DevOps and engineering teams operate under significant pressure to manage, scale, and deploy complex systems. When problems occur, it is often quicker and easier to patch, deploy a fix, or roll back a deployment than to find and resolve the root cause of the issue.
Root-cause analysis (RCA) is a different strategy: it involves systematically identifying the cause of an incident rather than addressing its symptoms. RCA helps teams implement permanent solutions, boost system resilience, and prevent repeated issues across infrastructure and operations. This article explores RCA and how teams can adopt this process.
What is RCA and why is it important?
Root-cause analysis (RCA) is a systematic, step-by-step method to identify the primary causes of a problem or incident by collecting and examining relevant data and testing solutions.
For DevOps and engineering teams, full-stack observability has become essential for maintaining strong application performance and reliability. This comprehensive strategy is crucial for effective RCA in application monitoring. RCA not only identifies symptoms but also uncovers the underlying causes of system disruptions or performance drops.
For development teams, RCA processes should target the underlying causes of errors and defects. This strategy helps keep systems stable, reliable, and efficient, minimizing expensive downtime and speeding up development. Moreover, RCA helps developers prioritize issues by impact and severity, allowing them to focus on the most critical problems first.
In security and compliance, RCA offers a structured approach to identify the root causes of breaches or failures. It looks beyond surface-level issues like malware to identify weak processes, human errors, or system flaws. This approach helps implement solutions, prevent future incidents, and strengthen the overall security posture.
RCA adoption can also help:
Prevent recurrence by addressing the root cause rather than applying temporary fixes.
Enhance reliability by reinforcing systems and resolving fundamental weaknesses.
Increase efficiency by minimizing the time spent troubleshooting similar issues.
Promote accountability through transparent, data-based analysis of incident causes.
Promote continuous improvement by supporting ongoing learning and process improvements across teams.
How does RCA work?
The RCA process uses specific steps to identify, review, analyze, design, and document an issue or incident. These steps can be explained as follows:
Incident detection (define the problem): State what happened, its impact, and its symptoms (for example, “Application latency increased by 50% for users in Europe”).
Data collection: Gather logs (application, system, and network), metrics (application performance monitoring [APM] and infrastructure), timelines, and user feedback related to the event.
Root-cause identification: Distinguish between contributing factors and the primary cause. Solution design: Develop action plans for code fixes, configuration changes, or process updates (for example, add automated performance tests).
Validation and documentation: Verify the effectiveness of fixes and record findings for future reference. Incorporate these discoveries into DevOps practices to drive proactive improvement.
Additionally, Causal analysis is a core part of RCA. It helps teams identify causal factors using analysis, diagrams, or both. Teams typically use one or two methods depending on incident complexity. Common approaches include:
Five whys: Repeatedly ask “Why?” to dig deeper (for example, “Why was it slow?” There were slow queries; “Why were there slow queries?” There was no index. “Why was there no index?” It was missing in the migration. “Why was it missing?” The issue was not included in performance testing.)
Fishbone (Ishikawa) diagram: Visualize causes across categories like people, processes, tools, environment, and code.
Fault-tree analysis: Provide a top-down, deductive, graphical method for analyzing how a system can fail.
Which teams and scenarios are most relevant for RCA?
Teams facing recurring issues, major failures, quality defects, or safety/compliance problems can significantly benefit from RCA. This process moves teams from quick fixes to sustainable solutions, improving reliability, efficiency, customer satisfaction, and risk management across operations and projects.
DevOps and site reliability engineering (SRE) teams can use RCA methods to diagnose production incidents and improve reliability for recurring issues, system crashes, performance issues, or deployment failures.
Engineering teams can use RCA methods to identify systemic processes or architectural weaknesses in the development, testing, and deployment of applications and code.
IT operations teams can use RCA methods to improve workflows and decrease ongoing downtime.
Quality assurance (QA) and compliance teams can use RCA to focus on audit and governance standards, identify the root causes of recurring defects, and ensure the product’s long-term integrity.
Examples of specific teams deploying RCA practices include:
For service outages, RCA methods can examine the reasons behind a critical system failure, identify a sudden server overload, or analyze a poorly coded update that causes frequent crashes.
For performance degradation, RCA methods can identify the causes of slow response times, diagnose application freezes, and provide teams with an understanding of network performance declines during peak hours.
For security incidents, RCA methods can identify the source of a breach or vulnerability, examine its causes (such as weak passwords, unpatched vulnerabilities, or phishing), and investigate failed login attempts and reasons users can’t access their accounts.
For product defects, RCA methods can trace recurring quality problems in product designs or process gaps, investigate application crashes and memory leaks, and analyze testing patterns that overlook performance issues.
What shifts in the industry have affected how RCA is applied?
The rapid evolution of the IT industry, characterized by cloud-native systems, microservices, continuous code updates, and intricate hybrid environments, complicates RCA. There is an increasing need for AI and observability tools that can analyze large volumes of data, moving the focus from reactive problem-solving to proactive prevention. This shift also demands enhanced collaboration among often-siloed teams to keep up with dynamic digital operations and achieve faster resolution times.
The following are key subject areas to consider:
Rise of distributed and cloud-native architectures: Microservices, containers, serverless, and multi-cloud environments have increased the complexity and interdependence of failure modes. RCA has evolved from a focus on individual nodes to end-to-end, service-level analysis spanning multiple components.
Observability patterns over simple monitoring: The industry has advanced from simple host metrics to comprehensive full-stack observability, including logs, metrics, traces, and profiling. Successful RCA now requires correlating diverse telemetry data rather than relying on a single signal.
Automation and AI-assisted troubleshooting: As systems grow larger, manual investigation slows down significantly. Teams are increasingly turning to automated correlation, anomaly detection, and suggested root causes to resolve issues faster and reduce alert fatigue.
Blameless postmortems and DevOps culture: Modern incident management highlights a blameless RCA approach, prioritizing process, design, and context over individuals. This encourages more honest analysis and fosters long-term improvements in reliability.
Shift-left reliability: RCA insights are increasingly integrated into design, development, and testing processes, allowing reliability and failure modes to be addressed earlier in the lifecycle rather than only after production incidents.
What implementation challenges are associated with RCA?
Challenges linked to RCA include limited time and resources, poor data quality, resistance to change, and organizational culture issues, which can lead to incomplete investigations, superficial solutions, or failure to apply findings. Consider these additional examples of challenges facing teams:
Incomplete data: Missing logs or metrics can obscure the root cause, including missing logs, poor data quality, or insufficient evidence.
Time pressure: Teams might prioritize restoration over thorough analysis due to fast-paced environments that demand immediate fixes.
Bias and assumptions: Conclusions without evidence can mislead investigations due to inadequate expertise or insufficient training.
Lack of standardization: Inconsistent RCA processes can reduce effectiveness due to a lack of structure or training.
Cultural resistance: Fear of blame can discourage open discussion of errors. Teams might resist changes, blame individuals instead of systems, or fear repercussions, all of which hinder open investigation.
What features should teams look for to support RCA?
For practical RCA, teams require features that enable collaborative data collection, comprehensive systemic analysis beyond symptoms, consideration of human and organizational factors, analysis and detection methods (such as “five whys” and fishbone diagrams), tracking actionable solutions, and promotion of a blameless culture. All of these should be supported within a framework that clearly defines roles, provides sufficient time, and ensures strong management backing for implementing improvements.
Consider the following solution features to look for and compare when incorporating RCA methods:
Unified telemetry: Metrics, logs, traces, and more
What it is: A unified platform that consolidates infrastructure metrics, application traces, logs, profiles, and synthetic results
Importance for RCA: Reduces the “incomplete data” problem by allowing teams to swiftly transition from a metric spike to relevant logs and traces with only a few clicks, removing the necessity to gather data from multiple tools
Service maps and dependency visualization
What it is: Visual maps illustrating how services and components rely on each other within a distributed system
Importance for RCA: Helps teams pinpoint the source of a failure and follow its spread, which is essential in microservice and cloud-native environments. Service maps and visualizations directly address the issues faced by modern architectures.
Intelligent correlation and anomaly detection
What it is: Features that automatically link events, alerts, and anomalies across services and identify unusual behavior using statistical or machine learning (ML) techniques
Importance for RCA: Helps filter noise and time limits by promptly highlighting possible related signals and unusual patterns. Correlation and anomaly detection features support engineers by reducing guesswork and biases when developing hypotheses.
Incident management with rich timelines
What it is: Integrated incident management tools that record events, alerts, changes, and investigator actions in a single, chronological view
Importance for RCA: Provides a clear timeline of events, showing when they happened and who was responsible. Incident management features support accurate post-incident analysis and help address issues caused by incomplete data and limited time.
Postmortem and collaboration support
What it is: A workflow support system for documenting incidents, sharing findings, and integrating with collaboration tools like Slack and ticketing systems
Importance for RCA: Supports consistent and repeatable RCA procedures and encourages a blameless culture by making it easier to gather learnings, assign follow-up actions, and share context across teams
Change detection and deployment visibility
What it is: Visibility into code deployments, configuration changes, and infrastructure updates, often integrated with observability data
Importance for RCA: Many incidents stem from recent changes. Showing “what changed and when,” together with performance and error data, can help quickly pinpoint the root cause, aiding teams in enhancing change management and prevention strategies
Datadog’s RCA tool helps teams pinpoint the root causes of issues, not just detecting their occurrence. It integrates metrics, logs, and traces to reduce mean time to resolution (MTTR) and prevents future problems by identifying specific causes like code updates or infrastructure failures. The tool converts alert noise into meaningful insights, revealing hidden dependencies and business impacts. This change allows teams to shift from reactive firefighting to proactive problem-solving.
Learn more
Discover more about RCA with Datadog’s article on automated root cause analysis with Watchdog RCA.
