Skip to main content

Instrumentation Score: Quantifying Telemetry Quality

Juraci Paixão Kröhling
OllyGarden

As observability engineers, we navigate a sea of telemetry daily. We instrument our applications, configure collectors, and build dashboards, all in pursuit of understanding our complex distributed systems. Yet, amidst this flood of data, a critical question often remains unspoken, or at best, answered by gut feeling: "Is our telemetry actually good?" We see the symptoms of bad telemetry — slow incident response, sky-high observability bills, and misleading alerts — but pinpointing the root causes in the instrumentation itself, and driving consistent improvements, remains a significant challenge. What if we could move beyond subjective assessments and cultivate a more data-driven approach to telemetry quality?

For too long, evaluating instrumentation effectiveness has been a subjective exercise. We've lacked a common language or a standard measure to truly understand if our telemetry is enriching our insights or just overgrowing the plot. Today, we're not just talking about a concept; we're inviting you to participate in shaping a foundational element for better observability: the Instrumentation Score specification. OllyGarden has initiated this open-source effort to provide a standardized way to measure the quality and effectiveness of OpenTelemetry instrumentation.

What Is the Instrumentation Score?

At its core, the Instrumentation Score is a numerical value derived from analyzing OTLP (OpenTelemetry Protocol) data streams. It's not a black box. The score is calculated based on a set of rules, each targeting a specific aspect of instrumentation quality. Each rule has a defined impact (e.g., Critical, Important, Normal, Low) and a weight, allowing for a nuanced assessment of your telemetry.

The primary focus is on OpenTelemetry, leveraging its semantic conventions and best practices as the bedrock for rule definitions. The score aims to be:

  • Objective: Providing a consistent, quantifiable measure, removing subjectivity.
  • Actionable: Highlighting specific areas of non-conformance so engineers know exactly what to fix.
  • Standardized: Offering a common and portable benchmark for services, teams, or even across organizations over time.

The Role of Rules

The true power of the Instrumentation Score lies in its rules, which codify known best practices and anti-patterns that directly impact data usability, cost, and analytical value. For instance, rules can ensure fundamental data integrity by flagging telemetry missing critical attributes like service.name, which is essential for aggregation, filtering, and ownership in most backends. Other rules address common cost and performance anti-patterns, such as identifying metric attributes with excessively high cardinality that can explode database costs and cripple query performance, or detecting overly large traces that increase network overhead and storage with verbose, low-value data.

Furthermore, the score highlights trace completeness by pointing out broken traces or missing root spans that hinder end-to-end visibility. It also encourages efficient signal usage, for example, by discouraging the use of expensive logs for simple event counting when metrics would be a more performant and cost-effective choice. These examples merely scratch the surface; the specification is designed to be extensible, allowing the community to define rules for a wide array of scenarios, including adherence to specific semantic conventions or the use of appropriate instrumentation SDK versions.

The need for such a standardized approach isn't just theoretical; it's a practical challenge faced by engineering teams globally. James Moessis, Senior Software Engineer in the Observability team at Atlassian, shares this perspective: "Instrumentation Score is a much-needed innovation that fills a critical gap in the observability ecosystem. It's the kind of idea that made me think, 'We should have done this ages ago.' At Atlassian, our observability team is constantly tackling telemetry quality issues across the company's many services. With so many services, it can sometimes feel like we are trying to boil the ocean. Having a standardized 'instrumentation score' would certainly help us identify and report to teams where issues are."

James' sentiment echoes what many in the observability field experience. It underscores the value of a common yardstick to help teams prioritize improvements and communicate effectively about telemetry health. This widespread need is precisely why we believe the Instrumentation Score must be an open, collaborative effort.

An Open Invitation: Why Your Contribution Is Crucial

OllyGarden has kickstarted the Instrumentation Score specification and is committed to its development as an open-source, community-driven standard. We are ensuring this initiative fosters an open governance model, drawing support and contributions from across the industry, including companies like Dash0, New Relic, Splunk, Datadog, and Grafana Labs.

But the true strength and comprehensiveness of this score will come from you — the observability engineers in the trenches. The initial set of rules and conventions provides a solid foundation, but we all know that "good" and "bad" telemetry patterns often emerge from hard-won experience with specific technologies, platforms, or failure modes.

This is where you come in. We are actively seeking contributions to the Instrumentation Score specification:

  • Propose New Rules: Encounter a common instrumentation pitfall that isn't covered? Define it. What about rules for serverless environments, service meshes, or emerging technologies like AI/ML observability? Your insights are invaluable.
  • Refine Existing Rule Concepts: Have ideas on how to better detect a particular anti-pattern? Suggest improvements to rule definition, impact weighting, or metadata.
  • Share Edge Cases and Anti-Patterns: Help us build a comprehensive knowledge base by sharing real-world examples of telemetry that led you astray or cost you a fortune.
  • Debate and Discuss: Engage in discussions around rule definitions, ensuring they are clear, actionable, and universally applicable.

By contributing, you help codify the collective wisdom of the observability community into a practical, actionable standard. This isn't just about defining rules; it's about creating a shared understanding and a common language for telemetry excellence. You'll be shaping a tool that benefits the entire ecosystem, helps tame telemetry chaos, and ultimately makes our collective lives as engineers easier.

Getting Started and Making an Impact

The Instrumentation Score is more than just a number; it's a catalyst for conversation and continuous improvement in how we instrument our systems. It's a tool to help us all move from reactive troubleshooting to proactive telemetry optimization.

We invite you to:

1. Explore the Instrumentation Score landing page for an overview.
2. Dive into the specification on GitHub. This is where the collaborative work happens. Familiarize yourself with the current structure and rule ideas.
3. Contribute: Open an issue to discuss a new rule idea or suggest an improvement. Better yet, submit a pull request with your proposed rule definition, including its rationale, suggested severity, and how it could be detected. Let's build this together.

OllyGarden is proud to have planted the first seed for the Instrumentation Score. Now, let's cultivate it together. Let's build a standard that empowers every engineer to confidently answer "Yes, our telemetry is good, and here's how we know."

Juraci Paixão Kröhling is a Software Engineer at OllyGarden, OpenTelemetry Governing Board Member and CNCF Ambassador

The Latest

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Instrumentation Score: Quantifying Telemetry Quality

Juraci Paixão Kröhling
OllyGarden

As observability engineers, we navigate a sea of telemetry daily. We instrument our applications, configure collectors, and build dashboards, all in pursuit of understanding our complex distributed systems. Yet, amidst this flood of data, a critical question often remains unspoken, or at best, answered by gut feeling: "Is our telemetry actually good?" We see the symptoms of bad telemetry — slow incident response, sky-high observability bills, and misleading alerts — but pinpointing the root causes in the instrumentation itself, and driving consistent improvements, remains a significant challenge. What if we could move beyond subjective assessments and cultivate a more data-driven approach to telemetry quality?

For too long, evaluating instrumentation effectiveness has been a subjective exercise. We've lacked a common language or a standard measure to truly understand if our telemetry is enriching our insights or just overgrowing the plot. Today, we're not just talking about a concept; we're inviting you to participate in shaping a foundational element for better observability: the Instrumentation Score specification. OllyGarden has initiated this open-source effort to provide a standardized way to measure the quality and effectiveness of OpenTelemetry instrumentation.

What Is the Instrumentation Score?

At its core, the Instrumentation Score is a numerical value derived from analyzing OTLP (OpenTelemetry Protocol) data streams. It's not a black box. The score is calculated based on a set of rules, each targeting a specific aspect of instrumentation quality. Each rule has a defined impact (e.g., Critical, Important, Normal, Low) and a weight, allowing for a nuanced assessment of your telemetry.

The primary focus is on OpenTelemetry, leveraging its semantic conventions and best practices as the bedrock for rule definitions. The score aims to be:

  • Objective: Providing a consistent, quantifiable measure, removing subjectivity.
  • Actionable: Highlighting specific areas of non-conformance so engineers know exactly what to fix.
  • Standardized: Offering a common and portable benchmark for services, teams, or even across organizations over time.

The Role of Rules

The true power of the Instrumentation Score lies in its rules, which codify known best practices and anti-patterns that directly impact data usability, cost, and analytical value. For instance, rules can ensure fundamental data integrity by flagging telemetry missing critical attributes like service.name, which is essential for aggregation, filtering, and ownership in most backends. Other rules address common cost and performance anti-patterns, such as identifying metric attributes with excessively high cardinality that can explode database costs and cripple query performance, or detecting overly large traces that increase network overhead and storage with verbose, low-value data.

Furthermore, the score highlights trace completeness by pointing out broken traces or missing root spans that hinder end-to-end visibility. It also encourages efficient signal usage, for example, by discouraging the use of expensive logs for simple event counting when metrics would be a more performant and cost-effective choice. These examples merely scratch the surface; the specification is designed to be extensible, allowing the community to define rules for a wide array of scenarios, including adherence to specific semantic conventions or the use of appropriate instrumentation SDK versions.

The need for such a standardized approach isn't just theoretical; it's a practical challenge faced by engineering teams globally. James Moessis, Senior Software Engineer in the Observability team at Atlassian, shares this perspective: "Instrumentation Score is a much-needed innovation that fills a critical gap in the observability ecosystem. It's the kind of idea that made me think, 'We should have done this ages ago.' At Atlassian, our observability team is constantly tackling telemetry quality issues across the company's many services. With so many services, it can sometimes feel like we are trying to boil the ocean. Having a standardized 'instrumentation score' would certainly help us identify and report to teams where issues are."

James' sentiment echoes what many in the observability field experience. It underscores the value of a common yardstick to help teams prioritize improvements and communicate effectively about telemetry health. This widespread need is precisely why we believe the Instrumentation Score must be an open, collaborative effort.

An Open Invitation: Why Your Contribution Is Crucial

OllyGarden has kickstarted the Instrumentation Score specification and is committed to its development as an open-source, community-driven standard. We are ensuring this initiative fosters an open governance model, drawing support and contributions from across the industry, including companies like Dash0, New Relic, Splunk, Datadog, and Grafana Labs.

But the true strength and comprehensiveness of this score will come from you — the observability engineers in the trenches. The initial set of rules and conventions provides a solid foundation, but we all know that "good" and "bad" telemetry patterns often emerge from hard-won experience with specific technologies, platforms, or failure modes.

This is where you come in. We are actively seeking contributions to the Instrumentation Score specification:

  • Propose New Rules: Encounter a common instrumentation pitfall that isn't covered? Define it. What about rules for serverless environments, service meshes, or emerging technologies like AI/ML observability? Your insights are invaluable.
  • Refine Existing Rule Concepts: Have ideas on how to better detect a particular anti-pattern? Suggest improvements to rule definition, impact weighting, or metadata.
  • Share Edge Cases and Anti-Patterns: Help us build a comprehensive knowledge base by sharing real-world examples of telemetry that led you astray or cost you a fortune.
  • Debate and Discuss: Engage in discussions around rule definitions, ensuring they are clear, actionable, and universally applicable.

By contributing, you help codify the collective wisdom of the observability community into a practical, actionable standard. This isn't just about defining rules; it's about creating a shared understanding and a common language for telemetry excellence. You'll be shaping a tool that benefits the entire ecosystem, helps tame telemetry chaos, and ultimately makes our collective lives as engineers easier.

Getting Started and Making an Impact

The Instrumentation Score is more than just a number; it's a catalyst for conversation and continuous improvement in how we instrument our systems. It's a tool to help us all move from reactive troubleshooting to proactive telemetry optimization.

We invite you to:

1. Explore the Instrumentation Score landing page for an overview.
2. Dive into the specification on GitHub. This is where the collaborative work happens. Familiarize yourself with the current structure and rule ideas.
3. Contribute: Open an issue to discuss a new rule idea or suggest an improvement. Better yet, submit a pull request with your proposed rule definition, including its rationale, suggested severity, and how it could be detected. Let's build this together.

OllyGarden is proud to have planted the first seed for the Instrumentation Score. Now, let's cultivate it together. Let's build a standard that empowers every engineer to confidently answer "Yes, our telemetry is good, and here's how we know."

Juraci Paixão Kröhling is a Software Engineer at OllyGarden, OpenTelemetry Governing Board Member and CNCF Ambassador

The Latest

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...