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The OpenTelemetry Getting Started Survey: Understanding Users' Observability Journeys

Ana Margarita Medina
Senior Staff Developer
ServiceNow

Organizations can face significant challenges, ranging from skill development to user adoption, when implementing new technologies. This is particularly evident in the realm of observability, an increasingly critical area for organizations striving to maintain optimal performance and reliability across digital applications. Recently, the OpenTelemetry End-User SIG surveyed more than 100 OpenTelemetry users to learn more about their observability journeys and what resources deliver the most value when establishing an observability practice.

Most respondents have initiated their observability journey, whether they are in the process of standing up an observability practice or are already well-established. Regardless of experience level, there's a clear need for more support and continued education, especially in helping those who are just starting with observability technologies. When asked what resources they wish they had when getting started with OpenTelemetry, more than half (67%) said they wanted comprehensive documentation, quickly followed by reference implementations for instrumentation (65%), and more detailed tutorials (63%).



When getting started with observability, most respondents are working with containerization technologies, with about 80% using Kubernetes and 63% using Docker.


While quite a few languages are used across organizations, more than 50% of respondents utilize JavaScript, Java, Go, and Python.


The majority of respondents stated that Traces Specification, Instrumentation APIs and SDKs, and Metrics Specification are the most important aspects of their OpenTelemetry journeys.


Observability will continue to be a cornerstone for organizations to not only measure and understand application performance, but to also build resilience into technology stacks. It's imperative for leaders to empower their teams with the necessary tools and knowledge, as they play a pivotal role in the successful adoption and implementation of observability practices. By equipping teams with the proper resources, organizations can overcome the common challenges associated with implementing new technologies, ensuring a smoother transition and maximizing the full potential of their observability initiatives.

Ana Margarita Medina is a Senior Staff Developer at ServiceNow

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The OpenTelemetry Getting Started Survey: Understanding Users' Observability Journeys

Ana Margarita Medina
Senior Staff Developer
ServiceNow

Organizations can face significant challenges, ranging from skill development to user adoption, when implementing new technologies. This is particularly evident in the realm of observability, an increasingly critical area for organizations striving to maintain optimal performance and reliability across digital applications. Recently, the OpenTelemetry End-User SIG surveyed more than 100 OpenTelemetry users to learn more about their observability journeys and what resources deliver the most value when establishing an observability practice.

Most respondents have initiated their observability journey, whether they are in the process of standing up an observability practice or are already well-established. Regardless of experience level, there's a clear need for more support and continued education, especially in helping those who are just starting with observability technologies. When asked what resources they wish they had when getting started with OpenTelemetry, more than half (67%) said they wanted comprehensive documentation, quickly followed by reference implementations for instrumentation (65%), and more detailed tutorials (63%).



When getting started with observability, most respondents are working with containerization technologies, with about 80% using Kubernetes and 63% using Docker.


While quite a few languages are used across organizations, more than 50% of respondents utilize JavaScript, Java, Go, and Python.


The majority of respondents stated that Traces Specification, Instrumentation APIs and SDKs, and Metrics Specification are the most important aspects of their OpenTelemetry journeys.


Observability will continue to be a cornerstone for organizations to not only measure and understand application performance, but to also build resilience into technology stacks. It's imperative for leaders to empower their teams with the necessary tools and knowledge, as they play a pivotal role in the successful adoption and implementation of observability practices. By equipping teams with the proper resources, organizations can overcome the common challenges associated with implementing new technologies, ensuring a smoother transition and maximizing the full potential of their observability initiatives.

Ana Margarita Medina is a Senior Staff Developer at ServiceNow

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Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

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We just surveyed 300 frontend and mobile engineers across 16 countries, and the finding that keeps sticking with me isn't the one about AI. It's this: 74% of engineering teams rate themselves in the "middle" of the observability maturity scale. Not reactive, not strategic. Stuck in the middle. They have dashboards, they have tracing, they have alerts. And yet when something goes wrong, they still can't tell you why ...

In MEAN TIME TO INSIGHT Episode 25, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses  AI's impact on the Wide Area Network (WAN) ... 

Application performance monitoring (APM) dashboards are only as useful as what they are configured to measure. The default setup covers obvious failure modes such as downtime, error spikes, and latency breaches, but it does not cover everything. Some failures produce no alerts or anomalies. The dashboard stays green while users experience a broken product. Here are six signs that is happening ...

The race to deploy AI is largely over. Most enterprises have entered it. The question now is not whether artificial intelligence is running inside the organization. The question is whether anyone is genuinely responsible for what it does. That is not a technical question. It is a leadership one. And most organizations are not yet structured to answer it honestly ...

A new analysis of 250 real-world queries across common retail tasks, such as product pricing, availability, ratings, shipping and specifications, reveals systemic inefficiency at the heart of web-based AI agents. On average, 97.9% of the data retrieved by agents from live web pages is irrelevant to the query being answered. Specifically, the average page ingested ran nearly 9,000 characters, while the average answer was just 32 characters, resulting in a noise-to-signal ratio of 278:1. Price queries were the most extreme outlier, with noise rates approaching 99.5%. That's not a rounding error. That's a structural problem ...

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...