 1

Operations Viewpoint

Operations people, but not the business user and others, will be desperately interested in:

■ % Utilizations

■ Wait times

■ Disk space used

■ Disk I/O Throughput

■ Disk I/O response time

■ Memory % used

■ Page rate

■ etc. etc.

End User Viewpoint

The previous factors are meaningless to the user of the application, who is more interested in:

■ Response times ( which depends on overall latency, percentiles, variations but they are not interested in that detail)

■ Variability of that response; large variations equal poor productivity via irritation and loss of concentration

■ Throughput of work where applicable

■ Availability

■ Other "speed" factors relating to their work

Business Manager Viewpoint

This viewpoint might reflect that of the end user is some respects, but will often be even more general:

■ What is the time between receipt of an order, shipment, invoicing and reconciliation?

■ Is the customer satisfied with this?

■ Can we speed up the processes without excessive cost?

■ Other business aspects

There are other people who will have different requirements and perspectives of performance: service desk, external customers, especially website users, and possibly regulatory bodies. They are important and in performance life, one size does not fit all.

The Outcome

When considering performance management, which is more than simply monitoring, the differing requirements (viewpoints) of various stakeholders needs to be taken into account. It is often difficult to retrofit analysis of performance data to cater for people not considered at the design stage. You may be asked by the CEO, out of the blue: "Why do we take 2 days to issue an invoice after shipment while competitor X takes one?"

Role of the SLA

Whose level of service (quality of service, QoS) are we talking about? Basically, all the types of person outlined above. This (rather these) QoS are usually formalized in a Service Level Agreement or SLA. This will dictate what needs to be measured and analyzed:

"If you can't measure it or derive it, you can't report it."

"A service-level agreement (SLA) is a contract between a service provider and its internal or external customers that documents what services the provider will furnish and defines the performance standards the provider is obligated to meet." [WhatIs.com].

The trick here is to marry these viewpoints which means translating the operational data into service level agreement (SLA) terms and hence into stakeholder perspective, another word for viewpoint All this is complicated when one moves from the relatively simple classical IT environment to the mixed web and application environments, rendered even more difficult to fathom by virtualization and clouds.

The Endpoint

There is no reason why external customers shouldn't be part of any SLA drawn up if the APM setup is designed to cover all important stakeholders.

In addition, it should be transparent to the stakeholders outside operations whether the system runs native, virtualized, in a cloud or in a series of school exercise books. The APM design with these differing viewpoints in mind is the key aspect of this.

Dr. Terry Critchley is the Author of "Making It in IT", "High Performance IT Services" and “High Availability IT Services”.

This blog was created from extracts from Terry Critchley's book: High Performance IT Services [ August 25 2016]

Hot Topics

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 ...