Why Metrics Must Guide Your DevOps Initiative

Jonah Kowall
Learn more about Kentik

Metrics-oriented thinking is key to continuous improvement – and a core tenant of any agile or DevOps philosophy. Metrics are factual and once agreed upon, these facts are used to drive discussions and methods. They also allow for a collaborative effort to execute decisions that contribute towards business outcomes.

DevOps, although becoming a commonly used job title, is not a role or person and there is no playbook or rule set to follow. Instead, DevOps is a philosophy which spans people, process, and technology. The goal is releasing better software more rapidly, and keeping said software up and running by joining development and operational responsibilities together.

Additionally, DevOps aims to improve business outcomes, but there are challenges in selecting the right metrics and collecting the metric data. Continuous improvement requires continuous change, measurement, and iteration. What’s more, the agreed-upon metrics drive this cycle, but also create insights for the broader organization.


Data-Driven DevOps

A successful DevOps transformation focuses on a couple areas. To start, a culture change is needed between development and operations teams. Another core tenant of DevOps is measurement. In order to accomplish a true DevOps transformation, it’s important to measure the current situation and regularly review metrics which indicate improvement or degradation. One of the core tenants of DevOps is measurement, and using said measurements as facts when driving decision making. These metrics should span several areas which may have been considered disjointed in the past.

To help DevOps teams think of possible metrics and how these metrics relate to key initiatives, Gartner recently released this useful metrics pyramid for DevOps:


Many of these metrics span development, operations, and most importantly – the business. They measure efficiency, quality, and velocity. However, Gartner points out that the hardest part is often defining what we can collect, take action upon, audit, and use to drive a lifecycle.

The second challenge (which Gartner does not discuss) is how these metrics should be linked together to offer meaningful insights. If the metrics do not allow linkage between a release and business performance, attribution gaps remain. And unfortunately, many enterprises today analyze metrics that have a lack of linkage or relationship between them.

To help with these relationships, context is critical. Without context, metrics can be open to interpretation, especially as you move up the Gartner pyramid. So it’s crucial to be able to link metrics together and attribute earnings or cash flow with a release or change that represents improvements in the application.

Additionally, metrics should be able to drive visibility inside the application without creating an additional burden for developers. With automated instrumentation, metric data can be produced consistently and comprehensively across all teams. This is extremely beneficial as many teams have different ways of collecting data, which can traditionally lead to inconsistencies. Consistent measurements should always be obtained from the application components and desired business outcomes of the application.

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Why Metrics Must Guide Your DevOps Initiative

Jonah Kowall
Learn more about Kentik

Metrics-oriented thinking is key to continuous improvement – and a core tenant of any agile or DevOps philosophy. Metrics are factual and once agreed upon, these facts are used to drive discussions and methods. They also allow for a collaborative effort to execute decisions that contribute towards business outcomes.

DevOps, although becoming a commonly used job title, is not a role or person and there is no playbook or rule set to follow. Instead, DevOps is a philosophy which spans people, process, and technology. The goal is releasing better software more rapidly, and keeping said software up and running by joining development and operational responsibilities together.

Additionally, DevOps aims to improve business outcomes, but there are challenges in selecting the right metrics and collecting the metric data. Continuous improvement requires continuous change, measurement, and iteration. What’s more, the agreed-upon metrics drive this cycle, but also create insights for the broader organization.


Data-Driven DevOps

A successful DevOps transformation focuses on a couple areas. To start, a culture change is needed between development and operations teams. Another core tenant of DevOps is measurement. In order to accomplish a true DevOps transformation, it’s important to measure the current situation and regularly review metrics which indicate improvement or degradation. One of the core tenants of DevOps is measurement, and using said measurements as facts when driving decision making. These metrics should span several areas which may have been considered disjointed in the past.

To help DevOps teams think of possible metrics and how these metrics relate to key initiatives, Gartner recently released this useful metrics pyramid for DevOps:


Many of these metrics span development, operations, and most importantly – the business. They measure efficiency, quality, and velocity. However, Gartner points out that the hardest part is often defining what we can collect, take action upon, audit, and use to drive a lifecycle.

The second challenge (which Gartner does not discuss) is how these metrics should be linked together to offer meaningful insights. If the metrics do not allow linkage between a release and business performance, attribution gaps remain. And unfortunately, many enterprises today analyze metrics that have a lack of linkage or relationship between them.

To help with these relationships, context is critical. Without context, metrics can be open to interpretation, especially as you move up the Gartner pyramid. So it’s crucial to be able to link metrics together and attribute earnings or cash flow with a release or change that represents improvements in the application.

Additionally, metrics should be able to drive visibility inside the application without creating an additional burden for developers. With automated instrumentation, metric data can be produced consistently and comprehensively across all teams. This is extremely beneficial as many teams have different ways of collecting data, which can traditionally lead to inconsistencies. Consistent measurements should always be obtained from the application components and desired business outcomes of the application.

Hot Topics

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Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

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 ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

Explainability Is the New Battleground in AI-Powered Observability

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 ...

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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 ...

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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.