How to Discuss Observability Gaps in English
Learn the English vocabulary and phrases for flagging monitoring and observability gaps to your team and driving action to close them.
Identifying a gap in monitoring or observability is only useful if you can communicate it clearly enough for the team to prioritize fixing it. Engineers often discover these gaps during incidents — “we had no visibility into this” — and need the right language to explain the risk without sounding alarmist or vague. This post covers the vocabulary for describing missing metrics, alerts, and traces, and for making the case that closing the gap deserves engineering time.
Key Vocabulary
Blind spot — an area of the system with no meaningful monitoring, meaning issues there go undetected until they cause visible impact elsewhere. “The message queue is a blind spot — we have no metrics on queue depth or processing lag.”
Alerting coverage — the extent to which critical failure conditions are configured to trigger automated alerts, used to describe how well-monitored a system is. “Alerting coverage on the payment service is strong, but the reconciliation job has none at all.”
Signal-to-noise ratio — a measure of how many alerts are genuinely actionable versus how many are false positives or low-value, affecting whether engineers trust and respond to alerts. “Our signal-to-noise ratio is poor right now — half of on-call pages this month turned out to be non-issues.”
Distributed tracing — a method of tracking a single request as it moves across multiple services, used to diagnose latency and failures in complex systems. “Without distributed tracing, we couldn’t tell which of the six services in the request path was actually slow.”
Golden signals — the four key metrics (latency, traffic, errors, saturation) commonly used as the minimum baseline for monitoring any service. “We track the golden signals for every service except the batch processor, which is our biggest gap right now.”
Mean time to detect (MTTD) — the average time it takes to become aware that an incident is occurring, a key metric for evaluating observability effectiveness. “Our MTTD for this outage was 40 minutes, which tells us the alerting gap directly extended the incident.”
Root cause visibility — the ability to trace an incident back to its underlying cause using available logs, metrics, and traces, rather than relying on guesswork. “We restored service quickly, but root cause visibility was poor — we still don’t fully know why it happened.”
Common Phrases
- “We have no alerting on this failure mode, which means we’d only find out from customer reports.”
- “This is a blind spot we should close before it causes an incident, not after.”
- “Our MTTD on this class of issue is too high given how critical the service is.”
- “I’d like to propose adding golden signal dashboards for the services that currently lack them.”
- “The alert fatigue on this team is a symptom of poor signal-to-noise ratio, not too much monitoring.”
- “We only found this issue because someone happened to be looking at logs manually.”
Example Sentences
Flagging a gap discovered during an incident: “During yesterday’s incident, we realized we have zero alerting on queue depth for the email worker. It backed up silently for 45 minutes before anyone noticed, and we only caught it because a customer reported delayed emails. I’d like to prioritize adding this alert this sprint.”
Making the case for investment in observability: “We’ve had three incidents this quarter where the root cause was hard to identify because we lack distributed tracing across the checkout flow. I’d estimate this cost us an extra 20-30 minutes of MTTD each time. I think it’s worth allocating a sprint to close this gap.”
Following up after closing a gap: “Just a heads-up that we’ve added the missing alerting on the reconciliation job discussed last week. It now pages on-call if the job hasn’t completed successfully within its expected window, closing the blind spot we identified after the September incident.”
Professional Tips
- Tie observability gaps to a concrete incident or cost (“this extended MTTD by 20 minutes”) rather than an abstract concern — it makes the case for prioritization much stronger.
- Use “blind spot” deliberately for areas with zero visibility, and reserve “alert fatigue” for cases where there’s too much low-value noise — the two problems require different fixes.
- Propose the golden signals framework as a minimum bar when arguing a service is under-monitored — it gives the team a concrete, well-known standard to aim for.
- Frame closing observability gaps as reducing future incident duration, not just “nice to have” tooling work — this connects it to reliability goals stakeholders care about.
Practice Exercise
- Write a message flagging an observability gap discovered during a recent incident, including its impact.
- Draft two sentences making the case for investing engineering time in closing an alerting gap.
- Explain, in two sentences, the difference between “alert fatigue” and a “blind spot.”
Navigating Discrepancies with Precision
Let’s be honest – discussing “gaps” in observability can feel awkward. It’s not about pointing fingers, but rather collaboratively identifying areas where our monitoring isn’t providing the full picture. The key is phrasing your concerns clearly and constructively, utilizing specific vocabulary that demonstrates you understand the underlying issue and are focused on a solution. A vague statement like “We don’t have enough data” won’t cut it in a professional setting. Instead, focus on what isn’t being seen and why it matters.
Consider this scenario: During a code review for a new microservice, you notice the team hasn’t set up any metrics around latency spikes. You could simply say, “We should add some monitoring here.” That’s too general. A more effective approach would be, “I noticed we haven’t implemented any latency tracking for this service. Given the potential impact of slowdowns on user experience, proactively monitoring latency – perhaps using Prometheus with a simple rate counter – would provide valuable insight and allow us to quickly identify and address performance regressions.” Notice the use of precise terms like “latency tracking,” “rate counter,” and referencing potential impact (“user experience”).
Another common situation arises in Slack when discussing a recent production incident. Instead of saying, “Something’s wrong with the monitoring!” try something more targeted: “I’m seeing that our alert thresholds for CPU utilization on Server B haven’t been configured yet. Without this, we won’t be alerted to sustained high usage which could lead to performance issues. Perhaps a quick investigation into setting up a dynamic threshold based on historical data would be beneficial?” This demonstrates you understand the potential consequences and propose a specific corrective action – establishing a dynamic threshold. Focusing on why something is missing and suggesting a tangible solution will always be more productive than simply stating a problem.
Finally, when writing PR descriptions outlining monitoring changes, avoid jargon like “observability stack.” Instead, clearly state the purpose: “This PR adds Prometheus metrics to track request latency and error rates for Service X. These metrics will allow us to proactively identify performance bottlenecks and improve overall system stability.” The goal is clarity – ensure your team understands exactly what you’ve added and why it’s important. Remember, effective communication isn’t just about saying something; it’s about conveying understanding and driving action.