Skip to main content

The Age of the Customer Requires a Shift in Monitoring Focus

J. Rakowski

In my February 2014 report Left–Shift Technology Monitoring For Success In The Age Of The Customer, I explore what the near future will bring for technology monitoring approaches and solutions. Today, for the typical I&O organization, successful technology or service delivery monitoring focuses on two main areas. Firstly, availability, so ensuring the technology underpinning business services is up and available when needed. And secondly, performance, so making sure that technology utilized (applications and associated workloads) is fast enough for the business service it supports.

There is a major problem with this approach though. As the famous author Harper Lee stated “We know all men are not created equal” and the same can be said about your customers and employees – they are not all equal and the rapid pace of consumer technology innovation in areas such as mobile means that they will utilize technology in different ways to support productivity or to engage with your enterprise as a customer.

Our relationship with technology is changing rapidly. It is becoming more intimate and personal, meaning that datacenter centric monitoring approaches that focus on availability and performance alone, while still essential, are only the beginning of what is required for a holistic technology monitoring strategy.

In the age of the customer, it’s increasingly easy for your customers and even employees to switch to another technology provider. They will do this if they feel their experience needs are not being met from the technology based services provided.

Experience can be broken down into three simple facets:

1. Does the technology based service meet needs?

2. Is it easy to use?

3. Is it enjoyable?

The technology monitoring market in recent years has evolved from a focus on infrastructure monitoring, to application monitoring and the emergence of End User Experience Monitoring/Management (EUEM). EUEM looks to understand technology based business service performance from an employee or increasingly a customer perspective. However, many methods that I see in the market today are still application-centric in their monitoring approach e.g. the operator configures monitoring based on specified applications.

This means that many EUEM implementations fail to understand the full employee or customer journey. This includes how they use technology in in order to be productive within the enterprise or how they engage with enterprises, externally as customers. To understand the full technology interaction journey requires a more detailed form of end user monitoring which we define as End User Behavior Monitoring.

End User Behavior Monitoring has the capability of monitoring all interactions or activity between employees/customers and the technology they use, i.e consumer devices and applications. The aim here is not to alert, but to build up a detailed understanding of how the various types of business users or personas interact with business services so as to ensure that experience is tailored appropriately.

The following graphic summarizes this form of monitoring and its relation to other monitoring approaches on the market today:

Image removed.

Of course, end user behavior monitoring may ring alarm bells in regards to data privacy, but as our relationship with technology becomes increasingly more personal then I expect some of these concerns to fade away along with associated legislation.

If you are a Forrester client then my report highlights the concepts of End User Behavior Monitoring in detail. I also have an associated report coming out in Q2 2014 which looks at solutions on the market today which are capable of doing this form of monitoring within the enterprise.

John Rakowski is Analyst, Infrastructure and Operations, at Forrester.

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 ...

The Age of the Customer Requires a Shift in Monitoring Focus

J. Rakowski

In my February 2014 report Left–Shift Technology Monitoring For Success In The Age Of The Customer, I explore what the near future will bring for technology monitoring approaches and solutions. Today, for the typical I&O organization, successful technology or service delivery monitoring focuses on two main areas. Firstly, availability, so ensuring the technology underpinning business services is up and available when needed. And secondly, performance, so making sure that technology utilized (applications and associated workloads) is fast enough for the business service it supports.

There is a major problem with this approach though. As the famous author Harper Lee stated “We know all men are not created equal” and the same can be said about your customers and employees – they are not all equal and the rapid pace of consumer technology innovation in areas such as mobile means that they will utilize technology in different ways to support productivity or to engage with your enterprise as a customer.

Our relationship with technology is changing rapidly. It is becoming more intimate and personal, meaning that datacenter centric monitoring approaches that focus on availability and performance alone, while still essential, are only the beginning of what is required for a holistic technology monitoring strategy.

In the age of the customer, it’s increasingly easy for your customers and even employees to switch to another technology provider. They will do this if they feel their experience needs are not being met from the technology based services provided.

Experience can be broken down into three simple facets:

1. Does the technology based service meet needs?

2. Is it easy to use?

3. Is it enjoyable?

The technology monitoring market in recent years has evolved from a focus on infrastructure monitoring, to application monitoring and the emergence of End User Experience Monitoring/Management (EUEM). EUEM looks to understand technology based business service performance from an employee or increasingly a customer perspective. However, many methods that I see in the market today are still application-centric in their monitoring approach e.g. the operator configures monitoring based on specified applications.

This means that many EUEM implementations fail to understand the full employee or customer journey. This includes how they use technology in in order to be productive within the enterprise or how they engage with enterprises, externally as customers. To understand the full technology interaction journey requires a more detailed form of end user monitoring which we define as End User Behavior Monitoring.

End User Behavior Monitoring has the capability of monitoring all interactions or activity between employees/customers and the technology they use, i.e consumer devices and applications. The aim here is not to alert, but to build up a detailed understanding of how the various types of business users or personas interact with business services so as to ensure that experience is tailored appropriately.

The following graphic summarizes this form of monitoring and its relation to other monitoring approaches on the market today:

Image removed.

Of course, end user behavior monitoring may ring alarm bells in regards to data privacy, but as our relationship with technology becomes increasingly more personal then I expect some of these concerns to fade away along with associated legislation.

If you are a Forrester client then my report highlights the concepts of End User Behavior Monitoring in detail. I also have an associated report coming out in Q2 2014 which looks at solutions on the market today which are capable of doing this form of monitoring within the enterprise.

John Rakowski is Analyst, Infrastructure and Operations, at Forrester.

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 ...