Advanced Observability Teams See Big Efficiency Gains - Part 1

George Miranda
Honeycomb.io

As our production application systems continuously increase in complexity, the challenges of understanding, debugging, and improving them keep growing by orders of magnitude. The practice of Observability addresses both the social and the technological challenges of wrangling complexity and working toward achieving production excellence. New research shows how observable systems and practices are changing the application performance management (APM) landscape.

Observability Requires Both Technical and Social Approaches

Tooling alone can't solve anything, it's just a necessary part of any solution. Tackling the challenges of managing complex production systems isn't just a technical problem and it isn't just a social problem. We manage sociotechnical systems and any reasonable solution must take that into account in order to be effective.

Observability isn't logs, metrics, and tracing. Yes, those aspects are important. Those tools can help shed light on what's happening in the systems that are critical to your business. However, there's a big difference between having tools that provide instrumentation and using them to achieve better outcomes. Many of today's tools require you to predict the future by knowing in advance what conditions to monitor, which trends to look for, or the correlations you need to make to find application performance hotspots.

The coveted observability sweet spot is finding the unknown unknowns. Observability is a sociotechnical practice that allows you to answer any arbitrary questions about your environment, without needing to know ahead of time what you wanted to ask. However, it's doing the work that proves a bit more challenging for many teams, especially those weaning off legacy tools.

Practicing observability is a journey. It takes time for entire teams to adopt new practices and shift mindsets to a model of shared ownership. Our new study shows how different teams are practicing, or intending to practice, observability within the next two years. The report also examines the challenges teams face and the practices they are implementing as they progress on their observability journey.

Observability Maturity Research Findings

Teams must decide how to start their observability journey. Those early decisions have a high degree of impact because they influence both tool choices and habits during the software development and delivery lifecycle. Teams that adopt recommended observability practise to an advanced degree see greater benefits than less advanced teams. Advanced teams stabilize their systems, spend less time reactively fixing issues in production/refactoring code/resolving technical debt, and spend more time proactively innovating. 

The report affirms that adopting observability tools, site reliability engineering (SRE) practices, and a culture of shared ownership translates to efficiencies across the software engineering cycle, better end-user experiences, and ultimately helps teams achieve production excellence.

Outcomes are much more pronounced when teams apply observability mindsets and processes in conjunction with tooling. That combination can lead to a virtuous cycle of reinforcement, presuming those teams are using tools purposely designed to address observability use-cases. Research findings show that most teams adopt a handful of tools across disparate teams to accomplish daily tasks. Yet it's that same juggling of different tools that creates confusion, frustration, an oft-heard complaint of tool bloat, and ultimately leads to slower performance.

Go to Advanced Observability Teams See Big Efficiency Gains - Part 2

George Miranda is Product Marketing Director at Honeycomb.io

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Advanced Observability Teams See Big Efficiency Gains - Part 1

George Miranda
Honeycomb.io

As our production application systems continuously increase in complexity, the challenges of understanding, debugging, and improving them keep growing by orders of magnitude. The practice of Observability addresses both the social and the technological challenges of wrangling complexity and working toward achieving production excellence. New research shows how observable systems and practices are changing the application performance management (APM) landscape.

Observability Requires Both Technical and Social Approaches

Tooling alone can't solve anything, it's just a necessary part of any solution. Tackling the challenges of managing complex production systems isn't just a technical problem and it isn't just a social problem. We manage sociotechnical systems and any reasonable solution must take that into account in order to be effective.

Observability isn't logs, metrics, and tracing. Yes, those aspects are important. Those tools can help shed light on what's happening in the systems that are critical to your business. However, there's a big difference between having tools that provide instrumentation and using them to achieve better outcomes. Many of today's tools require you to predict the future by knowing in advance what conditions to monitor, which trends to look for, or the correlations you need to make to find application performance hotspots.

The coveted observability sweet spot is finding the unknown unknowns. Observability is a sociotechnical practice that allows you to answer any arbitrary questions about your environment, without needing to know ahead of time what you wanted to ask. However, it's doing the work that proves a bit more challenging for many teams, especially those weaning off legacy tools.

Practicing observability is a journey. It takes time for entire teams to adopt new practices and shift mindsets to a model of shared ownership. Our new study shows how different teams are practicing, or intending to practice, observability within the next two years. The report also examines the challenges teams face and the practices they are implementing as they progress on their observability journey.

Observability Maturity Research Findings

Teams must decide how to start their observability journey. Those early decisions have a high degree of impact because they influence both tool choices and habits during the software development and delivery lifecycle. Teams that adopt recommended observability practise to an advanced degree see greater benefits than less advanced teams. Advanced teams stabilize their systems, spend less time reactively fixing issues in production/refactoring code/resolving technical debt, and spend more time proactively innovating. 

The report affirms that adopting observability tools, site reliability engineering (SRE) practices, and a culture of shared ownership translates to efficiencies across the software engineering cycle, better end-user experiences, and ultimately helps teams achieve production excellence.

Outcomes are much more pronounced when teams apply observability mindsets and processes in conjunction with tooling. That combination can lead to a virtuous cycle of reinforcement, presuming those teams are using tools purposely designed to address observability use-cases. Research findings show that most teams adopt a handful of tools across disparate teams to accomplish daily tasks. Yet it's that same juggling of different tools that creates confusion, frustration, an oft-heard complaint of tool bloat, and ultimately leads to slower performance.

Go to Advanced Observability Teams See Big Efficiency Gains - Part 2

George Miranda is Product Marketing Director at Honeycomb.io

Related Links

Hot Topics

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Your Observability Stack Has a Telemetry Pipeline Problem

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

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80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

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40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Almost Half of AI-Generated Code Fails in Production

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

AI Requires Design, Not Edicts

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

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