What are It Infrastructure Team metrics? Crafting the perfect It Infrastructure Team 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 It Infrastructure Team 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 It Infrastructure Team metrics and KPIs 1. Total Data Volume The total amount of data stored in a database or system, measured in gigabytes or terabytes
What good looks like for this metric: Evaluated monthly; varies by industry
Ideas to improve this metric Regularly audit stored data Use data compression techniques Implement data archiving policies Evaluate data storage solutions Automate data clean-up processes 2. Growth Rate of Data Volume The percentage increase in data over a specific period, typically month-over-month
What good looks like for this metric: Generally should not exceed 5% monthly
Ideas to improve this metric Review data input processes Set growth targets Analyse growth trends Identify unnecessary data accumulation Implement stricter data entry policies 3. Percentage of Duplicate Records The proportion of records that appear more than once in a database
What good looks like for this metric: Aim for less than 1% duplication
Ideas to improve this metric Use data deduplication tools Standardise data entry fields Conduct regular data audits Train staff on data entry Implement unique identifiers 4. Data Accuracy Rate The percentage of data that is correct and free from error
What good looks like for this metric: Should be above 95%
Ideas to improve this metric Conduct regular data quality checks Provide data entry training Utilise automated validation tools Standardise data formats Implement error logging 5. Record Completeness Rate The percentage of records that have all required fields filled out
What good looks like for this metric: Should remain above 90%
Ideas to improve this metric Ensure all required fields are filled Review and update data entry templates Implement data input checks Improve user data input interfaces Incentivise complete data entry
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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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Tracking your It Infrastructure Team metrics Having a plan is one thing, sticking to it is another.
Having a good strategy is only half the effort. You'll increase significantly your chances of success if you commit to a weekly check-in process .
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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