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What is Mean Time to Resolution – and why does it matter?

Mean Time to Resolution (MTTR) is a key performance indicator (KPI) that measures the average duration needed to restore normal operation for an application, service or piece of infrastructure component.

Your MTTR directly impacts customer satisfaction, so you must have a keen understanding how it influences the reliability and availability of your services and applications to make informed decisions, enable operational efficiency, and ensure a seamless customer experience.

While understanding the meaning of MTTR may be relatively simple, knowing how to reduce it is anything but!

Let’s explore MTTR, how it works, and is calculated. Then we’ll discuss strategies to reduce your MTTR so you can optimize your processes, prioritize resources, and reduce downtime. Keep reading to learn:

  • What is Mean Time to Resolution– and why does it matter?
  • Is MTTR one metric – or four different ones?
  • How to calculate mean time to resolution
  • What’s a ‘good’ MTTR?
  • Challenges of reducing MTTR
  • How can AIOps lower MTTR?
  • How AIOps reduces MTTR
  • How FreeWheel slashed their MTTR with BigPanda AIOps

Is MTTR one metric – or four different ones?

In IT operations, MTTR is commonly used to describe the mean time to repair, recover, respond, or resolve. While these measure similar ITOps areas, they can differ considerably.

That’s why when discussing MTTR, it’s essential to confirm which incident metric is being discussed. Here are the definitions of these MTTR metrics:

  • Mean Time to Resolution or Resolve (MTTR): This critical metric measures the average duration required to rectify a system or service incident.
  • Mean Time to Repair (MTTR): This metric measures the average time required to repair and restore a failed IT system or component to operational status. It encompasses diagnosing, fixing, and confirming the resolution of an issue, reflecting the repair efficiency of technical teams.
  • Mean Time to Recover (MTTR): This broader metric quantifies the average duration needed for an IT service to recover from a failure and resume normal operations, including repair, data restoration, system restarts, or switching to a backup system.
  • Mean Time to Respond (MTTR): This metric indicates the average time IT teams take to initially respond to a reported issue or incident, serving as a key measure of the service desk’s responsiveness and setting user expectations for service delivery.

Mean Time to Recovery Venn diagram

How to calculate Mean Time to Resolution

Mean Time to Resolution is calculated by dividing the total time spent on resolving incidents by the number of incidents resolved within a specific period. This MTTR formula depicts how swiftly and effectively an IT team can address and solve problems.

Mean Time to Resolution or Resolve (MTTR) = (Total of time to resolve all incidents) ÷ (# of incidents)

  1. Let’s say a system has 2 incidents in a year, with a resolution time of 6 hours for the first incident and 10 hours for the second one.
  2. Then the Mean Time to Resolve in this scenario is the total time to resolve all incidents (6 hours + 10 hours) divided by 2 incidents = 8 hours

What’s a ‘good’ MTTR?

A good MTTR can vary depending on your industry and your IT operational effectiveness. However, we commonly see an average MTTR of around 2 hours for a minor incident, while major incidents have an average MTTR of 8 hours.

A lower MTTR signifies a more responsive IT environment, minimizing disruptions and enhancing customer satisfaction. Quick resolutions help maintain operational continuity and safeguard against the revenue and reputational damage of system outages or service degradations.

Chart on MTTR

Challenges of reducing MTTR

Reducing your Mean Time to Resolution isn’t easy. This is due to several common IT operational and technical challenges, which include:

  • Poor visibility into complex IT environments
  • Siloed teams and inadequate knowledge sharing
  • Limited system dependency awareness
  • Siloed data
  • Alert fatigue

One of the hurdles is the increasing complexity of hybrid IT environments with diverse systems, applications, and infrastructures. These growing tech stacks make diagnosis and resolution more difficult. Given the frequent need for integration between various monitoring and management tools, this critical data ends up siloed, removing visibility from system performance and issues.

Additionally, many organizations struggle with inadequate documentation and knowledge sharing. This causes delays as teams must start from scratch to resolve each incident. The sheer volume and variety of alerts can also overwhelm IT teams, leading to alert fatigue and the risk of missing critical incidents. These challenges underscore the need for a more holistic, integrated, and automated approach to IT operations management.

How can AIOps lower MTTR?

Eliminate alert noise

AIOps streamlines collaboration between IT operations and other teams using ticketing, chat and collaboration tools via bi-directional sharing and syncing. This helps ITOps teams gain insight into critical alerts, while correlation and alert grouping let teams quickly review all incident alerts to accelerate triage. These accelerated diagnostic capabilities reduce MTTR by enabling quicker response times and more precise interventions.

Streamline team collaboration

AIOps streamlines collaboration between IT operations and other teams using ticketing, chat and collaboration tools via bi-directional sharing and syncing. This allows companies to improve their application and service performance and availability to reduce MTTR.

Proactive incident management

By detecting incidents as they start to form and surfacing them before they escalate into costly outages, AIOps allows IT Operations teams to take a real-time incident management approach. This helps these IT teams switch from reactive to proactive incident management.

Holistic visibility of IT infrastructure

AIOps platforms offer integration with various monitoring tools, giving companies a comprehensive view of their IT infrastructure. This is instrumental for rapidly identifying and coordinating response efforts, particularly for cross-system or network-wide incidents.

Automate root cause analysis

AIOps can automatically identify the potential underlying cause of an IT incident and explain its impact across complex hybrid cloud deployments. When armed with the instant incident root cause analysis provided by AIOps, your ITOps teams can swiftly resolve incidents and significantly cut their MTTR.

Chart - How AIOps reduces MTTR

How FreeWheel slashed their MTTR with BigPanda AIOps

FreeWheel, a Comcast subsidiary specializing in TV advertising platforms, faced significant challenges in managing the sheer volume of daily alerts, averaging 15,000. This inundation resulted in extended outages and increased operational costs, with a MTTR averaging 25 hours per incident.

Implementing BigPanda AIOps was a transformative shift for FreeWheel. AIOps enabled them to leverage AI and machine learning (ML) to discern patterns and preemptively identify system issues. This significantly shortened the time required for incident resolution, helping to reduce complex, labor-intensive repairs.

AIOps analyzed vast operational data and applied ML algorithms to pinpoint potential root causes swiftly, giving IT teams targeted insights for quicker intervention. This acceleration was vital in reducing the MTTR, as evidenced by FreeWheel’s impressive 78% reduction in response time after implementing BigPanda.

Reduce MTTR by 50% with BigPanda AIOps

BigPanda improves the performance and availability of critical business applications by reducing MTTR by 50% or more. With AIOps, you can eliminate noise, automate specific incident management processes, and reduce your ITOps team’s manual toil. With BigPanda, you can:

  • Slash alert noise: By employing advanced algorithms, cut down noise by 90% or more, enabling more focused and efficient issue resolution and enhancing service performance.
  • Streamline collaboration and resolution: The platform facilitates streamlined team collaboration and automates various aspects of incident management, including early detection of incidents to prevent outages.
  • See what’s happening at a glance: BigPanda provides detailed, insightful reports on key performance indicators and trends, driving long-term improvements in incident management capabilities.

Discover how BigPanda can help you by experiencing our customized demo. With BigPanda, optimizing MTTR is more than faster rapid incident resolution; it also ensures consistently high service delivery and seamless IT operations.