5 Reasons Root Cause Analysis Takes So Long

When it comes to incident response, SREs with years of experience know one truth: speed matters. The faster a root cause is identified, the sooner systems can recover, reducing downtime and stakeholder impact. Root Cause Analysis (RCA) isn’t just a technical process; it’s the backbone of resilient systems and user trust.

But RCA is rarely straightforward. As SREs are navigating countless production incidents and firefights, we’ve all noticed how easily delays creep in. Whether it's sifting through fragmented dashboards, fighting with noisy alerts, or coordinating across silos, these challenges turn even well-oiled teams into bottlenecks.

This post distills five hard-learned lessons on why Root Cause Analysis takes too long, along with actionable advice honed from years of running production systems at scale. If you’re an SRE, DevOps engineer, or incident management lead, this is your playbook for cutting RCA timelines and improving system reliability.

1. Dashboard Overload and Fragmentation

Why it happens: Seasoned SREs know the "dashboard shuffle" all too well. In the middle of an incident, engineers waste precious time toggling between tools—Grafana for metrics, Splunk for logs, New Relic for APM data—all while trying to piece together a coherent picture.

What experts do:

• Unified observability: Advanced teams consolidate tools into platforms that provide a single pane of glass. This doesn’t just save time; it prevents gaps where critical insights might be missed.
• Pre-built incident views: Build and maintain custom dashboards tailored to incident types, so the most relevant data is already surfaced when incidents occur.

2. Lack of Context in Observability Data

Why it happens: Logs, metrics, and traces often lack metadata to tie them back to the broader context. Without this, engineers spend time correlating events manually—a painful exercise during high-stake incidents.

What experts do:

• Distributed tracing: Veteran teams don’t just rely on logs and metrics—they implement tracing tools like OpenTelemetry to gain end-to-end visibility into service interactions.
• Metadata tagging standards: By enforcing consistent tagging across all observability data—service IDs, environments, deployment versions—teams make it easier to query and correlate during incidents.

3. Siloed Data and Teams

Why it happens: RCA becomes a bottleneck when data is spread across multiple tools owned by different teams. Lack of shared playbooks or aligned processes makes coordination chaotic, further delaying resolution.

What experts do:

• Centralized data aggregation: Adopt platforms that aggregate data from all relevant sources—metrics, logs, traces, and events—into one system. Tools like Grafana Cloud are favorites for their ability to break down silos.
• Cross-team RCA culture: Senior SREs champion blameless postmortems and cross-team playbooks, fostering shared accountability and aligned response protocols.

4. Alert Fatigue

Why it happens: Years of experience teach you that more alerts don’t equal better alerts. Many teams suffer from noisy, redundant alerts that obscure the critical signals during incidents.

What experts do:

• Smarter alerting strategies: Move beyond static thresholds by using dynamic alerting tools like ML-powered solutions that adapt to service behavior over time.
• SLO-driven alerting: Tie alerts directly to Service Level Objectives (SLOs). This ensures every alert is actionable and tied to user impact, helping teams focus their efforts during incidents.

5. Reactive RCA Culture

Why it happens: Teams new to production incidents often see RCA as a reactive task—only focusing on it post-incident. This mindset delays systemic improvements and increases the risk of repeated failures.

What experts do:

• Proactive postmortems: Experienced SREs treat postmortems as a proactive learning opportunity. By analyzing incident patterns and underlying risks, they prevent problems before they occur.
• Multi-tiered SLOs: Advanced teams use multi-tiered SLOs to monitor early warning signs across their stack, catching potential issues before they escalate into major incidents.

What's the role of AI in root cause analysis?

As the arsenal of AI technologies is maturing, providing inference, causality, expert system decisioning and language capabilities (LLM), we believe that technology will soon disrupt the existing incumbent solutions, at the time that matters: during an incident! Dashboards / kpis / logs residing into the observability stack will become less relevant as AI systems will perform the root cause analysis and take remediation steps moments after an incident has occurred.

Don’t take me wrong; observability tooling will still be at play - and they won’t become obsolete, as they will provide the wealth of insights and signals for identifying and squashing bugs, removing performance bottlenecks and aggregate usage, logs and other valuable metadata.

Get a first glimpse into how NOFire AI can already today substantially accelerate RCA and therefore your production reliability.