As you integrate Site Reliability Engineering (SRE) best practices into your organizational framework, monitoring the efficiency of your incident management process becomes crucial. This forward-thinking approach is essential to a mature incident management strategy, and incident management KPIs are the cornerstone of effectively monitoring performance.

Key Performance Indicators (KPIs) are quantitative metrics that enable the evaluation of your processes, activities, and services' progression compared to your organization’s strategic objectives. Whether your KPIs are operational or strategic, their real value lies in their ability to provide clear, objective insights into your incident management effectiveness.

This article will review the importance of utilizing incident management KPIs, how they help measure the effectiveness of your current incident management processes and enable continuous improvement, and best practices to leverage these metrics wisely.

Summary of key incident management KPI best practices
While recommended practices may differ for different use cases, the following best practices — which we’ll explore later in this article — provide a solid baseline for effectively implementing incident management KPIs in an organization.

The role incident management KPIs play

Most successful enterprises are known to make strategic decisions based on KPIs that eventually help them pivot from reactive responses into proactive strategies. For instance, imagine the IT team of a large enterprise working through a backlog of incidents. They could tackle them mindlessly or leverage KPIs to identify patterns and achieve an iterative enhancement cycle for Continual Service Improvement (CSI).

But the effective use of KPIs requires careful consideration of several factors.

Remember that KPIs are not static. They should evolve with your business. If you notice that a particular KPI is consistently being met with ease, it might be time to revise your targets or introduce a new, more challenging KPI. Similarly, if a KPI is consistently missed, it may indicate that your process or resources need an overhaul.

The SLA adherence KPI is another significant indicator of your service delivery process. As part of the regular review process, if you find SLA breaches becoming common, it's essential to identify the root cause. Is the issue with the resource allocation, or are the agreed SLAs unrealistic?

It's also crucial to remain disciplined and avoid going overboard with all the potential KPIs you could track. Be selective and choose only those KPIs that best reflect your goals and provide actionable insights.

Key advanced incident management KPIs

The four advanced incident management KPIs below can help organizations take their incident management practices to the next level.

Percentage of Incidents Resolved Remotely (PIRR)

Think about the volume of incidents your team handles remotely over the total number of incidents. This rate should make sense in the context of your operations.

For starters, maintaining an acceptable PIRR means you're efficiently resolving issues without the need to send technicians to a physical (on-site) location, such as a customer’s office, a data center or any other location where the system or service is deployed. By contrast, incidents resolved remotely are those where the issue can be sorted out without needing to physically be at the location where the problem occurred. This could involve remote desktop control, customer support calls, or resolving server issues from a centralized location.

A higher PIRR helps quantify how often your team is able to solve problems without the need for time-consuming, costly on-site visits, and is typically a good indicator of efficient operations, as long as quality isn't compromised.

But be careful. Extreme spikes or dips in PIRR may indicate overlooked issues that require attention and could be a sign that some issues are not being addressed as they should be.

Recurring incidents percentage

Some incidents may consistently return, no matter how often you have resolved them. The recurring incidents percentage metric looks at how often you're seeing the same issues crop up, highlighting the need for more in-depth investigations.

A high percentage of recurring incidents commonly suggest that existing resolutions are essentially band-aid solutions and aren't effectively addressing the underlying systemic problem. A high rate here should also prompt you to investigate and rectify the effectiveness of the incident resolution and prevention mechanisms.

Ratio of incidents to problems

Are your team's efforts equally distributed between problem (root cause) analysis as much as resolving incidents? To assess this, consider measuring the number of incidents relative to the number of root causes you've identified.

Unlike the Recurring Incidents Percentage, which zeroes in on specific, repeating issues, a high Ratio of Incidents to Problems provides a broader view by suggesting that your team spends more time addressing symptoms (incidents) than identifying and resolving the root cause (problems). This approach may not necessarily involve the same recurring issues but suggests that the incident management approach is symptom-focused, instead of being problem-focused. Such an imbalance can potentially make your problem identification and resolution process inefficient, ultimately leading to repeat incidents with varied or even the same root cause.

Service level objectives (SLOs)

SLOs offers a pre-defined, nuanced view of service quality and reliability, encompassing various metrics that could affect Customer Satisfaction (CSAT) scores indirectly.
For instance, if you notice that your SLO budget has been significantly depleted over the last month, this could be an indicator of multiple factors: it might mean your product has bugs, or that a new feature is causing complications. Alternatively, it could also point that your incident response time needs a re-evaluation, affecting the time it takes to get back to operational normalcy after an issue.

As IT alerting solutions become increasingly integrated into incident management processes, organizations are better equipped to swiftly respond to critical incidents and ensure minimal disruptions to their services.

A high or low SLO can often preemptively signal the need for adjustments in your incident management strategy before it cascades into customer complaints or service level agreement (SLA) violations.

Four essential incident management KPI best practices

The four best practices below can help organizations optimize their use of incident management KPIs and improve performance throughout the incident lifecycle.

Incident management KPI best practice #1: Implement data standardization & visualization

KPIs are only as good as the data that informs them. Before you start tracking KPIs, ensure that the data you're pulling from is uniform and accurate.

For instance, you may track KPIs like mean time to resolve (MTTR), first call resolution (FCR) rate, incident recurrence rate, and SLA adherence. Each of these KPIs may be measured on different scales. MTTR might be in hours or days, FCR and SLA Adherence are in percentages, and the incident recurrence rate could be a raw count.

Analyzing these KPIs without standardizing them onto a common scale can be misleading. Large numbers from one KPI might dwarf smaller but more significant variations in another, leading to skewed interpretations.

Data normalization helps bring these different KPIs onto a common scale, ensuring a more accurate analysis and visualization.

• Min-max normalization adjusts the data to a specific range, typically between 0 and 1. It's excellent when you want to maintain the original data distribution but need it on a smaller, standardized scale. ‍ #### How does it work? Let's say you have MTTR ranging from 2 to 50 hours, and SLA adherence rates between 70% and 98%. Without normalization, a direct comparison of these metrics would yield unreliable insights due to the dissimilarity in their respective scales.

Min-max normalization adjusts these KPIs to a scale where the minimum value is transformed to 0, and the maximum value becomes 1. Every other value is adjusted proportionally in between.

For example, an MTTR of 2 hours becomes 0, an MTTR of 50 hours becomes 1, and an MTTR of 26 hours could be scaled to around 0.5. Similarly, an SLA adherence of 70% becomes 0, and 98% becomes 1. This scaling method allows you to make direct comparisons between disparate KPIs without losing the essence of what each KPI represents.

• Z-score standardization converts all data points into a common scale with an average of zero and standard deviation of one.

How does it work?

Let us take the example of Time to Resolve across different categories of incidents in your system. The Time to Resolve in minutes for network incidents might look like [30, 45, 40, 35, 50]. For application incidents, the times might be [70, 80, 75, 90, 85]. These two categories are on different scales, and comparing them directly could be misleading. Applying the Z-score standardization would calculate the Z-score for each of the categories, helping you understand if your incident resolution times are deviating significantly from the norm in either of the categories.

While Min-Max is about rescaling, Z-score is about centering the data around the mean and considering the distribution. As a result, this normalization approach is considered suitable for algorithms that assume features are centered at zero and have similar variances.

• Decimal scaling is another method where data is moved by decimal places to bring all points into a similar range. It's particularly effective when dealing with a wide range of values in a dataset.

How does it work?

This method standardizes the data but doesn't change its distribution. For example, consider an organization receiving an average of 100 incident reports per day. To make it easier to analyze the data, the organization can decimal scale the number of incident reports by dividing it by 10. This means that the number of incident reports will now range from 1 to 10, instead of 100 to 1000.
The approach helps to make the data more manageable and easier to analyze, ultimately helping track trends and identify areas where there are more incidents than others.

Before choosing one of the normalization methods, understand how they differ:

• Decimal scaling divides by a power of 10 to make values more manageable.
• Min-max scales to [0,1] to make values comparable.
• Z-score scales to mean 0 and std 1 to make values independent of distribution. Visualizing your KPI data can help you quickly spot patterns and anomalies that may be challenging to detect in raw data. The key, however, is choosing the right visual representation. It should align with the nature of your data and the insights you want to communicate. For instance, line graphs are excellent for tracking changes over time, while bar charts can effectively compare different categories.

And once your data is standardized, don't just leave it as random numbers in a spreadsheet. Tools like Squadcast can convert those raw figures into interactive charts and graphs, making it easier to spot Service Level Objective (SLO) trends and patterns at a glance. Remember, your KPIs should inform your actions, and a clear, effective visualization is a crucial step in that process.

Incident management KPI best practice #2: Leverage predictive analysis and AI-driven proactivity

The ability to forecast potential incidents before they occur holds significant value. Techniques like regression analysis or time series forecasting help achieve this by modeling relationships between variables and analyzing trends over time.

Consider leveraging AI/ML to automate KPI tracking. These technologies can churn massive amounts of data and find patterns otherwise practically impossible for a human to spot. More importantly, if Continual Service Improvement (CSI) is one of your key objectives, remember that the power of AI to learn and adapt over time can help your system become more intelligent and effective with every incident it encounters.

To support this, consider the following tips:

• Create clear policies and procedures for data usage. Uniformity and clarity in the policies within an organization help streamline and supercharge the adoption of AI/ML.
• Ensure data quality. High-quality data serves as the foundation of predictive analysis. Make sure to understand the type of data collected and its application. Also, ensure your data is accurate, consistent, and relevant.
• Use the right tools. Utilize solutions like Squadcast Analytics to perform an extensive analysis of past incidents at both the broad organizational level and the granular team level. The platform further allows you to dive deeper to single out system bottlenecks that generate the most alerts and outages, categorizing these by teams, tags, and services.

Incident management KPI best practice #3: Embrace feedback loops and continuous learning

When a KPI indicates a slowdown in incident resolution, the call to action is to essentially delve into it, understand the cause, and make the necessary adjustments. This feedback loop is essential for the continual refinement of your processes.

It is equally important that your team members are well-versed in interpreting KPIs. With each incident resolved, a new data set is added to the pool, providing additional opportunities to analyze and learn. Every cycle of this process represents an opportunity for improvement and a step closer to optimal efficiency and effectiveness.

While there is no thumb rule to promote this culture, consider adopting different strategies to enhance the continual learning environment:

• Use past incidents retrospectives and data to create what if, hypothetical scenarios. Dry runs like these can help the team understand how different actions can influence KPIs and outcomes in real situations.
• Involve your team in KPI development, understand why they're tracking specific metrics, and how they can refine KPIs further. This can give them a deeper understanding of why certain metrics are tracked and how they can influence them.

Incident management KPI best practice #4: Create benchmarks and conduct performance assessments

Compare KPIs with industry standards to see how your incident management stacks up against the competition. Benchmarking also lets you compare your incident management performance with best practices or historical data. This objective performance measurement can reveal your strengths and weaknesses, guiding your improvement efforts.

When interpreting benchmarks, your team size, resource allocation, and the complexity of incidents handled should all be factored in. It is also important to note that every organization has unique circumstances and goals. Ensure that industry averages are only seen as reference points, not absolute standards.

If real-time tracking of KPIs is crucial, utilize a dashboard like Squadcast’s Reliability Tracker to provide an instant snapshot of current performance against set KPIs and benchmarks. Whether you opt for a commercial off-the-shelf solution or a custom-built one, ensure your dashboard can offer a snapshot of the current KPI performance versus the industry benchmarks.

Conclusion

Enterprises often misunderstand the true value of KPIs, seeing them merely as numeric markers. In contrast, KPIs help with strategic analysis by highlighting patterns, identifying bottlenecks, and guiding improvement areas.

But before you start monitoring KPIs, it is worth noting that although they provide crucial data, KPIs can't always capture the intricacies of every operational nuance. This doesn't undermine the value of KPIs, though. Instead, this emphasizes the need to supplement them with other factors - your team's insights, situational understanding, the complexities of each incident, and a platform that helps you monitor your KPI performance efficiently.