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A New Approach for Digital Performance

Creating one perspective to align business & technology objectives
Nicolas Robbe

As Kristopher Baxter from Netflix wrote in a recent post, “high performance is not an optional engineering goal – it's a requirement for creating great user-experiences.”

As a CMO, I could not agree more. In fact, I would even argue that for the business, application performance is only relevant if it correlates to meaningful user experiences and conversion metrics. The most common challenge hindering companies from realizing the full promise of application performance solutions has been the lack of a common language, and business-relevant metrics to measure monitor and set targets for customer experiences. The organizational divisions that separate development, IT operations and business teams have led to varied and disparate perspectives on end-user experience, how performance impacts business, and the level of investments needed to consistently excel.

After all, if development or IT teams can’t share a perspective on the end-user experience with marketing or eCommerce teams, how possibly can these groups effectively collaborate to create and execute a unified, cohesive plan to consistently improve what the business cares about?

Everyone understands that apps now drive business success, and that all parts of the organization share responsibility for ensuring that application performance is up to the challenge. But to really move beyond the traditional APM mindset, where performance is seen as a technical problem, marketing and business leaders across global industries are in need of new approach to monitoring. An approach that starts and end with the user experience.

Enter the Customer Experience Cockpit

It is one thing to gather data and intelligence about digital elements that affect end-users' experiences, whether those users are internal or external customers. But making it easy for everyone to view, trust, interpret, digest and put it to good use is quite another.

One approach that every digital organization should adopt is establishing a shared “Customer Experience Cockpit” where Digital business owners, development and IT operations can collaborate on a shared, real-time dashboard focused squarely on end-user experience. Think of it as a NOC focused on metrics the business cares about. What digital businesses have lacked for a long time is graphical, real-time view of their users' satisfaction — not just binary pass/fail metrics or page views. What is needed is an easy-to-consume, holistic view into individual end-to-end experience that offers easy detection and quick response to changing demands of their users.

Actionable intelligence on end-user experience, shared with business and technology leaders in a way that is meaningful to all has always been a key focus area for Dynatrace.

Here are the three keys of an effective customer experience cockpit:

1. A simple, real-time customer satisfaction metric or “index” that correlates million of performance measurements, and and each user individual context, like which phone they use or connection they are on, to estimate how each one perceives their experience.

2. Explorative analytics to quickly identify which patterns are problems. Is it users in a specific state or country? Is it a specific type of phone or browser causing the issue? Is it a specific landing page causing an issue?

3. Unified data, that contains the high level customer experience information, correlated with the associated deep technical data, so that any customer experience issue can be investigated, understood and resolved in minutes, not days or weeks.

The future success of digital performance management will be cemented in its ability to transform data into actionable insights in the most efficient, meaningful manner possible for all participants. By sharing this knowledge in a way that makes sense to every stakeholder in light of their varied missions, tomorrow’s leaders will have an unshakable advantage as they work to meet end-user expectations and their organizations competitiveness in the evolving digital economy.

Nicolas Robbe is CMO at Dynatrace.

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 ...

A New Approach for Digital Performance

Creating one perspective to align business & technology objectives
Nicolas Robbe

As Kristopher Baxter from Netflix wrote in a recent post, “high performance is not an optional engineering goal – it's a requirement for creating great user-experiences.”

As a CMO, I could not agree more. In fact, I would even argue that for the business, application performance is only relevant if it correlates to meaningful user experiences and conversion metrics. The most common challenge hindering companies from realizing the full promise of application performance solutions has been the lack of a common language, and business-relevant metrics to measure monitor and set targets for customer experiences. The organizational divisions that separate development, IT operations and business teams have led to varied and disparate perspectives on end-user experience, how performance impacts business, and the level of investments needed to consistently excel.

After all, if development or IT teams can’t share a perspective on the end-user experience with marketing or eCommerce teams, how possibly can these groups effectively collaborate to create and execute a unified, cohesive plan to consistently improve what the business cares about?

Everyone understands that apps now drive business success, and that all parts of the organization share responsibility for ensuring that application performance is up to the challenge. But to really move beyond the traditional APM mindset, where performance is seen as a technical problem, marketing and business leaders across global industries are in need of new approach to monitoring. An approach that starts and end with the user experience.

Enter the Customer Experience Cockpit

It is one thing to gather data and intelligence about digital elements that affect end-users' experiences, whether those users are internal or external customers. But making it easy for everyone to view, trust, interpret, digest and put it to good use is quite another.

One approach that every digital organization should adopt is establishing a shared “Customer Experience Cockpit” where Digital business owners, development and IT operations can collaborate on a shared, real-time dashboard focused squarely on end-user experience. Think of it as a NOC focused on metrics the business cares about. What digital businesses have lacked for a long time is graphical, real-time view of their users' satisfaction — not just binary pass/fail metrics or page views. What is needed is an easy-to-consume, holistic view into individual end-to-end experience that offers easy detection and quick response to changing demands of their users.

Actionable intelligence on end-user experience, shared with business and technology leaders in a way that is meaningful to all has always been a key focus area for Dynatrace.

Here are the three keys of an effective customer experience cockpit:

1. A simple, real-time customer satisfaction metric or “index” that correlates million of performance measurements, and and each user individual context, like which phone they use or connection they are on, to estimate how each one perceives their experience.

2. Explorative analytics to quickly identify which patterns are problems. Is it users in a specific state or country? Is it a specific type of phone or browser causing the issue? Is it a specific landing page causing an issue?

3. Unified data, that contains the high level customer experience information, correlated with the associated deep technical data, so that any customer experience issue can be investigated, understood and resolved in minutes, not days or weeks.

The future success of digital performance management will be cemented in its ability to transform data into actionable insights in the most efficient, meaningful manner possible for all participants. By sharing this knowledge in a way that makes sense to every stakeholder in light of their varied missions, tomorrow’s leaders will have an unshakable advantage as they work to meet end-user expectations and their organizations competitiveness in the evolving digital economy.

Nicolas Robbe is CMO at Dynatrace.

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 ...