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Start with the outcomes the platform must protect
List the important user journeys and business outcomes the platform supports before deciding which telemetry to collect. For each, state what a successful result means and how the team will recognize it. A server returning a response is not sufficient if the user cannot complete the task or receive a useful result.
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AWS recommends aligning application telemetry and key performance indicators with business results, and considering application behavior alongside user experience and dependencies. Its observability guidance also treats telemetry as a foundation for investigating operational behavior. These principles apply regardless of which monitoring products a team uses.
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Check whether reliability measures reflect user experience
For each important journey, identify a service-level indicator (SLI)—the measurement used to describe reliability—and its service-level objective (SLO), the reliability target communicated to the organization. The SLI should represent behavior users care about, not merely an infrastructure condition such as host availability. OpenTelemetry’s observability primer frames reliability around whether a service does what users expect, rather than whether it is simply reachable.
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Then examine where each measure begins and ends. A service can report success while a client-side failure, a broken dependency, or an incomplete asynchronous action prevents the user from getting the intended result. Google’s product-focused SRE guidance distinguishes service, client-side, and end-to-end SLOs and cautions that service success does not always mean a useful user outcome.
- Service coverage: Does the SLI capture the service behavior that matters to the journey?
- Client coverage: Could web or mobile behavior cause failure that the service-side measure misses?
- Asynchronous coverage: Does success depend on work that completes after the initial response?
- End-to-end coverage: Where a journey crosses multiple components, is there a measure of the result users actually receive?
Add client-side or end-to-end SLOs where they close a real gap. Their purpose is not to multiply targets, but to reveal important failures that service-level measures cannot see.
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Verify that signals cover the systems and connect during investigation
Inventory telemetry for the services and dependencies involved in priority journeys. Metrics summarize numeric behavior over time; logs record timestamped messages and are not necessarily associated with a particular request; traces follow a request through spans across services. These signals are complementary: together they help responders move from a broad symptom to the events and request path that explain it.
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For each critical component, ask whether responders can get from an alert or user symptom to the relevant metric, log entries, trace, and dependency evidence without adding instrumentation during the incident. OpenTelemetry explains these signal types and their role in understanding system behavior in its observability primer.
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AWS recommends identifying the data needed for observability, standardizing its collection, and examining application, user-experience, dependency, and trace data. Its examples include CloudWatch and X-Ray; those are AWS-specific examples, not a neutral comparison or endorsement of observability vendors. See AWS’s implementation guidance.
Test alerts, dashboards, and response ownership
Review alerts one by one. An alert should identify an outcome at risk or a condition that requires action, and responders should know who owns it and what response is expected. Thresholds that produce frequent false positives make it harder to distinguish urgent problems from noise. AWS recommends actionable alerts and dashboards, and baselines and thresholds that teams actively review in its workload observability guidance.
- Can the on-call responder tell what user or business outcome may be affected?
- Is there a clear owner and a defined response?
- Do thresholds reflect an actionable condition rather than routine variation?
- Does the dashboard show information suited to its audience and help interpret related metrics, logs, and traces together?
If an alert cannot prompt a clear decision or investigation, revisit its threshold, routing, or purpose. A dashboard is useful when it helps its intended audience understand what is happening and decide what to do; adding panels alone does not establish that it serves that purpose.
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Observability scope can fall out of step with the system it describes. Revisit monitoring when architecture or business priorities change, during operational readiness reviews, and after significant incidents or changes. AWS’s reliability guidance on reviewing monitoring scope and metrics calls for regular review rather than treating monitoring as a one-time setup.
- Look for stale metrics or thresholds that no longer reflect current behavior.
- Find unmonitored components and gaps in client, dependency, or end-to-end coverage.
- Check for false-positive alerts and signals that responders cannot act on.
- Identify reliance on default metrics or technical measures disconnected from business outcomes.
A review should lead to concrete changes: a missing measure, improved instrumentation, a corrected threshold, clearer ownership, or a decision that existing coverage is sufficient for a stated outcome. The Google SRE Workbook’s monitoring chapter points readers to Site Reliability Engineering: How Google Runs Production Systems for foundational monitoring guidance.
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