In IT, “observability” refers to the ability to comprehensively understand and analyze a system’s internal state and behavior based on its output data (e.g. logs, metrics, and traces). Unlike monitoring, which relies on predefined metrics and alerts, observability enables the discovery of unknown issues through deeper, correlation-based analysis of system behavior — examining logs, metrics, and traces together.
This article explains the essentials of observability in plain terms.
Monitoring and Observability – What Is the Real Difference?
Monitoring tools (Zabbix, Nagios, Grafana, and others) have been a foundational part of IT for decades. They report when a server goes down, when response times spike, when a service becomes unavailable. To do this, however, they require upfront configuration: which metrics to collect, which thresholds should trigger alerts.
This approach worked well as long as systems remained relatively straightforward.
Observability, by contrast, requires significantly less upfront configuration to show what broke and where. This also makes it easier to set up and customize – and it delivers a more accurate picture than traditional monitoring.
It achieves this by processing three data types simultaneously: metrics, logs, and traces. Each provides a different perspective on the same system. Together, they surface correlations that none of them could reveal in isolation.
If monitoring gives you GPS coordinates, observability gives you the full map.
The Concept of Observability
The term was introduced by Hungarian-American engineer Rudolf E. Kálmán in control theory – where it means precisely this: how well the internal state of a system can be estimated based solely on its external outputs.
IT adopted that idea directly. In an IT context, observability is a property of a system that reveals what is happening internally – based on externally measurable data. It makes it possible to understand system state during live production, and to quickly determine why something is not working as expected.
Control theory also provides another important insight: observability and controllability are paired concepts. You can only control what you can see. Tamás Darabos, Deputy CEO of Telvice, explores this relationship in depth.
Telvice’s own definition: Observability is the continuous self-diagnostic capability of a digital enterprise ecosystem. It signals immediately when stability is at risk, and reveals the full causal context of any issue – including precisely where intervention is needed.
How Does It Work?
A physician is not satisfied with knowing that a patient has a fever. They want to understand the cause – so they order bloodwork, imaging, and further tests. They do not treat an isolated symptom. They look for connections.
Observability operates the same way. The system reads from three sources simultaneously: metrics, which show how the system is performing; logs, which record what happened and when; and traces, which follow the path of a request as it moves through the different parts of the system. Each of these data types is useful on its own. Together, however, they reveal something that none could show individually: the complete causal picture.
This intersection with automated response capabilities is explored further in our article on AIOps and observability.
When Is It Needed?
There are three situations where the need for observability becomes most acute.
The first: when no single person in the organization has end-to-end visibility across the IT environment. Modern enterprise systems have grown so complex that this is the reality for nearly every organization with more than 500 employees. Achieving predictable IT operations requires this level of transparency.
The second: when disruptions or slowdowns in digital customer-facing services – or other critical processes – carry direct business consequences. In these situations, every minute lost matters. Observability helps not only in finding faults faster, but in enabling the organization to innovate with greater confidence.
The third: when the costs of digital operations become opaque. It is no longer clear how much each system actually costs to run, where resources are being drained unnecessarily, or where optimization would be worthwhile. The relationship between observability and OPEX reduction is addressed in a dedicated article.
How Does Implementation Work?
Most organizations already have monitoring tools in place – and data being generated. The question is whether that data exists in fragmented, isolated silos, or whether it is visible in one place, in context. The logic of implementation is therefore straightforward: first, consolidate what already exists. Then connect it. Then place it in a business context. At that point, it becomes possible to see which business process a technical signal affects, which service it is slowing down, and where intervention is required.
This is where the Single Source of Truth model and business observability converge: technical data becomes the foundation for business decisions.
A range of platforms support this today. Telvice works with the two market-leading solutions: Dynatrace and Datadog.
For a deeper dive, listen to our podcast episode – where these questions are explored over 35 minutes from a practical perspective.
