Monitoring tells you when a known condition occurs. Observability helps you understand why a distributed system is behaving the way it is — including failures nobody predicted. This free Cyber Tech Insights whitepaper sets out a practical, phased path for IT operations teams.
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Download PDFWhat’s inside
- Monitoring vs. observability — how they differ and why you need both.
- The signals that matter — metrics, logs, traces, events and profiles, and why correlation is where the value lies.
- Why OpenTelemetry — instrumenting once with a vendor-neutral standard to avoid lock-in.
- Alerting on SLOs — error budgets, paging on fast burn and cutting alert fatigue.
- Cost control — sampling, retention tiers and cardinality limits.
- A four-phase roadmap and an observability readiness checklist.
Who it’s for
IT operations, SRE and platform teams, and the leaders who fund their tooling.
Get the whitepaper4 pages · PDF · Cyber Tech Insights
Download PDF5 best practices to get value from observability
Tools alone do not create insight. The teams that benefit most from observability tend to follow a handful of simple, proven practices.
- Start from user journeys. Identify the few journeys that matter most, such as login, checkout or claim submission, and instrument them end to end before expanding coverage.
- Standardise instrumentation. Use open standards such as OpenTelemetry so that traces, metrics and logs share consistent names and context. This avoids lock-in and makes correlation possible across teams.
- Define service level objectives. Agree what good looks like for latency, availability and error rates, then alert when an error budget is burning too fast rather than on every threshold breach.
- Manage telemetry cost deliberately. Sample high-volume traces, drop noisy debug logs in production, set retention by data value and review the largest data sources every month.
- Close the loop with incidents. After every significant incident, ask what signal would have revealed the problem sooner and add it. Over time this builds coverage where it matters.
Monitoring versus observability
Monitoring answers questions you anticipated in advance, such as whether CPU is above 90 per cent. Observability lets engineers ask new questions about unexpected behaviour without shipping new code, because rich, correlated telemetry is already available. Both are needed: monitoring for known failure modes and observability for the unknown ones.
Common pitfalls
- Collecting everything and paying to store data nobody queries.
- Alerting on symptoms that do not affect users, leading to alert fatigue.
- Leaving observability to a central team instead of making service owners responsible for their own signals.
- Ignoring business context, so dashboards show technical health but not customer impact.
Frequently asked questions
Do we need to replace our monitoring tools?
Not necessarily. Many organisations add tracing and correlation on top of existing tools and consolidate gradually.
Where should a small team begin?
Instrument one critical service with traces, define two or three SLOs and build a single dashboard that the on-call engineer actually uses.
A 90-day action plan
Days 1 to 30: choose two customer-facing services, map their dependencies and agree what reliable service means for each in terms of latency and error rates.
Days 31 to 60: add distributed tracing to those services, connect logs and metrics through shared identifiers, and replace noisy threshold alerts with alerts tied to user impact.
Days 61 to 90: review the first incidents handled with the new signals, measure time to detect and resolve, and decide which services to onboard next based on business importance.
Questions to ask tool vendors
- How is pricing calculated, and how can we forecast spend as volumes grow?
- Do you accept open telemetry formats without proprietary agents?
- How long is data retained at each price tier, and can we export it?
- What controls exist to mask personal or sensitive data in logs?
- How do you support on-call workflows, runbooks and post-incident reviews?
Key terms explained
- Trace: the end-to-end path of a single request as it passes through services.
- Span: one timed operation within a trace, such as a database call.
- SLO: a target for reliability agreed between service owners and the business.
- Error budget: the amount of unreliability an SLO allows before work shifts to stability.
- Cardinality: the number of unique label combinations in metrics, a major driver of cost.
The bottom line
Correlated telemetry helps teams understand unexpected behaviour quickly and protect the experience of their users. Starting from critical journeys, adopting open standards, defining reliability targets, controlling cost and learning from incidents turn tools into real insight. Download the whitepaper for detailed guidance, then begin with one or two services and expand coverage based on business importance and the lessons learned from each incident.
Further reading on observability
For authoritative, vendor-neutral guidance on observability, see the OpenTelemetry project. You can also browse our free whitepapers.
