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6 Most Common Observability Mistakes

Apr 26, 2024 / 5 minute read

Diagnose issues faster and enhance your system performance by exploring the most common observability mistakes you should avoid.


Observability is the process of analyzing a system’s outputs to see the internal state of an application. This method is crucial for modern software systems, as it allows organizations and their IT teams to understand the operations, detect issues, and improve a system's overall reliability and efficiency.

The need for effective and comprehensive observability increases as modern systems become more complex. Avoiding common mistakes when executing observability helps build a more resilient and competent application.

Find out more about the common mistakes in observability practices to know how to make your implementation more successful. Read on.

The Common Pitfalls of Observability

Observability is an approach to understanding and managing complex systems. It aims to understand the internal state of a system based on its external outputs. It focuses on collecting, analyzing, and interpreting data from various sources within a system.

While observability is related to monitoring, both concepts serve different purposes. Monitoring involves tracking predefined metrics or logs to identify when a system deviates from its expected behavior.

In contrast, observability offers deeper and more actionable insights into the system's behavior and internal state. Observability emphasizes root cause analysis and understanding complex systems, making it a more holistic approach to system health and performance management than monitoring.

Common Mistakes in Implementing Observability

Implementing observability is not easy. It comes with challenges, and certain mistakes are bound to happen. Some of these mistakes include:

  1. Not Having Clear Objectives

    Implementing observability without having a strong grasp of what you want to achieve can result in mindlessly collecting irrelevant data. It can also lead to missing out on vital metrics.

  2. Lack of Context

    Collecting observability data without sufficient context can make the process challenging. Data without context is difficult to interpret. Observability data must be correlated with other data points to provide actionable insights.

  3. Failure to Establish Alerts

    Failure to set up alerts effectively can often lead to over-alerting or missed critical incidents. How alerting works in a system should be refined regularly to ensure teams are receiving meaningful and relevant notifications.

  4. Cluttered Dashboard

    Observability dashboards that are cluttered and non-intuitive can affect your insights. Designing dashboards should bear the end user in mind and focus on achieving valuable insights.

  5. Lack of Documentation

    Failing to provide the necessary documentation on how the tools were utilized can lead to misuse of the observability stack. This mistake can also affect the system’s security and data collection capabilities.

  6. Ignoring Cost Management

    Observability can incur high costs. Collecting, storing, and processing large volumes of data is expensive. Failing to set boundaries in implementing observability can often lead to exceeding the budget.

Best Practices for Avoiding Observability Mistakes

Avoiding observability mistakes is crucial for maintaining robust, efficient, and scalable systems. Here are a few best practices that can help you sidestep the common errors in the observability process:

  1. Implement Comprehensive Documentation

    Always implement thorough documentation of your observability process from the beginning. This includes detailed information on the metrics, logs, and traces for the vital components. Document the specific tools used for monitoring, their configuration, and any custom scripts or dashboards created. Also, make sure to execute the documentation from the onset so it is easier to identify issues before they escalate.

  2. Set Meaningful Alerts and Thresholds

    Set alerts based on significant thresholds that are indicative of real issues. Use past data and trends to configure and adjust the thresholds according to the system’s normal behavior.

  3. Focus on High-Value Signals

    When implementing observability, it is easy to get carried away by the massive volume of collected data. However, make sure to evaluate and focus on signals that are critical to business operations. Doing so would help diagnose issues faster and enhance the overall monitoring process.

  4. Make Sure Observability Data is Accessible

    Access to the observability data should not be limited to DevOps teams only. It should also be available for the relevant stakeholders for transparency. The observability data must be understandable for everyone.

  5. Evaluate Your Observability Strategy Regularly

    Systems grow and evolve regularly, and your observability practices must always adapt to this constant evolution. Continuously review the effectiveness of your tools, the relevance of your alerts, and the efficiency of the overall process.


Observability is essential for understanding and managing complex systems. However, mistakes usually happen in the process. These missteps include not having clear objectives, overlooking the importance of context, creating cluttered dashboards, and neglecting documentation.

However, organizations can improve system performance and reliability by avoiding those mistakes and implementing observability efficiently. It solves issues faster, supports better decision-making, and promotes continuous improvement.

FAQs on the Most Common Observability Mistake

What are the building blocks of observability?

The building blocks of observability are metrics, logs, and traces. Metrics measure KPIs, logs record system events, and traces outline the path of requests through a system.

Does observability imply controllability?

Observability does not imply controllability. A system can be observable and allow internal state inference without being controllable. However, not all states are reachable by external inputs, and vice versa.


Riley Peronto

Director of Product Marketing

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