Observability

Observability refers to the ability to infer the internal state of IT systems from their outputs, above all from logs, metrics, and traces. This article explains these data types and the difference from traditional monitoring, and shows why observability is gaining importance for network operations and security analysis.

'The system is slow' is one of the most expensive sentences in IT operations, because the cause can lie somewhere in the interplay of applications, services, infrastructure, and network. Classic monitoring reliably reports that something is wrong. The question of why it is wrong often remains open. Observability starts exactly there: systems are instrumented so that even unexpected problems can be investigated directly from their telemetry data.

What is observability?

The term comes from control engineering and describes how well the internal state of a system can be inferred from its outputs. Transferred to IT, this means: applications and infrastructure continuously deliver telemetry that is collected centrally and made queryable. The three most important data types are logs, metrics, and traces, supplemented by events and metadata. What matters is less the volume of data than its linkability: only when a conspicuous metric can be assigned to the corresponding traces and log entries does data become a reliable finding. Observability is therefore a property of systems and ways of working, not a single tool that could simply be installed. It emerges through clean instrumentation and a way of working that bases decisions on measured values. A widespread misconception is therefore wanting to establish observability solely by purchasing a platform: without instrumented systems, every platform stays blind.

How it works

Logs: Time-stamped event records capture what a system did at a particular moment. They provide the detailed context for analyzing individual processes.
Metrics: Numerical time series such as latency and error rate show trends and deviations. They are suited to alerting and capacity planning.
Traces: Distributed request tracing shows the path of an individual request across services and systems, including the time each step consumes.
Correlation: Uniform identifiers and metadata connect the data types with one another, so that you can navigate from symptom to cause.
Instrumentation: Applications and infrastructure emit telemetry via open standards such as OpenTelemetry. This reduces lock-in to individual analysis tools.
Analysis: Exploratory queries and anomaly detection turn raw data into reliable answers to questions that no one had formulated beforehand.

Why it matters

Shorter fault analyses: when symptom and cause can be linked, the time to resolution drops considerably.
Unknown failure patterns: distributed systems fail in ways that no one anticipated as a threshold. Observability allows questions that only arise during the incident.
Basis for security analysis: the same telemetry that explains operations makes unusual access and communication patterns visible and supports investigation after incidents.
Objective service quality: measured response times per application replace gut feeling in conversations with business units and service providers.
Well-founded planning: trends from metrics make capacity and investment decisions demonstrable instead of estimated.
Fewer escalations: teams clarify causes using shared data instead of shifting responsibility back and forth between the network and application sides.

Typical deployment scenarios

The benefit shows wherever several components are involved in one result:

Distributed applications: in microservice architectures, tracing shows which service slows down a call chain and where waiting times accumulate.
Hybrid networks: telemetry from sites and WAN links shows whether a fault lies in the application or on the transport path.
Cloud migrations: comparisons before and after the move demonstrate how response times develop as a result of changed data paths.
Security operations: after an incident, historical telemetry answers when which access took place and which systems were involved.

Observability vs. monitoring

Monitoring checks known variables against defined thresholds: is the service reachable and does it respond in time? This remains indispensable, but covers exclusively failure patterns that were known in advance. Observability extends this approach with exploratory analysis: the telemetry is so rich and linked that even new, never-before-seen problems can be investigated without first having to build another check. Put simply, monitoring answers the question of whether something is broken. Observability answers the question of why. Anyone who builds observability gets functioning monitoring as a partial result. The reverse does not hold, because a collection of individual checks can hardly be combined into a coherent picture after the fact. The terms are therefore by no means mutually exclusive; they describe maturity levels of the same task.

KAEMI as your partner

Reliable network operations emerge from visibility. KAEMI operates corporate networks with end-to-end telemetry, from the site connection to the individual application session. In SD-WAN environments , we measure per application and transport path how latency and packet loss develop, and assign faults to the correct link segment before they escalate. In the Software-Defined LAN , access ports and device profiles provide the data basis for operations and security analysis at the site. We share this transparency with our customers, as regular reporting and as a shared factual basis during faults. If you would like to bring more visibility into your network operations, get in touch with us via the contact page .

Frequently asked questions about Observability

What is the difference between observability and monitoring?

Monitoring watches predefined metrics and raises an alarm when thresholds are violated. It thus detects only problems that someone anticipated. Observability collects rich, linked telemetry and thereby makes systems investigable even for new, unexpected questions. Monitoring reports that something is broken. Observability helps to understand why, and is thus the more comprehensive discipline.

What role do logs, metrics, and traces play?

Metrics show, as time series, that something is changing, such as rising latency. Traces show where in a call chain the time is lost. Logs provide the detailed context for the individual process. The three data types unfold their value only together: linked via shared identifiers, they lead from the anomaly through the affected service to the concrete cause.

Is observability relevant for networks too?

Yes, increasingly. Modern networks deliver telemetry on transport paths and application quality per connection, for example in SD-WAN environments. This makes it possible to demonstrate whether a fault lies on the transmission path or in the application. Especially in hybrid architectures with cloud components, this assignment is decisive, because responsibilities can otherwise hardly be clarified. Network telemetry therefore belongs in every observability strategy.

What does observability have to do with security?

Security analysis lives on the same data as operations, above all logs and connection data. A good observability basis shortens the investigation of incidents, because access and communication paths can be traced retrospectively. Unusual patterns are noticed earlier, and reporting duties can be served with reliable facts. Anyone who collects telemetry in a disciplined way strengthens operations and security at the same time.

How do you get started with observability?

It starts with the questions the system is supposed to answer, for example on the availability of critical business processes. This is followed by an inventory of the existing telemetry and its gaps. Open standards such as OpenTelemetry help to capture data uniformly and avoid tool lock-in. A limited start with one important service, whose insights guide the further build-out, makes sense.