What is Observability?

September 14, 2022

A robot demonstrating how observability helps developers find issues in applications

In a nutshell, observability allows you to understand the what, how, and why a malfunction occurs within your application by tracking and processing telemetry data. Gone are the days of monitoring what applications looked like weeks before a problem occurred. With the rise of observability, DevOps and SRE teams can observe their distributed system in real-time, allowing them to solve an issue before it affects a large portion of users. 

This is the perfect guide for you if you’re a new developer or someone getting started with observability.  This guide will cover what observability is, why it’s important, its benefits, and everything you need to know to implement it into cloud-native environments.

What Is Observability?

Definition of observability

Observability is having the tooling and processes in place to ask arbitrary questions about your environment without—and this is the key component—having to know what you want to ask ahead of time. The term observability has its roots in mathematics and, more specifically, in control theory. A system is considered “observable if and only if the value of the initial state can be determined from the system output.” 

Observability has duality with controllability; “a system is called controllable if and only if the system states can be changed by changing the system input” (Kalman, 1960, 1963). Observability in information systems is the ability to comprehend and determine the behavior of an entire software system from the system’s output. 

Observability is a new, rapidly evolving discipline for DevOps and Software Engineering teams. Compared to traditional monitoring tools, observability allows stakeholders to explore properties and patterns without gathering predefined data sets. As a result, companies embracing observability can extend their current monitoring solutions by centralizing data that comes from various sources and not just their application’s technology stack. 

Observability allows them to interrogate application performance and business-related data freely. By contextualizing it, they can quickly find and isolate issues that truly impact the business, boost their end-user satisfaction and improve their time to market by focusing on innovation.

What is The Difference Between Observability and Monitoring?

It can be argued that monitoring is a subset of observability. Monitoring is a step towards fully scaled observability across your platform. Monitoring and application performance monitoring (APM) are often used interchangeably.

Observability uses telemetry data (logs, metrics, and traces) to maintain application health and understand the why behind application errors. Together they use cues from your application’s external outputs to gain key insight into the health of internal states. 

Here is a breakdown of the difference between observability and monitoring:

  • Monitoring is the process of using pre-configured telemetry data with dashboards and alerts to understand application health and system performance.
  • Observability is the ability to understand the inner state of your evolving complex systems by analyzing all available outputs in real-time.

To learn more, check out our observability vs monitoring guide to find a more in-depth perspective on this topic.

What Are The Components of Observability?

Vendiagram showing metrics, traces, and logs make up observability

Observability is composed of metrics, logs, and distributed traces. These three components are also known as the three pillars of observability. Collecting this data is an essential first step in the observability process, but it doesn’t give you the complete picture. 

Here is an overview of the telemetry data collected:

  • Metrics: Metrics are numerical data measurements representing an application’s health.  Metrics are optimized for retrieval, storage, and processing. This makes it simpler to query and allows your application to analyze data sources over extended periods of time.
  • Logs: A log is a timestamp record of events occurring within an application’s system. Typically metrics show the first sign of an error, but logs give you context to understand why the problem is happening and how it’s impacting operations. Logs give better insight into debugging complex distributed systems than percentiles or averages alone. 
  • Traces: Traces show the end-to-end journey of requests in your system through a series of related distributed events. As requests move from node to node, it is called a span. SREs and software engineers use traces to conceptualize the structure and path of a request.
  • RUM: RUM collects information from application users to identify hidden bugs or problems throughout your code. RUM is short for real user monitoring.

Now that you have this information, it’s time to use it to create successful business outcomes and improve customer experience. Organizations are taking things a step further by adopting additional platforms to organize their backend information to improve software health. 

Utilizing automation, OpenTelemetry, and synthetic monitoring are some of the main ways people enhance their observability capabilities. These additions streamline solutions for IT, SRE, and DevOps teams faster and easier than ever despite the increased complexity of digital environments.

Why Is Observability Important?

Modern-day enterprise environments are becoming more complex and distributed, which makes it harder to maintain application health. Cloud-native microservices have multiple moving parts making it easier for errors to occur and more difficult to detect the issues. Thankfully, observability gives you actionable insight into what problems are happening before they affect your user experience. 

As environments switched from simple IT systems to running on Kubernetes clusters, issues either became unmonitored or undiscovered. Observability makes it possible to identify the “unknown unknowns” that developers used to struggle to find. 

Monitoring requires “known unknowns,” which told SREs and DevOps professionals that there was a problem but didn’t explain why the error occurred. The added layer of observability saves organizations time and helps them easily maintain software health. 

As technology progresses, AI and automation are becoming increasingly important. AIOps are getting integrated into modern applications to streamline and automate actions that used to take extensive manpower. 

Utilizing this feature to collect telemetry data more accurately will help you execute testing, monitoring, and continuous delivery more easily. It will also give you a broader understanding of what is happening across your full technology stack.

What Are The Benefits of Observability?

Benefits of Observability

IT, SRE, developer, and operations teams can gain major benefits by implementing observability into their application. Improve your end user’s experience and increase productivity by collecting telemetry data to solve common issues in your cloud-native environments.

Here are the significant benefits of observability:

  • Increases Visibility: Clear visibility of applications has become increasingly difficult as organizations have leaned into having complex distributed systems. Observability gives you the necessary visibility to solve problems faster before affecting your customer. Increasing the end-user experience can increase revenue, improve customer loyalty, and optimizes their process. 
  • Enhances Debugging:  Observable systems allow developers to track requests from start to finish with contextualized data along the way. This additional information along the user’s journey enables IT specialists to fix and debug problems more quickly when a failure occurs in your system.
  • Improves User Experience: Developers can catch latency occurring within their distributed services faster than ever, thanks to observability platforms like Instana. The ability to do so makes your users’ experience better and in turn, can improve your company’s reputation and lead to repeat customers. 
  • Upgrades Alerting: Observability helps find the most relevant performance issues faster than ever with notification alerts. These alerts help IT professionals troubleshoot, reduce unnecessary noise, and find the root of any issue in a shorter timeframe than before. 
  • Optimizes Business Strategy: Analyze full-stack analytics and data in real-time to improve organization plans and accelerate conversion rates. Understanding the impacts of different IT releases will help give you contextual data to know if you’re achieving your business goals.

Waste less time trying to find the root cause of application errors and improve your software health with observability. 

How Do You Make a System Observable?

Now that you have an in-depth understanding of what observability is and how it can benefit your business, you’re probably wondering how can I make my system observable.

 Achieving observability is much more than just collecting telemetry data from your digital system. You need the proper tools in place to source the data you need and add additional context to find solutions to errors. 

Below are the five fundamental components of implementing observability:

  • Instrumentation: Instrumentation tools use telemetry data from open source or vendor-specific platforms to give you visibility over your infrastructure. 
  • Distributed Tracing: This is an essential aspect of observability because distributed tracing shows how the internal microservices of a system are interconnected and maps out each user request.
  • Incident Response: Your observability platform must have an alerting management system that informs the correct IT  team when problems arise. 
  • Data Correlation: Processing and correlating telemetry data adds the context you need to turn your data into graphs and charts. These visualizations will help your team see a complete picture of the data collected and make sense of any rises and falls during a time series. 
  • AIOps: Machine learning models automate IT operations such as aggregating, correlating, and prioritizing incident data. AIOps tools ultimately help to accelerate incident response and improve mean time to prevention (MTTR). 

Streamline Your Workflow With Instana’s Observability Tools

Instana’s AI-powered observability platform offers you the capabilities needed to navigate complex cloud environments. Our features will help you streamline workflows, assist your team in making better business decisions, and improve the end-user experience. Try Instana’s 14-day free trial to start observing your system today.


  • Kalman, R. E. (1960) ‘On the general theory of control systems’, IFAC Proceedings Volumes, 1(1), pp. 491–502. doi: https://doi.org/10.1016/S1474-6670(17)70094-8.
  • Kalman, R. E. (1963) ‘Mathematical Description of Linear Dynamical Systems’, J.S.I.A.M Control, 1(2), pp. 152–192. doi: 10.2307/j.ctvcm4h1q.10.

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Instana, an IBM company, provides an Enterprise Observability Platform with automated application monitoring capabilities to businesses operating complex, modern, cloud-native applications no matter where they reside – on-premises or in public and private clouds, including mobile devices or IBM Z.

Control hybrid modern applications with Instana’s AI-powered discovery of deep contextual dependencies inside hybrid applications. Instana also gives visibility into development pipelines to help enable closed-loop DevOps automation.

This provides actionable feedback needed for clients as they to optimize application performance, enable innovation and mitigate risk, helping Dev+Ops add value and efficiency to software delivery pipelines while meeting their service and business level objectives.

For further information, please visit instana.com.