How Companies Detect and Troubleshoot System Issues
Even if we don’t like to admit that, we rely heavily on digital systems. Our businesses depend on every hardware and software working properly. Failures and downtime result in financial losses, loss of staff productivity, and damage to our reputation.
Despite that, most companies don’t have a real-time view of what is happening inside their systems.
This is particularly true of on-premises or hybrid environments, where visibility is limited and tooling has developed organically over time. The first sign that something has gone wrong is often when someone reports a problem. By then, the damage is already done.
The worst scenario is when we don’t monitor anything and just wait for something to break down. Then, when it does, the whole team springs into action to put out the fire. Even when the whole issue could have been prevented earlier.
How to set a new, better course for greater security? Let’s take a look at the challenges and different approaches.
Proactive vs reactive approach
For years, monitoring has been the standard way to keep systems under control.The teams focused on creating indicators that defined the health of the system. If the metric exceeded the specified limit, alerts were sent.
This approach is built on prediction. Engineers try to anticipate how a system might fail, decide when alerts look dangerous, and then wait for alarms to go off. Alerts are collected on dashboards and handled by separate teams. Teams adjust them, respond to them, but it is still a reactive approach.
Monitoring works well for simple, stable, monolith systems. The problem is that modern systems are neither simple nor stable. Monitoring is based on known failure modes. Teams are trying to predict every possible way a system might break, then watch for those specific signals. When systems grow more complex, this becomes unrealistic.
As a result, systems can be technically “healthy” according to dashboards while users experience slowness, errors, or timeouts. Monitoring is not wrong by any means. We just need to take a step forward in today’s landscape.
Observability - gaining full visibility
Observability is the ability to understand what is happening inside your systems by analyzing data outputs from infrastructure and applications. It uses the data and insights generated by monitoring to provide a holistic understanding of your system, including its health and performance. When observability works well, organizations can find problems quickly, notice when things are changing for the worse, and fix problems faster.
Instead of asking whether a predefined threshold has been exceeded, observability allows teams to ask open-ended questions about system behavior and get answers directly from real production data. We start from the point where the failure occurs and can accurately open up what was happening in our system at that moment. We no longer rely on assumptions, but can reverse engineer the event and see how changes affect performance, how individual requests behave, and how issues propagate across services.
Following the statement from the “Observability engineering” book by Charity Major “monitoring is for the known-unknowns, but observability is for the unknown-unknowns”*.
Why observability is challenging (and rewarding)
Observability is not something you can buy and switch on. It doesn’t depend that much on tools either – it’s a way of understanding systems that accepts a simple truth: modern applications are made up of countless moving parts, and the number of possible interactions between them is effectively endless.
Today’s systems rarely fail in obvious or predictable ways. A single user request may pass through multiple services, written in different languages, backed by different databases, and running on infrastructure that changes constantly. Some components are fully under your control, while others belong to external providers. When something goes wrong, the challenge is not noticing that a problem exists, but figuring out where it originates and why it behaves the way it does.
This is why observability goes beyond automatic instrumentation and predefined dashboards. While those are useful starting points, they rarely provide enough context on their own. Engineers need to understand how their code behaves in real production conditions and take ownership of making that behavior visible. Adding meaningful context to logs, traces, and events is what turns raw data into insight.
Adopting observability also changes how teams think about reliability. The goal is no longer to eliminate all failures – an impossible task in complex systems – but to notice degradation early, understand how it affects users, and respond before small issues grow into major incidents.
That shift is what makes observability challenging. It requires new habits, shared responsibility, and a willingness to explore unknowns. But it’s also what makes it so powerful: teams gain the ability to understand their systems as they actually behave, not just as they were designed on paper.
How to get started?
The first thing to do is to check the current state of your systems and verify where you are in terms of awareness.
Can you consistently answer open-ended questions about the inner workings of your applications to explain any anomalies without hitting dead ends in your investigation? And perhaps most importantly, can you keep your business running smoothly? A useful way to think about observability is through the questions it lets you answer.
For example:
- How do you typically find out about system issues?
- What data do you collect from your system?
- Can you trace a user request from frontend to database?
- Can you move from a high-level view down to a single request in seconds?
- Can you investigate completely new issues without having predicted them in advance?
And most importantly:
- Can you ask one question, get an answer quickly, and immediately ask the next – without hitting a dead end?
If the answer is “yes,” you’re moving toward real observability. If not, we can recommend getting started with our free observability assessment. It’s a short form that will give you a good starting point, detailed assessment report and custom improvement roadmap.
Our Approach
Let’s look at the real example of observability with one of our products – Observability Operations Center.
Step 1: Investigation
Working with OOC usually begins by helping organizations understand how they currently detect and investigate system problems. In hybrid and on-premises environments, visibility is often spread across many different tools. This makes it hard to see how technical issues actually impact real users.
The first step then is a focused observability assessment that identifies blind spots, investigation bottlenecks, and gaps between monitoring data and business impact.
Step 2: Connecting data sources
Based on this assessment, OOC establishes a foundation for observability using the chosen tool (Splunk Observability) and existing data sources. Metrics, logs, and traces are collected and correlated across infrastructure, applications, and integrations. Critical user and business transactions are monitored so that teams can track a single request from the front end to the back end, including any external dependencies.
Step 3: Setting behavior-based alerts
Rather than relying only on fixed alert thresholds, OOC helps teams move to behavior-based alerts that surface unusual changes and early warning signs. Monitoring data is enriched with helpful context, so teams can quickly move from “something is wrong” to understanding the root cause, without guesswork. AI insights make the entire analysis process much easier.
With this approach, organizations move away from constant firefighting and toward a proactive understanding of their systems. Problems are spotted earlier, root causes are found faster, and teams feel more confident running complex environments. With OOC, we help teams ask better questions and get clear answers from real production data.
Summary
While monitoring is fundamental to identifying system issues, observability takes this further in modern environments. It provides teams with a more detailed insight into how applications behave in production, enabling them to detect issues earlier, investigate problems more quickly, and minimise their impact on users.
If you are ready to start your observability journey, you have come to the right place! Follow us on LinkedIn to enjoy weekly observability posts.