When it comes to managing modern IT systems, troubleshooting is one of the most critical activities. It’s about identifying and resolving issues that slow down applications, disrupt services, and ultimately cost time, money, and customer trust. A well-established troubleshooting process, supported by robust observability practices, can make all the difference in diagnosing and fixing problems efficiently. Let me walk through my generic troubleshooting process and see how observability tools and data are key to each stage. The following graphic depicts the steps from panic to success.

1. Detection: Panic! – Something went wrong

The first step in troubleshooting is knowing there’s a problem in the first place. This is where alerting and detection come into play. Modern observability platforms are designed to send out alerts when something goes wrong, but what exactly should you be looking for? The symptoms are generally quite simple: things are either too slow, or they’re throwing errors. These are the two primary indicators that something isn’t working as expected. Yes, it is that simple: Slow or Error – Drop me a message when you know another symptom that does not categorize as one of those two.

Whether your application’s response times are lagging or your logs are filling up with errors, observability tools allow you to catch these symptoms early. Metrics, logs, and traces provide real-time insights into your system’s behavior, ensuring that you’re aware of potential issues before they snowball into bigger problems. That is classic monitoring and part of any APM solution over the last decades.

2. Triage: Narrowing down the problem

Once you know there’s an issue, the next step is triage. This means identifying where the problem lies and, more importantly, which team is responsible for addressing it. The triage process involves looking at the data you’ve collected to figure out in which layer of your stack the issue originates: is it a network problem, an application bug, a database issue, or a frontend malfunction? Most modern applications have a lot of services or micro-services that are calling each other to serve a customer request. An error that hits a user can originate in any of those services including the browser of the user. Triaging the area where the error started is a question of having the right data.

Observability plays a key role here, offering context-rich data like traces that help you see how various components of your system interact. Tools that provide end-to-end visibility can make it easier to see whether the issue is in the backend, frontend, or somewhere in between. By streamlining this process, you can get the right people involved faster and avoid unnecessary delays. Plus you don‘t need to wake up teammembers in the middle of the night for nothing.

3. Diagnostics: Identifying the root cause

After triage, you move into diagnostics, which is about understanding the exact nature of the problem. In this phase, it’s essential to gather detailed data, often over a period of time, to identify patterns. For example, is the system consistently slow? Is the slowness increasing over time, or does it happen randomly? There are 50 shades of slow and each type of slowdown gives clues about what might be going wrong. For example a constant increase can point to a memory leak while an up- and down movement (sawtooth pattern) can have its origin the wrong garbage collection strategy. But you need more data to follow the leads and spot the culprit.

This is where observability shines. Modern observability tools can provide both real-time and historical data, allowing you to compare current performance with past behavior. You can analyze logs for recurring error patterns, traces for performance bottlenecks, and metrics to spot anomalies. The more data you have, the better you can pinpoint the root cause. And it is important that your solution allows to view the data from various angles or perspectives. Just storing it in a data sink is not enough – you need to be able to correlate data in various ways to confirm or refute a suspicion.

4. Root Cause Detection: Confirming the issue

Once you have an idea of what might be causing the problem, it’s important to confirm it. This step often involves recreating the issue in a test environment or closely monitoring the behavior after a proposed fix. Just because something looks like the root cause doesn’t mean it is, so it’s crucial to validate your findings before moving forward. Experience and intuition are very valuable but can also lead you down a rabbit hole, that has nothing to do with the real root cause. I’ve have to admit, I’ve been there more than once and wasted valuable time chasing shadows.

Observability tools help by providing the same diagnostic data during testing as in production. This allows you to replicate real-world conditions, ensuring that your solution will actually resolve the issue when deployed in production.

5. Resolution: Applying and verifying the fix

Once you’ve identified and confirmed the root cause, the next step is to fix it. Whether this involves applying a patch, rolling out a workaround, adjust a configuration or just restarting services, observability data plays a key role in verifying the effectiveness of your solution. You’ll want to check if the same metrics, logs, or traces that indicated a problem before are now showing improvements.

After applying the fix, monitoring tools can track performance in real time to ensure the issue doesn’t reoccur. If it does, you can quickly loop back into the troubleshooting process with more data and insights than before.

6. The role of observability tools in modern troubleshooting

Modern IT systems are complex, often involving multiple microservices, databases, frontend technologies, and networks, all distributed across various environments. As systems grow more complex, so does troubleshooting. That’s why having a strong observability infrastructure in place is critical.

There are two primary categories of tools you can use: open-source solutions like Prometheus, OpenTelemetry, Grafana, and ELK, and commercial tools like Instana, Dynatrace, Datadog, and New Relic. Both have their pros and cons. Open-source solutions offer flexibility and cost advantages, but they require you to manage and store the data yourself. Commercial solutions are often easier to set up and manage, but they come at a higher cost and often lock you into specific ecosystems.

Regardless of the tools you choose, having visibility into logs, metrics, and traces is essential. These three pillars of observability work together to provide a complete view of your system’s performance, helping you detect issues earlier, triage more effectively, and diagnose root causes faster.

Summary: Observability makes troubleshooting smarter

Troubleshooting is an inevitable part of managing any modern IT environment, but with the right approach and the right tools, it doesn’t have to be painful. Observability is the key to efficient troubleshooting, giving you the data and context you need to quickly identify, diagnose, and fix issues. From detection through to resolution, observability helps teams stay on top of issues and ensure that systems are running smoothly. In a world where performance and availability are crucial, investing in observability is no longer optional—it’s essential.

If you like to review your current process and tool stack you are more than welcome to join us at the Observability Heroes Community – it’s free to join, give it a try.