What is observability? What does it mean? Why does it matter?

Observability is a bit of a buzzword in the modern-day software industry. But it’s way more than just a buzzword. Why? Because observability empowers engineers and developers to create superior customer experiences despite the increasing complexity of the digital enterprise.

Before you start thinking about observability in your software environment, it’s crucial to understand that it’s not just a fancy synonym for monitoring. In short, observability enables you to:

  • Collect, explore, alert, and correlate all telemetry data types
  • Accelerate time to market
  • Ensure uptime and performance
  • Troubleshoot and resolve issues faster
  • Gain greater operating efficiency and produce high-quality software at scale
  • Understand the real-time fluctuations of your digital business performance
  • Optimize investments
  • Build a culture of innovation

Let’s find out more about observability.

What is observability?

In control theory, observability is defined as a measure of how well internal states of a system can be inferred from knowledge of that system's external outputs. Simply put, observability is how well you can understand your complex system.

Metrics, events, logs, and traces—or MELT—are at the core of observability. But observability is about a whole lot more than just data.

The relationship between monitoring and observability

It’s not useful to conceptualize the relationship between monitoring and observability as “monitoring vs. observability.” Monitoring is a subset of observability and is a key action for observability. Here’s a little more on their distinction and relationship:

Monitoring (a verb) is symptom-oriented. It tells you that something is wrong. Observability (a noun) is a property of your approach that lets you ask why. Monitoring is predicated on knowing in advance what signals you want to monitor (these are your “known unknowns.”)

But in today’s complex, distributed systems built on hundreds if not thousands of microservices, it becomes impossible to predict all failure modes. Observability gives you the flexibility to dig into “unknown unknowns” on the fly.

How observability fits into the modern software environment

Is observability a new concept? Or just a passing buzzword? A little context will help answer these questions.

One of the key benefits of working with older technologies (for example, mainframes and static operations) was the limited set of failure modes. When things went wrong, it was pretty easy to understand why. Most older systems failed in the same few ways time and time again.

At first, monitoring tools attempted to shed light on what was happening with software performance. You could trace application performance with monitoring data and time-series analytics. It was a manageable process. But systems became more complex. Today, the possible causes of failure are abundant.

Modern-day systems are fast transforming into complex, open source, cloud-native microservices running on Kubernetes clusters. What’s more, they are being developed and deployed at lightning speed by distributed teams. With DevOps, progressive delivery, and agile development, the whole software delivery process is faster than ever before.

When working on these complex, distributed systems, identifying a broken link in the chain can be near impossible. And with the explosion of microservices architectures, every member of your software team must understand, analyze, and troubleshoot application areas they don’t necessarily own.

Building “better” applications is not the solution. Nothing is perfect. Everything fails at one point or another, whether due to code bugs, infrastructure overload, or changes in end-user behavior. The best thing developers can do is create software that is easier to fix when the inevitable occurs.

The problem is many developers cannot predict all of their software’s failure modes in advance. Often, there are simply too many possibilities, some of which are genuine unknown unknowns. You cannot fix the problem because it doesn’t even exist yet.

Conventional monitoring can’t remedy this issue. It can only track known unknowns. Following known KPIs is only as useful as the KPIs themselves. And, sometimes you track KPIs that are completely irrelevant to the problem occurring.

It all boils down to this: Your monitoring is only as effective and useful as your system is monitor-able. Observability is how you approach the monitor-able-ness of a system.

The four fundamental components of observability

Observability is the practice of instrumenting systems to secure actionable data that details when and why an error occurs. To achieve observability, you need four fundamental components:

1. Open instrumentation

Open instrumentation gathers open source or vendor-specific telemetry data from a service, host, application, container, or any other entity that produces data.

This allows for visibility over the whole surface area of critical applications and infrastructure. It also future-proofs teams as you introduce new platforms and data types into the system.

2. Correlation and context

The telemetry data collected must be analyzed so that all data sources can be connected. Metadata also needs to be incorporated to enable correlation among various parts of the system and their data. Together, these actions create context and shape meaning. This allows curation to be delivered in visual models of the system.

Find out more about observability with context in the video below:

3. Programmability

No quantity of automatic curation can meet the unique requirements of a business or cater to all its use cases. Organizations need the flexibility to create their own context and curation with custom applications based on their unique business objectives. For example, an app could help teams easily calculate and visualize the impact of errors on end-user engagement. An app could also offer a customized path to understand how to improve error rates.

4. AIOps

When you are responsible for ensuring your modern infrastructure is always available, you need tools to accelerate incident response. Unlike traditional incident management tools, AIOps solutions use machine learning models to automate IT operations processes. With AIOps, you can empower your team to correlate, aggregate, and prioritize incident data automatically. You can eliminate alert noise, proactively detect issues, and accelerate mean time to resolution (MTTR).

A future-proof observability platform

Software innovation continues to evolve at a rapid rate, requiring you to operate in increasingly complex environments. The next big trends in software development are likely completely unknown currently. Despite this, you can safely assume that you’ll be expected to move faster and embrace new technologies while ensuring a seamless end-user experience.

With these future challenges, organizations need a robust observability platform that minimizes complexity, mitigates risk, and keeps overhead low. The platform must also be easy to use and understand. It should enhance rather than inhibit a team’s understanding by displaying all critical telemetry and business data in one place. Deriving meaningful information that enables error resolution should be intuitive and straightforward.

When seeking out a future-proof observability platform, ensure that it can:

  • Gather and combine telemetry data from open and proprietary sources. This harks back to the open instrumentation component mentioned above. It allows for the interoperability of all data, regardless of the source.
  • Create connections between data types and use those connections to form contextual meaning. Context should be visualized in curated views that dynamically highlight the most critical information.
  • Allow you to build applications on top of it that deliver interactive, curated experiences. Programmability redefines the possibilities of observability, making it customizable to your unique business.
  • Leverage AIOps to detect, understand, and respond to incidents faster. Machine learning reduces alert noise and helps you find insights in the data.

Observability platform benefits

With an open, connected, and programmable observability platform, your business can experience these profound benefits:

  • Faster deployment
  • Quicker innovation
  • Less toil
  • Lower cost
  • Better resource optimization

Together, these traits equip you with a deeper understanding of your systems, the data they produce, and the customer experiences they enable. This gives your business a competitive advantage. The result is sustainable business growth.

You gain real-time analytics to understand how your digital systems are performing. Your teams will spend less time troubleshooting and more time building. Read more about why the future is open, connected, and programmable.