What are System Administrator metrics? Crafting the perfect System Administrator metrics can feel overwhelming, particularly when you're juggling daily responsibilities. That's why we've put together a collection of examples to spark your inspiration.
Copy these examples into your preferred app, or you can also use Tability to keep yourself accountable.
Find System Administrator metrics with AI While we have some examples available, it's likely that you'll have specific scenarios that aren't covered here. You can use our free AI metrics generator below to generate your own strategies.
Examples of System Administrator metrics and KPIs 1. Uptime Percentage Measures the amount of time the service is up and running without interruptions. Calculated by dividing the total operational minutes by the total minutes in a period.
What good looks like for this metric: 99.9% or higher
Ideas to improve this metric Implement redundancy systems Use robust monitoring tools Conduct regular maintenance Train staff for quick incident response Opt for reliable service providers 2. Response Time The time it takes for the service to respond to a user action or request. Typically measured in milliseconds or seconds.
What good looks like for this metric: Less than 200ms
Ideas to improve this metric Optimize server configurations Use a content delivery network Streamline code and queries Enhance database performance Regularly audit application performance 3. Error Rate The percentage of failed requests in relation to the total number of service requests.
What good looks like for this metric: Less than 1%
Ideas to improve this metric Implement detailed logging Enhance debugging processes Regular code reviews Continuous service testing Deploy robust error handling 4. Customer Satisfaction Score (CSAT) A measurement derived from customer feedback focusing on satisfaction with the service, typically collected via surveys.
What good looks like for this metric: 80% or higher
Ideas to improve this metric Enhance user experience design Implement customer feedback loops Resolve issues promptly Provide user-friendly interfaces Conduct regular user training 5. Transaction Success Rate The percentage of successful transactions completed without any errors or failures.
What good looks like for this metric: 95% or higher
Ideas to improve this metric Optimize transactional workflow Enhance payment gateway reliability Continuously monitor transaction logs Implement strong authentication mechanisms Regularly update and test payment procedures
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1. Incident Response Time The average time it takes for the IT department to respond to an incident after it is reported.
What good looks like for this metric: 30 minutes to 1 hour
Ideas to improve this metric Implement automated alert systems Conduct regular training sessions Set up a 24/7 support team Streamline incident escalation processes Utilise incident management tools 2. First Contact Resolution Rate The percentage of IT issues resolved during the first contact with the user.
What good looks like for this metric: 70% to 80%
Ideas to improve this metric Enhance self-service tools and resources Improve knowledge base quality Conduct specialised training for support staff Implement a feedback loop for continuous improvement Use advanced diagnostic tools 3. System Uptime The percentage of time that IT systems are operational and available for use.
What good looks like for this metric: 99% to 99.9%
Ideas to improve this metric Regularly update and patch systems Implement high availability solutions Conduct regular system monitoring Perform routine maintenance checks Use redundant systems 4. User Satisfaction Score The average satisfaction rating given by users after IT services are provided.
What good looks like for this metric: 4.0 to 4.5 out of 5
Ideas to improve this metric Offer regular customer service training Obtain user feedback and act on it Enhance communication channels Implement a user-friendly ticketing system Provide regular updates to users 5. Mean Time to Repair (MTTR) The average time taken to fully repair an IT issue after it is reported.
What good looks like for this metric: 2 to 4 hours
Ideas to improve this metric Improve diagnostic procedures Use automated repair tools Maintain an updated inventory of spare parts Enhance collaboration between IT teams Conduct thorough post-incident reviews
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1. Data Processing Throughput Measures the amount of data processed successfully within a given time frame, typically in gigabytes per second (GB/s)
What good looks like for this metric: Varies by system but often >1 GB/s for high-performing systems
Ideas to improve this metric Increase hardware capabilities Optimise software algorithms Implement data compression techniques Use parallel processing Upgrade network infrastructure 2. Latency Time taken from input to desired data processing action, measured in milliseconds (ms)
What good looks like for this metric: <100 ms for high-performing systems
Ideas to improve this metric Enhance server response time Minimise data travel distance Optimise application code Utilise content delivery networks Implement load balancers 3. Error Rate Percentage of errors during data processing compared to total operations, measured as a %
What good looks like for this metric: <5% for acceptable performance
Ideas to improve this metric Implement error-handling codes Train systems with more robust datasets Regularly update software Conduct thorough system testing Improve data input validity checks 4. Disk I/O Rate Measures read and write operations per second on storage devices, expressed in IOPS (input/output operations per second)
What good looks like for this metric: >10,000 IOPS for SSDs, lower for HDDs
Ideas to improve this metric Upgrade to faster storage solutions Redistribute data loads Increase cache sizes Use faster file systems Optimise database queries 5. Resource Utilisation Percentage of CPU, memory, and network bandwidth being used, expressed as a %
What good looks like for this metric: 75-85% for efficient resource use
Ideas to improve this metric Perform regular system monitoring Distribute workloads more evenly Implement scalable cloud solutions Prioritise critical processes Utilise virtualisation
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1. Throughput Measures the number of log files processed per minute to ensure the service meets the 40k requirement
What good looks like for this metric: 40,000 log files per minute
Ideas to improve this metric Optimize log processing algorithms Upgrade server hardware Use a load balancer to distribute requests Implement batch processing for logs Minimize unnecessary logging 2. Latency Measures the time it takes to process each log file from receipt to completion
What good looks like for this metric: Less than 100 milliseconds
Ideas to improve this metric Streamline data pathways Prioritise real-time log processing Identify and remove processing bottlenecks Utilise caching mechanisms Optimize database queries 3. Error Rate Tracks the percentage of log files that are not processed correctly
What good looks like for this metric: Less than 1%
Ideas to improve this metric Implement robust error handling mechanisms Conduct regular integration tests Utilise validation before processing logs Enhance logging system for transparency Review and improve exception handling 4. Resource Utilisation Measures the use of CPU, memory, and network to ensure efficient handling of logs
What good looks like for this metric: Below 80% for CPU and memory utilisation
Ideas to improve this metric Optimize code for better performance Implement vertical or horizontal scaling Regularly monitor and adjust resource allocation Use lightweight libraries or frameworks Run performance diagnostics regularly 5. System Uptime Tracks the percentage of time the system is operational and able to handle log files
What good looks like for this metric: 99.9% uptime
Ideas to improve this metric Implement redundancies in infrastructure Schedule regular maintenance Monitor system health continuously Use reliable cloud services Establish quick recovery protocols
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1. Incident Response Time The average time taken by the team to respond to reported incidents
What good looks like for this metric: Less than 30 minutes
Ideas to improve this metric Implement automated alert systems Conduct regular training on incident management Set clear response time goals Prioritise incidents based on severity Review and analyse past response times for improvement 2. System Uptime The percentage of time systems are operational and available
What good looks like for this metric: 99.9% or above
Ideas to improve this metric Conduct regular system maintenance Implement redundancy solutions Perform load testing to understand capacity Monitor system health in real-time Establish a disaster recovery plan 3. User Satisfaction Score Survey score given by users based on their satisfaction with team support
What good looks like for this metric: 8 out of 10 or higher
Ideas to improve this metric Regularly survey users to gather feedback Implement a user-friendly ticketing system Ensure timely updates to users Provide training in customer service skills Analyse feedback and address common issues 4. Ticket Resolution Rate The percentage of tickets resolved within the agreed service level agreement (SLA)
What good looks like for this metric: 95% or higher
Ideas to improve this metric Establish clear SLAs for ticket resolution Use ticketing software to prioritise workload Encourage team collaboration on complex issues Track pending tickets and address bottlenecks Hold regular reviews on ticket performance 5. Change Success Rate The percentage of system changes that are successfully implemented without causing incidents
What good looks like for this metric: 90% or higher
Ideas to improve this metric Establish a change management process Conduct risk assessments before changes Communicate changes to all stakeholders Provide training on implementing changes Review and learn from failed changes
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1. Response Time The time taken for a system to respond to a request, typically measured in milliseconds.
What good looks like for this metric: 100-200 ms
Ideas to improve this metric Optimise database queries Use efficient algorithms Implement caching strategies Scale infrastructure Minimise network latency 2. Error Rate The percentage of requests that result in errors, such as 4xx or 5xx HTTP status codes.
What good looks like for this metric: Less than 1%
Ideas to improve this metric Improve input validation Conduct thorough testing Use error monitoring tools Implement robust exception handling Optimize API endpoints 3. Request Per Second (RPS) The number of requests the server can handle per second.
What good looks like for this metric: 1000-5000 RPS
Ideas to improve this metric Use load balancing Optimise server performance Increase concurrency Implement rate limiting Scale vertically and horizontally 4. CPU Utilisation The percentage of CPU resources used by the backend server.
What good looks like for this metric: 50-70%
Ideas to improve this metric Profile and optimise code Distribute workloads evenly Scale infrastructure Use efficient data structures Reduce computational complexity 5. Memory Usage The amount of memory consumed by the backend server.
What good looks like for this metric: Less than 85% of total memory
Ideas to improve this metric Identify and fix memory leaks Optimise data storage Use garbage collection Implement memory caching Scale infrastructure
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Tracking your System Administrator metrics Having a plan is one thing, sticking to it is another.
Setting good strategies is only the first challenge. The hard part is to avoid distractions and make sure that you commit to the plan. A simple weekly ritual will greatly increase the chances of success.
A tool like Tability can also help you by combining AI and goal-setting to keep you on track.
More metrics recently published We have more examples to help you below.
Planning resources OKRs are a great way to translate strategies into measurable goals. Here are a list of resources to help you adopt the OKR framework:
Tability's check-ins will save you hours and increase transparency
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