The ultimate guide to incident management KPIs and metrics

IT incident management aims to swiftly identify, address, and resolve IT disruptions to restore normal service operations. Tracking IT incident management key performance indicators (KPIs) is a vital step toward minimizing disruptions for customers and users.

But there are several different KPI and metrics choices, and it’s not easy to identify the right ones that can drive meaningful improvements in incident management. Let’s explore which incident management KPIs to monitor and how to use them.

The goal of incident management is to handle and escalate incidents as they happen to fulfill defined service levels. Incident management KPIs provide insights into how an enterprise addresses these incidents, the duration of outages, team performances, and more. These incidents include hardware, software, and quality issues.

Crucial KPIs include incident duration, number of incidents, and mean time to resolve (MTTR). These KPIs precisely measure the impact on users, highlightingthe importance of quick recovery due to the potential business losses from system downtimes.

While ‘KPIs’ and ‘metrics’ can be used interchangeably, they have distinct meanings. KPIs are tailored to critical business objectives, whereas metrics might simply reflect a status. By closely monitoring the appropriate KPIs and metrics, an organization can gauge its progress toward its incident management objectives.

Tracking incident management KPIs helps IT service team managers improve their operations. They use them to highlight problems with a company’s IT infrastructure.

Incident management KPIs can show which part of the resolution process is weak. For example, an ITOps team is concerned about the duration of service disruptions. Their manager discovers that while the team is quick to diagnose simple issues, they are slowed down by a cumbersome process for sorting through alerts and escalating them, and take too long to assign to the right L2 and L3. So measuring the duration of service disruptions informs the manager that part of the incident management process needs special attention.

An analysis of alerts can reveal correlations between issues involving the same configuration, priority, user group, dependencies, tech assigned, service involved, location, or other factors. Then, you can look at these holistically and design changes.

KPIs can benchmark the team’s performance and provide concrete goals for improvement. Tracking KPIs is motivating and makes ITOps more efficient.Better productivity leads to higher user satisfaction, increases the reliability of systems, and helps your business achieve its objectives. Furthermore, KPIs can spotlight hardware that needs replacement or configuration issues when incidents are categorized.

  1. Compression rate: Use this metric to measure a team’s ability to tie multiple incidents to the same cause so engineers treat the cluster as one incident. Tracking this number can increase efficiency.
  2. Impact duration: This measures the time between issue onset and incident resolution, impacting customer and user experience. It’s crucial for service availability and is sometimes quantified in “impact minutes” to make user experience impact more tangible.
  3. Incident duration: The period between when the service team creates a ticket, the end of the incident, and when the ticket is resolved. This KPI represents the effectiveness of your incident management process. Duration reflects how much of your service resources you use and can be the key indicator in service-level agreements (SLAs).
  4. Mean Time to Detect/Discover (MTTD): The average time between the onset of an issue and its detection. This number shows how long it takes a team to recognize a problem.
  5. Incidents resolved on first contact: The percentage of incidents resolved when the service desk has initial contact with a user or a support team member touches the problem for the first time.
  6. Mean Time To Acknowledge (MTTA): The average time between an alert and the service team acknowledging it.
    Mean Time To Action (MTTA): The average time from an alert to the first action to fix it. This KPI measures how quickly engineers see and respond to an incident.
  7. Mean Time To Assign (MTTA): The average time from receiving an alert to assigning it to service staff. This KPI is typically the same as the mean time to action. This metric indicates how long it takes to identify the proper owner of the resolution process, and the team or person best equipped to deal with the issue.
  8. Mean Time To Know or Diagnose (MTTK): The average time between an alert and when you know the cause of the issue.
  9. Mean Time To Recover/Repair/Resolve (MTTR): The average time from receiving an alert to recovery—meaning the system is functioning again.
  10. Mean Time to Remediate: The average time between discovering a threat or vulnerability and closing the ticket, more commonly used with IT security.
  11. Incident volume: How many incidents occur in a given period, such as a week, month, and year.
  12. Uptime: The percentage of time that an application or system is available to users and fully functioning.

Incident management plays out against baseline expectations agreed to by an organization and its customer or between the IT team and its users. The components of these expectations are the following:

  • Service-level agreement (SLA): An agreement in which the service provider promises the user certain performance. The SLA defines penalties or consequences for not delivering results, such as a cost reduction for the customer. The user might have responsibilities, too, such as reporting issues promptly. The ITOps team may monitor the number of incidents that breach SLAs.
  • Service-level objective (SLO): SLOs are part of the SLA. These service objectives are the metric levels for determining if the SLA was achieved. Ideally, SLOs should be straightforward to track. Common measures are uptime (how much of the time an application or service is operational), latency, throughput, and MTTR.
  • Service-level indicator (SLI): The SLI measures compliance with SLOs. If your SLO has an uptime of 99.95% and you achieved 99.96%, your SLI is 100 percent. To be compliant with your SLA, the SLI has to meet or exceed the standards set in that document.

Enterprise IT stacks typically generate a deluge of status messages and problem indicators. IT service managers can improve operations by focusing on meaningful incidents and the best KPIs. These incidents require action and have relevant, reliable, and real-time data.

Incidents that are meaningful to measure are:

  • Actionable: The incident requires action to prevent some level of business impact.
  • Comprehensive: You have all causal and symptomatic alerts that stem from the root cause.
  • Contextualized: Information to diagnose and remediate the incident is provided across teams and topologies, including diagnostic indicators and documentation.

These incidents exclude alerts that do not signal a service or hardware problem, such as configuration changes and security updates. The right KPIs for measuring these incidents are relevant, reliable in that they accurately capture it, and are available in real time.

Incident management dashboards

When putting together your incident management dashboard, keep in mind the difference between your KPIs and your metrics. Your metrics are simply measurements, like a status or process indicator, while your KPIs represent your strategic goals.

Key metrics dashboard

A KPI dashboard for incident management showcases key metrics in one view. The dashboard should continuously update so that you can see at a glance the status of incident management in real-time.

Common KPIs for a dashboard include the number of open incidents, average age of open incidents, average incident duration, average impact duration, MTTD, MTTA, and MTTR.

You can add any other metric that is relevant to your situation. For example, if your team has focused on improving one part of its workflow, pick a KPI to reflect performance changes in this process.

KPI dashboard

For a dashboard to have value, you must have reliable data to feed KPI analytics in real-time. Your dashboard can provide a live view of incoming incidents, origin, status, and how quickly the team closes tickets.

BigPanda Unified Analytics streamlines data-driven improvements in incident management and business outcomes within complex IT operations. Its interactive dashboards, like the Executive Summary dashboard shown here, offer fresh insights and pre-defined KPIs to measure and track performance against targets.

You can then use these insights 
to create new use cases that 
showcase the tangible business benefits stemming from 
improvements in IT Operations.

While a team’s trend over time on incident management KPIs is critical, companies also want to know how their performance compares to their peers. We should note that variables such as prioritization system, geographic spread, and lead time influence KPIs.

Common goals and averages for incident management KPIs:

Here’s a quick look at incident management KPIs across the different phases of AIOps:

  • Phase 0: Chaotic – Incident management is haphazard, with either no documented processes or processes specific to individual teams. This lack of coordination often results in significant delays during diagnosis and root cause analysis.
  • Phase 1: Reactive – While a documented incident management process is in place, the limited trust among team members hinders effective information sharing during diagnosis and root cause analysis.
  • Phase 2: Responsive – The incident management process is well-documented and consistently applied across teams. Although some tasks might be automated, the majority of the process still relies on manual efforts.
  • Phase 3: Proactive – A large portion of the incident management process is now automated. While auto-remediation is applied to certain scenarios, it doesn’t handle more intricate issues well.
  • Phase 4: Semi-Autonomous – The entirety of incident management, from detection to resolution, is automated. This system is adept enough to efficiently direct complex incidents to the appropriate channels.

When comparing your organization’s incident management performance to these phases, you can use these descriptions as benchmarks. The goal is to progress from chaotic or reactive phases towards the more proactive and semi-auto AI phases, where automation and coordination are highly effective.

Understanding the ITIL incident management framework

Using the ITIL framework, companies can standardize their incident management practices. ITIL provides essential guidance to establish and optimize incident management based on the IT Service Management (ITSM) model. These incident management practices aim to minimize the negative impacts of incidents by restoring normal service operations as soon as possible.

In this model, the IT service desk is a single point of contact with users about disruptions. The service team follows best practices for incident management to resolve big and small issues, from a network outage to a printer not working.

However, this framework is meant to be adapted to your organization’s specific structure and requirements, encompassing computing, applications, and networks. For instance, the incident management process is more fluid in younger or agile organizations and those embracing DevOps. The group may tailor the incident response to the nature of the problem, often with a cross-functional team.

Every ITOps, SRE, and DevOps team strives to improve their KPIs. But, knowing what changes will have the greatest impact can be challenging. These are some of the best ways to improve incident management performance.

Reduce the number of incidents

Noise often overwhelms service teams and impacts all incident management KPIs because high incident volume stresses resources and makes it hard for service engineers to quickly respond, diagnose, and resolve problems.

To reduce the number of incidents, you must optimize alert management, implement rule-based silencing to filter out unnecessary alerts stemming from routine activities, and adjust alert parameters to reduce non-actionable notifications. Additionally, correlating alerts can group related notifications, preventing teams from addressing duplicate incidents.

Improve incident response time

To slash your incident response time and enhance incident management, automate the resolution of minor issues, ensure clear escalation protocols, and utilize AIOps for proactive anomaly detection and alert correlation. By doing so, teams can prioritize significant incidents, reduce duplication, and ensure efficient response mechanisms.

Strengthen diagnostic capability

Effective incident management addresses immediate incidents and emphasizes in-depth problem management to prevent future issues. Incorporating post-incident reviews, codifying knowledge in runbooks, and leveraging AIOps can streamline responses, aid in root cause analysis, and reduce mean time to resolution (MTTR).

Embrace continuous improvement.

To enhance incident management, continuously iterate and evaluate process improvements using key performance indicators, like MTTR, as a measure of success. Additionally, gather comprehensive data on each incident, asking specific yes-no questions to identify areas of improvement and better understand the nature of the incidents.

Monitoring incident management KPIs is crucial, but it’s essential to recognize their limitations, as excessive data can cloud vital insights, and KPIs reveal outcomes, not causes. The complexity of diagnosing problems requires a deeper analytical approach and machine learning tools.

MTTx metrics, which measure ITOps efficiency, can be misleading; for instance, a company with fewer incidents but a longer MTTR might manage its IT better than a company with a shorter MTTR due to frequent minor issues.

While KPIs are helpful, they have limitations and can lack context and actionability. For instance, the same MTTx can signify different business impacts depending on external circumstances. Imagine your company has no issues for months, then faces a major incident that takes a day to resolve, resulting in a 10-hour MTTR. In contrast, a typical enterprise with frequent minor incidents might have a 2-hour MTTR. Yet a company without issues for 11 months may manage its IT better.

Additionally, improving incident management might alter KPIs unexpectedly, such as AI-based solutions increasing MTTR while boosting efficiency. That’s why incident management needs AIOps to add critical context and to make incidents easily identifiable and actionable.

AI and ML are reshaping computing and automating formerly manual processes in incident management. Here’s how AI/ML power AIOps platforms to perform the following functions in incident management:

  • Filter alerts so important issues stand out
  • Identify the right team to address the issue
  • Connect related alerts to minimize redundant work
  • Automate aspects of ticketing, issue categorization, and escalation
  • Correlate alerts and system changes to identify the root cause
  • Visualize topology and the timeline to expedite resolution

Because AIOps harnesses AI to sift through massive operational datasets, it sheds light on incident management KPIs. That’s where BigPanda comes into the picture and offers deeper, contextual perspectives. This in-depth visibility not only streamlines incident response but also drives continuous improvement in operational strategies.

Transform your incident management capabilities with the power of BigPanda Operational Intelligence and Automation Platform, driven by AIOps. BigPanda helps you swiftly and intelligently resolve IT incidents with the following features:

  • Real-time incident detection: BigPanda quickly spots problems as they happen. This proactive approach empowers you to address problems promptly, preventing potential business-disrupting outages.
  • Context-rich triage: With a wealth of contextual information, incidents are triaged swiftly and accurately. This context-rich environment enables your team to make informed decisions, significantly reducing incident response times.
  • Advanced topology modeling: The BigPanda platform goes beyond surface-level analysis. It builds detailed models of your setup, making it easier to find and fix what’s causing issues now and prevent them in the future.

With BigPanda, gain system-wide visibility, conduct context-rich triage, and automate your root case analysis to slash your MTTR. Get a personalized demo to experience how BigPanda can improve service availability and transform your incident management.