Most CTOs track Mean Time to Recovery as a reliability metric—uptime dashboards, incident post-mortems, SLA reports showing how fast you bounce back when things break.
But when your last production issue took 4 hours to diagnose instead of 30 minutes, you weren’t just dealing with a monitoring gap. You were dealing with technical debt that’s accumulated in how your systems are structured, how your teams coordinate, and how much knowledge exists only in a few people’s heads.
Mid-market companies see MTTR run 30-40% longer than enterprises with dedicated SRE teams. That gap is about accumulated complexity.
In this edition, I’ll show you where slow incident response reveals technical debt, and what teams are doing to cut MTTR in half.
Your incident calls keep getting longer and involving more people
Look at your last five production incidents. How many people were on those calls? If the number grew from 2-3 to 6-8, your systems got more complex, not your incidents.
The person who built the service isn’t on-call anymore, and the new engineer needs three other teams to diagnose it. One Forrester study found incidents where “20 people were on a call for 6 hours” just to identify which component failed.
When a diagnosis requires a conference call instead of a Slack message, your service boundaries don’t align with your team structure. Every extra person on that call represents a dependency that shouldn’t exist.
So what can you do?
- Track incident call size as a metric; if growing, your architecture is fragmenting
- A map that services teams can diagnose independently, versus those that require coordination
- Prioritize decoupling services that consistently need cross-team calls to debug
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You added more monitoring tools, but MTTR went up
Adding another observability tool should speed up diagnosis, but it often has the opposite effect. Engineers spend 30 minutes per incident figuring out where to look: the error might be in Splunk, the latency spike in Datadog, and the logs in Azure Monitor, if anyone remembers the credentials.
Over half of companies use 6+ observability tools, with 11% using 16 or more. The problem is correlation. Switching between platforms adds 30-60 minutes in pure navigation time, manually stitching together what should be unified.
One $250M software firm consolidated to a single platform and cut missed SLA incidents by 90%, finding root causes “in minutes instead of hours.” Another saw MTTR drop 50% by eliminating handoffs between systems.
What you can do:
- Audit your stack, more than three tools creates coordination tax
- Measure time-to-first-signal: over 5 minutes means fragmentation is the bottleneck
- Consolidate before adding another specialized tool
Your fastest incidents are the ones your senior engineers already fixed before
When you look at incident resolution times, there’s usually a pattern: certain engineers close tickets in 20 minutes while others take 3 hours for the same problem. Most teams call this experience.
The real issue is that knowledge about how your systems fail exists only with those engineers, not in your runbooks or monitoring setup. When they’re unavailable—on vacation, in meetings, or gone to another company—your MTTR doubles because nobody else knows the workaround for that database leak or which config prevents the cache bug.
Deloitte found 78% of developers felt time on undocumented systems hurt morale and led to turnover. Half of organizations have incomplete incident playbooks, and teams without runbooks take 2-3× longer to resolve standard issues.
So what can you do?
- Document fixes after resolution; 15 minutes updating runbooks prevents hours later
- Identify systems only 1-2 people can debug; those are dependencies on people
- Make runbook creation part of shipping new services
When one service goes down, three others break too
A database timeout in your payment service shouldn’t cause your inventory API to fail, but it does. A memory leak in authentication shouldn’t cause notifications to crash, but it does. When one component breaks and takes three others with it, you’re debugging four systems instead of one.
Cloud outages rose to 27% of total in 2024, often cascading via dependencies. The difference is intentional decoupling. One retailer split a shared database into service-specific instances and saw multi-system failures drop 70%, cutting average incident duration by 2 hours.
When failures cascade, your team spends more time tracing dependencies than fixing the root cause.
So what can you do?
- Audit your last 10 incidents. If more than 3 affected multiple services, you have a coupling problem
- Design circuit breakers and fallbacks into high-dependency services
- Prioritize decoupling services that fail most frequently
Fast incident response is about reducing coordination overhead, tribal knowledge, and coupling that turns outages into multi-hour investigations. Teams that cut MTTR in half fix the systems, making diagnosis harder.
For mid-market companies, slow MTTR consumes 20-30% of senior capacity, burns on-call engineers, and signals fragile infrastructure. When competitors diagnose in 30 minutes while you take 3 hours, they have a structural advantage.
