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What is MTTR?
What’s the meaning of MTTR? Mean Time to Resolution. MTTR is defined as the measure of the average duration to restore regular operation for an application, service, or infrastructure component. It’s a key performance indicator for incident management (KPI). To connect MTTR to customer satisfaction, you must know how it impacts service and application reliability and availability. From there, you can make informed decisions, operate efficiently, and provide a seamless customer experience.
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Key components of MTTR
MTTR measures how quickly a system recovers after an issue. The goal is to minimize downtime and get things back to normal as soon as possible. Several components contribute to the total resolution time:
- Detection: This is the time it takes to spot an issue. Monitoring tools, alerts, and automated detection systems play a significant role in reducing incident detection time. The faster you catch the problem, the better your chances of keeping MTTR low.
- Acknowledgment: After detecting the issue, the team needs to acknowledge it. This step involves confirming the problem and identifying the next steps. Delays here can prolong the overall resolution time.
- Investigation and diagnosis: This part often takes the most time. Diagnosis may need troubleshooting, checking logs, or running tests to find the root cause.
- Repair: After you’ve diagnosed the issue, it’s time to fix it. Whether you’re restarting services, applying a patch, or replacing hardware, minimizing downtime is critical.
- Recovery and testing: After fixing the issue, you must restore and test the system to ensure everything functions correctly. This step often involves verifying that there are no other issues and that operations have been successfully restored.
- Restoration and communication: The final step involves updating dashboards, notifying stakeholders, or closing the incident ticket to communicate that the resolution is complete.
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How to calculate mean time to resolution (MTTR)
MTTR calculation divides the time spent resolving incidents by the number of incidents resolved within a given period. This MTTR formula depicts how quickly and effectively an IT team can address and solve problems.
MTTR = (Total of time to resolve all incidents) ÷ (# of incidents)
For example, let’s say a system had two incidents in a year. The resolution time for the first incident was six hours. The resolution time for the second was 10 hours. The MTTR would be 8 hours.
8 = (6 hours + 10 hours) ÷ 2 incidents
A lower MTTR signifies a more responsive IT environment, underscored by faster response and better customer satisfaction. Quick resolutions help maintain operational continuity and safeguard against revenue and reputational damage caused by outages or service degradations.
MTTR vs. Other important metrics
MTTR is important for measuring the speed at which incidents are resolved. Discussions often include other metrics to provide a more comprehensive view of system performance.
For example, the mean time to detect (MTTD) measures how long it takes to detect an issue after it occurs. A high MTTD means it’s taking too long to spot problems, which slows down the entire resolution process.
In addition to mean time to resolution, MTTR is used for various terms, including repair, recovery, response, or resolution. While these measures similar ITOps areas, their definitions differ. Be sure to confirm the specific incident metric represented when discussing MTTR.
- Mean time to repair (MTTR): The average time needed to fix and restore a failed IT system or part to working order. It typically includes the full repair process — diagnosing, fixing, and confirming the resolution — and indicates the technical teams’ efficiency.
- Mean time to recovery: This is a measure that shows the response time it takes to resolve an IT service after a failure. It includes time for repairs, restoring data, restarting systems, or switching to a backup system.
- Mean Time to Respond: The average response time for a service team to address a reported issue is important. It shows how quickly the service desk reacts. This time helps set user expectations for service delivery.
Mean time between failures (MTBF) tracks system reliability by measuring the average response time between breakdowns. While MTTR focuses on how quickly an issue is fixed, MTBF indicates how often problems happen in the first place. Together, MTBF and MTTR provide a balanced view of system resilience: MTBF shows reliability, and MTTR measures recovery efficiency.
Learn more in “Guide to incident-response metrics and KPIs.”
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Case study: How Autodesk improved MTTR by 85%
Before BigPanda, Autodesk struggled with a flood of alerts — more than 100,000 every month — and the inefficiencies of juggling multiple monitoring tools. The large amount of data and complex tools made it hard to find the root cause. This added extra manual steps, which increased MTTR.
By using BigPanda, Autodesk made its processes easier. It added useful data and smart ticketing that worked well with ServiceNow and Slack. Event correlation with BigPanda reduced the alert noise, reducing incidents by 69% and MTTR by 85%. These improvements enabled the IT team to detect anomalies more quickly and manage resources more effectively. Read the full Autodesk case study.
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Why is reducing MTTR important?
Five reasons lowering MTTR for IT operations is essential include:
Maintaining high system and service availability
High availability is a top priority to ensure access to systems and services with minimal interruptions. MTTR directly affects system uptime: The faster you resolve issues, the less downtime for users and customers. Keeping MTTR low means systems stay operational, even when unexpected issues arise.
Improving user experience
Faster issue resolution helps both internal employees and external customers. It means less downtime, fewer service disruptions, and smoother operations. This is especially important for services that interact with customers. Downtime can erode trust, result in lost sales, and lead to frustration.
Reducing the impact on business operations
Contain and resolve incidents before they escalate into bigger issues. For example, if an e-commerce site goes down, every minute of downtime can lead to significant revenue loss. By improving MTTR, IT teams keep disruptions brief, minimizing their operational and financial impact.
Improving compliance and SLA adherence
Many organizations have strict service-level agreements (SLAs) that specify maximum allowable downtime or resolution times. Failing to meet these targets can result in penalties, damage to reputation, and strained customer relationships.
Organizations operating in industries with stringent regulatory requirements — such as financial services and healthcare — may face significant compliance issues if downtime impacts critical operations. Keeping MTTR low to meet SLAs and regulatory standards can protect your organization from legal and financial consequences.
Enhancing operational efficiency and resource allocation
The faster IT teams resolve issues, the more they can focus on tasks that improve overall productivity. They can also manage resources more effectively, balancing keeping systems healthy and driving business growth. High MTTR means they spend too much time putting out fires. This takes resources away from important work like system or security improvements.
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Factors that affect MTTR
Reducing mean time to resolution isn’t easy. Common IT operational and technical challenges include:
- Complexity of IT infrastructure
- Alert noise and false positives (alert fatigue)
- Siloed tools and data
- Siloed teams and inadequate knowledge-sharing
- Poor visibility into complex IT environments
- Inefficient workflows
- Lack of context in alerts
- Manual processes and human error
One hurdle is the increasing complexity of hybrid IT environments with diverse systems, applications, and infrastructures. These growing tech stacks make diagnosis and resolution more difficult. Monitoring and management tools often need to work together. When this doesn’t happen, important data gets stuck in silos. This reduces our ability to monitor how the system is performing and identify any issues that may arise.
Many organizations need to enhance their documentation and knowledge-sharing practices. Poor communication causes delays if teams have to start from scratch to identify and resolve each incident. The large number and different types of alerts can overwhelm IT operations teams. This can cause alert fatigue and lead to the missed detection of important incidents. These challenges underscore the need for a more holistic, integrated, and automated approach to IT operations management.
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How BigPanda helps reduce MTTR
BigPanda streamlines IT incident management using AI-driven event correlation and root-cause analysis. The platform integrates monitoring tools, normalizes real-time event data, and transforms it into actionable insights. Instead of becoming overwhelmed by alerts, your IT team can focus on diagnosing and resolving issues faster.
BigPanda uses AI to correlate events, helping teams diagnose incidents and pinpoint their root cause faster. More efficient problem identification is crucial for lowering MTTR and maintaining high service availability.
Another notable feature is the BigPanda Similar Incidents component, which identifies recurring patterns from past issues. BigPanda gathers important past data when a new incident happens. This helps IT teams use past solutions and avoid doing the same troubleshooting again. This accelerates resolutions and reduces manual work.
Next steps
Read about more organizations that reduced MTTR by implementing the BigPanda platform:
- At FreeWheel, a Comcast company, we reduced MTTR by 78%. This cut the average resolution time from 25 hours to 5.5 hours. We achieved this by providing high-quality, actionable incidents to our response teams.
- “BigPanda has enabled us to get more real-time, relevant data around a specific incident,” shared Steve Liegl, director of infrastructure and operations at WEC Energy Group. “This has significantly reduced our MTTR.”
- “We can now route [alerts] to the appropriate teams. We get them to that team faster and reduce MTTR, which makes the customers really happy,” said Jon Moss, head of edge software engineering at Zayo.
