Start with an operational question
Observability helps you understand what is happening inside a system through the data it emits. OpenTelemetry describes signals such as metrics, logs and traces. The first decision is not which tool to install, but which questions you need to answer when a user cannot complete an action.
In a ticketing system, you might ask: Was the request received? Was a notification sent? Is it waiting to be processed after a failure? A general CPU metric does not necessarily answer those questions. Start with the process the business depends on and define which states you need to see.
Measure outcome, load and time
Choose metrics that describe an operation's success or failure, response time and throughput. If there is a queue, also track how many jobs are waiting and how long the oldest one has been there. A counter of successful jobs can look healthy even when some requests have been stuck for hours.
Keep the number of metric label combinations under control. A user or request ID is not a good label for every metric. Beyond data volume, ask whether the label contains personal information and whether it is needed for the operational question you chose.
Write logs you can tie back to the process
A useful log describes an event, its outcome and enough context for diagnosis. A message like “Something failed” does not tell you which stage needs attention. A request ID can help correlate events, as long as it contains no personal information and does not replace access control on the viewing tools.
Do not log passwords, tokens or full request bodies by default. Plan access permissions and retention periods for operational data. You can record the error code and the stage where the failure occurred, and keep sensitive details in a separate, protected mechanism when they are genuinely needed.
Follow an operation as it moves between components
A trace describes a request's path across components and can help pinpoint where time was spent or which connection failed. It is especially useful when the path crosses multiple services. Check that context is preserved when work is handed off to a queue or a background worker.
There is no need to collect every detail on every request. Set a sampling policy and check that it does not hide the scenarios that matter to you. Keep the operational question in view: if a piece of data does not help with diagnosis, its cost and collection may not be justified.
Alert only when someone can act
An alert should describe a problem that has impact and an owner who can act on it. Next to each alert, write what to check, what normal looks like and what to do once the issue is confirmed. A value crossing a threshold is not necessarily an event worth waking someone up for; context decides.
Before launch, trigger a controlled failure and ask someone else to identify it using the tools and documentation. If they see an impressive dashboard but cannot figure out what to do, the data or the runbook needs work. It is a small exercise that connects monitoring to real operations.