Our organization has new senior leadership that wants every department, including IT, to define performance KPIs. We are having trouble finding metrics that reflect the quality and reliability of our work rather than the volume generated by other teams. Ticket counts, security review counts, and similar numbers seem easy to inflate and do not necessarily show value.
We considered average ticket resolution time, but many of our tickets are used for long-term tracking and should remain open. We also do not want to reward people for closing tickets prematurely just to improve the numbers. Our team provides a mix of help desk and advanced support at a major research university, handling everything from routine administrative issues to highly specialized problems involving laboratory instruments, high-speed data acquisition, networking, and large research storage systems.
What KPIs would give leadership a useful picture of IT performance while accounting for service quality, reliability, security, customer experience, and the highly variable nature of our work?
5 Answers
Start with team- or service-level outcomes rather than individual productivity. Useful measures include availability of important systems, percentage of incidents meeting agreed SLAs, time to first human response, customer satisfaction, patch compliance, and the rate of recurring incidents. Reopened tickets can also indicate poor-quality resolutions, as long as they are interpreted alongside ticket complexity and not used as a standalone target.
Keep the executive dashboard small—perhaps four or five metrics. A balanced set could be system availability, SLA performance, customer satisfaction, security remediation compliance, and change or incident quality. Define the targets with the business first, such as the acceptable outage window or the required response time for each priority. KPIs should show control and improvement, not become quotas for closing work.
Your concern about ticket volume and resolution time is valid because both can be gamed. Better reliability and quality measures include change failure rate, mean time to recover from incidents, recurring incidents within a defined period, and the percentage of critical vulnerabilities remediated within policy. These show whether IT is preventing and recovering from problems effectively, rather than simply processing more tickets.
For a team handling unusual research systems, individual ticket metrics will miss a lot of the value. Consider tracking service health, reductions in recurring alerts after corrective work, automation delivered, technical-debt reduction, supported systems kept current, and documented root-cause analysis after major incidents. The metric should recognize solving difficult problems properly, even when there are only a few of them.
For projects and operational changes, track deployment success rate, rollback or hotfix frequency, delivery against agreed milestones, and budget or scope variance. It can also be helpful to measure forecast accuracy: if work was estimated to take ten days, how close was the actual delivery time? That discourages people from making deliberately pessimistic estimates just to appear early.
Exactly. Otherwise you get the classic situation where someone says a job will take three weeks, finishes it in three days, and looks much better than someone who gave an honest estimate.
Customer satisfaction is especially useful as a counterbalance to SLA numbers. A ticket can technically meet its SLA and still leave the user frustrated if the communication or solution was poor.