What are It Team metrics?
Crafting the perfect It 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 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 Team metrics and KPIs
Metrics for Windows and VMWare Server Support
1. First Call Resolution Rate
Percentage of issues resolved on the first call without escalation
What good looks like for this metric: 70-80%
Ideas to improve this metric- Provide comprehensive training for team members
- Implement a robust knowledge base
- Utilise remote access tools for rapid solutions
- Ensure clear communication during ticket handling
- Optimise ticket categorisation for swift allocations
2. Average Ticket Resolution Time
Average time taken to resolve support tickets
What good looks like for this metric: Less than 24 hours
Ideas to improve this metric- Enhance prioritisation of critical tickets
- Streamline internal workflows
- Employ ticketing software with automation features
- Offer frequent skill-improving sessions
- Regularly review ticket handoff processes
3. Customer Satisfaction Score
Rating provided by customers after resolution
What good looks like for this metric: Above 90%
Ideas to improve this metric- Solicit feedback through post-service surveys
- Provide personalised support where applicable
- Encourage proactive follow-ups with customers
- Analyse feedback data to identify improvement areas
- Reward team members demonstrating high satisfaction scores
4. Ticket Backlog
Number of unresolved tickets over a specified time period
What good looks like for this metric: Less than 10% of total tickets
Ideas to improve this metric- Regularly audit ticket queue for overdue items
- Implement weekly backlog reviews
- Balance team workload to prevent overloads
- Develop standard operating procedures for common issues
- Encourage team accountability for assigned tickets
5. Service Uptime
Percentage of time the server environment is operational and available
What good looks like for this metric: 99.9%
Ideas to improve this metric- Conduct regular system health checks
- Implement proactive monitoring systems
- Establish a thorough disaster recovery plan
- Schedule routine maintenance during off-peak hours
- Ensure redundancy for critical systems
Metrics for IT Department Efficiency
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
Metrics for Monitor data growth accuracy
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
Metrics for Automation in Business Units
1. Process Automation Rate
Percentage of business processes currently automated compared to the total processes possible
What good looks like for this metric: 20-30%
Ideas to improve this metric- Conduct a thorough process audit
- Identify repetitive manual tasks
- Leverage robotic process automation tools
- Invest in staff training for technology adoption
- Establish clear automation goals
2. Time Saved Through Automation
Amount of time saved as a result of implementing automation in business processes
What good looks like for this metric: 10-50% time reduction
Ideas to improve this metric- Analyse current process time expenditures
- Prioritise automation of most time-consuming tasks
- Collaborate with departments to streamline processes
- Continuously measure time savings
- Optimise processes post-automation
3. Cost Reduction Due to Automation
Financial savings accrued from reducing manual labour and errors via automation
What good looks like for this metric: 15-30% cost reduction
Ideas to improve this metric- Assess cost structures before automation
- Focus on high-cost processes for automation
- Use cost-effective automation solutions
- Regularly evaluate cost savings
- Scale successful automation projects
4. Error Reduction Rate
Decrease in errors in business processes as a result of automation
What good looks like for this metric: 30-70% reduction in errors
Ideas to improve this metric- Identify error-prone processes
- Use high-accuracy automation technologies
- Implement continuous error monitoring systems
- Train employees on new automated systems
- Review error rates regularly to refine processes
5. Employee Satisfaction with Automation
Degree to which employees are satisfied with the automation tools and their impact on work
What good looks like for this metric: 70-85% satisfaction rate
Ideas to improve this metric- Conduct employee satisfaction surveys
- Provide comprehensive training sessions
- Encourage employee feedback on automation tools
- Create a support system for employees
- Highlight benefits of automation to employees
Metrics for Security and Compliance
1. Device Compliance Rate
Measures the percentage of devices that meet compliance requirements for security standards.
What good looks like for this metric: 95% compliance rate
Ideas to improve this metric- Conduct regular compliance audits
- Update security policies frequently
- Train employees on compliance requirements
- Automate compliance checks
- Use endpoint protection software
2. Threat Detection Time
The average time taken to detect a security threat on an end-user device.
What good looks like for this metric: Under 24 hours
Ideas to improve this metric- Implement real-time monitoring
- Utilise AI-powered threat detection tools
- Regularly update threat databases
- Conduct regular security tests
- Enable fast response procedures
3. Patch Management Timeliness
The average time taken to apply security patches to end-user devices.
What good looks like for this metric: Within 72 hours
Ideas to improve this metric- Automate patch deployment
- Schedule regular update checks
- Prioritise critical patches
- Maintain a patch inventory
- Verify patch installations regularly
4. Data Encryption Rate
The percentage of end-user devices that have encryption enabled for data storage.
What good looks like for this metric: 100% encryption rate
Ideas to improve this metric- Enforce encryption policies
- Provide encryption tools
- Train users on encryption benefits
- Audit encryption compliance
- Utilise full-disk encryption solutions
5. Incident Response Rate
Measures the effectiveness and speed of response when a security incident occurs.
What good looks like for this metric: 90% incidents resolved within 48 hours
Ideas to improve this metric- Establish a dedicated response team
- Develop a detailed incident response plan
- Run regular incident response drills
- Utilise automated incident detection systems
- Review response procedures post-incident
Metrics for Showcase Team Performance
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
Metrics for Service Health Evaluation
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
Metrics for Device Usage Analysis
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
Metrics for Handling Log Files
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
Metrics for Data Selection and Rule Development
1. Data Accuracy
Measures the percentage of data entries that are correct and error-free across the system
What good looks like for this metric: Above 95%
Ideas to improve this metric- Implement regular data audits
- Use automated data validation tools
- Provide staff training on data entry accuracy
- Establish clear data entry guidelines
- Enable error-detection algorithms
2. Data Completeness
Assesses the percentage of data records that are fully filled and not missing any critical fields
What good looks like for this metric: Above 90%
Ideas to improve this metric- Conduct routine completeness checks
- Utilise automated form filling
- Standardise data requirements
- Regularly review data input processes
- Incentivise complete data entry
3. Data Timeliness
Measures the speed at which data is updated or made available for processing
What good looks like for this metric: Within 24 hours
Ideas to improve this metric- Automate data update processes
- Set clear timelines for data entry
- Monitor data latency regularly
- Establish a data steward for timely updates
- Prioritise data updates during peak times
4. Data Consistency
Evaluates whether data is consistent across different databases and sources
What good looks like for this metric: Close to 100% consistency
Ideas to improve this metric- Implement cross-system data comparisons
- Use master data management tools
- Regularly review data transformation processes
- Ensure consistent data entry formats
- Provide training for consistent data handling
5. Data Relevance
Determines the degree to which data is relevant and useful for current business needs
What good looks like for this metric: Above 85% of data in use
Ideas to improve this metric- Regularly review and update data policies
- Conduct user feedback sessions
- Align data selection with business objectives
- Utilise data analytics to assess relevance
- Remove outdated or redundant data regularly
Metrics for Automate Billing Process
1. Process Time Reduction
Measures the reduction in time taken to complete the billing process after automation compared to before.
What good looks like for this metric: 30% reduction in process time
Ideas to improve this metric- Use programmable robotic process automation tools
- Integrate with existing accounting software
- Utilise cloud computing for scalability
- Streamline data entry with OCR technology
- Implement a database for template management
2. Error Rate in Billing
Assesses the percentage of billing errors post-automation compared to manual processes.
What good looks like for this metric: Under 2% error rate
Ideas to improve this metric- Implement validation checks
- Conduct regular training for software use
- Use automated reconciliation methods
- Regularly update software for accuracy
- Use real-time data error alerts
3. Cost Savings
Calculates the reduction in costs due to automating the billing process.
What good looks like for this metric: 20% cost reduction
Ideas to improve this metric- Optimise software licenses
- Reduce paper and printing expenses
- Automate repetitive tasks to reduce overtime
- Conduct a cost-benefit analysis regularly
- Invest in preventive maintenance
4. Employee Productivity
Measures the increase in productivity of employees as a result of reducing manual billing tasks.
What good looks like for this metric: 15% increase in productivity
Ideas to improve this metric- Reallocate time savings to skill development
- Schedule regular feedback sessions
- Use productivity tracking tools
- Encourage a culture of continuous improvement
- Provide resources for professional growth
5. Billing Accuracy Rate
Monitors the percentage of bills processed accurately after automation.
What good looks like for this metric: 98% accuracy rate
Ideas to improve this metric- Ensure comprehensive testing before full deployment
- Regular data audits
- Use machine learning for pattern recognition
- Integrate feedback loops for corrective action
- Keep detailed records of errors for analysis
Metrics for Empowering Innovation and Service Delivery
1. System Uptime
The percentage of time the infrastructure is operational and accessible to users.
What good looks like for this metric: 99.9%
Ideas to improve this metric- Implement redundancy systems
- Perform regular maintenance checks
- Upgrade hardware components
- Monitor using advanced tools
- Develop a disaster recovery plan
2. Service Response Time
The average time taken to respond to service requests or queries from users.
What good looks like for this metric: Less than 3 seconds
Ideas to improve this metric- Optimise server configurations
- Use load balancing techniques
- Increase bandwidth availability
- Implement caching strategies
- Enhance database management
3. User Satisfaction Score
A measure of user satisfaction collected through surveys and feedback forms.
What good looks like for this metric: Above 85%
Ideas to improve this metric- Conduct regular user feedback sessions
- Implement a user-friendly interface
- Deliver consistent customer support
- Analyse feedback for improvements
- Introduce regular updates based on suggestions
4. Innovation Adoption Rate
The percentage of new features or innovations adopted by users over time.
What good looks like for this metric: Above 60%
Ideas to improve this metric- Promote new features actively
- Provide training sessions for users
- Offer incentives for early adoption
- Simplify the onboarding process
- Use user testimonials to encourage uptake
5. Incident Resolution Time
The average time taken to resolve incidents or issues reported within the infrastructure.
What good looks like for this metric: Under 4 hours
Ideas to improve this metric- Maintain a knowledgeable support team
- Use automated incident detection
- Streamline the issue escalation process
- Maintain a robust incident management tool
- Review and refine resolution procedures
Metrics for Speed and Security Analysis
1. Latency
Time taken for a transaction or processing a fall event from the input to the final output
What good looks like for this metric: 200-500 milliseconds
Ideas to improve this metric- Optimize network bandwidth
- Utilise more efficient consensus algorithms
- Reduce data complexity in transactions
- Incorporate edge computing techniques
- Enhance processing speeds of nodes
2. Throughput
Number of transactions processed within a given period
What good looks like for this metric: 10-100 transactions per second
Ideas to improve this metric- Increase the number of nodes
- Upgrade node hardware
- Implement parallel processing techniques
- Optimize transaction validation methods
- Utilise sharding techniques
3. Security Breach Rate
Number of successful breaches attempts per month
What good looks like for this metric: 0-1 breach per year
Ideas to improve this metric- Regularly update encryption protocols
- Conduct routine security audits
- Implement multi-factor authentication
- Train staff on security awareness
- Utilise a robust incident response strategy
4. Scalability
Ability to maintain performance as network size or data volume increases
What good looks like for this metric: Linear performance degradation with scale
Ideas to improve this metric- Adopt more scalable consensus algorithms
- Reduce data redundancy
- Utilise cloud resources for storage
- Implement load balancing techniques
- Employ distributed ledger technology
5. Data Integrity
Accuracy and consistency of data over its lifecycle
What good looks like for this metric: 99.9% integrity rate
Ideas to improve this metric- Regularly verify data with hash functions
- Set permissions and roles for data access
- Utilise smart contracts for automatic validation
- Implement data replication strategies
- Conduct integrity checks at regular intervals
Metrics for Enhance Incident Response and Management
1. Mean Time to Resolve (MTTR)
Average time taken to resolve major incidents, calculated from the time the incident is reported until it is fully resolved
What good looks like for this metric: 2-4 hours
Ideas to improve this metric- Implement automated incident response tools
- Conduct regular training for incident response teams
- Refine incident categorisation and prioritisation processes
- Establish a dedicated major incident team
- Analyse past incidents to identify improvement areas
2. Major Incident Recurrence Rate
Percentage of major incidents that recur within a specific timeframe after resolution
What good looks like for this metric: Below 5%
Ideas to improve this metric- Conduct thorough root cause analysis
- Implement permanent fixes rather than temporary solutions
- Regularly review and update the incident management process
- Enhance collaboration between incident and problem management teams
- Utilise knowledge management to share solutions and prevent recurrence
3. Incident Resolution Quality
Quality of incident resolution measured through stakeholder feedback and post-incident reviews
What good looks like for this metric: Above 90% positive feedback
Ideas to improve this metric- Develop a clear incident resolution checklist
- Provide additional training on customer service skills
- Standardise post-incident review processes
- Gather and act on stakeholder feedback
- Implement continuous improvement initiatives
4. Stakeholder Communication Effectiveness
Effectiveness of communication with stakeholders during major incidents, measured through feedback and surveys
What good looks like for this metric: Above 80% satisfaction
Ideas to improve this metric- Establish a communication plan template
- Utilise multiple communication channels
- Train staff in effective communication techniques
- Regularly update stakeholders during incidents
- Review and refine communication strategies based on feedback
5. Incident Detection Time
Time taken to detect incidents from the moment they occur to the moment they are identified
What good looks like for this metric: Within 10 minutes
Ideas to improve this metric- Implement advanced monitoring and alerting systems
- Conduct regular audits of detection tools and processes
- Improve correlation of events and patterns
- Train staff to recognise potential incidents quickly
- Increase the frequency of system health checks
Metrics for Improve Investor Engagement
1. Investor Meeting Attendance Rate
The percentage of investors who attend scheduled meetings out of the total invited
What good looks like for this metric: 70-80%
Ideas to improve this metric- Increase meeting reminders and follow-ups
- Offer multiple time slots to accommodate different time zones
- Incorporate engaging presentation materials
- Provide clear and concise meeting agendas
- Utilise feedback to improve future meetings
2. Post-Meeting Investor Follow-Up Rate
The percentage of investors who engage in follow-up communication after a meeting
What good looks like for this metric: 50-60%
Ideas to improve this metric- Personalise follow-up emails
- Highlight key takeaways from meetings
- Offer additional data or insights discussed in meetings
- Address any investor queries promptly
- Schedule subsequent touchpoints in advance
3. Investor Email Open Rate
The percentage of investor-targeted emails that are opened by recipients
What good looks like for this metric: 15-25%
Ideas to improve this metric- Craft compelling subject lines
- Send emails at optimal times
- Segment audience to target messaging
- Reduce email frequency to avoid saturation
- Ensure emails are mobile-friendly
4. Investor Engagement Index
A composite score determining the overall engagement level based on various interactions
What good looks like for this metric: 60-75
Ideas to improve this metric- Foster personal relationships
- Enhance content relevancy
- Regularly update investors on progress
- Invite feedback and suggestions
- Utilise investor-exclusive updates
5. Investor Response Time
The average time taken by investors to respond to a communication
What good looks like for this metric: 24-48 hours
Ideas to improve this metric- Implement a clearer call-to-action
- Utilise CRM tools to track and enhance interactions
- Ensure prompt internal communication for quick responses
- Provide immediate value in communications
- Follow up on non-respondents
Tracking your It 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.
Tability's check-ins will save you hours and increase transparencyMore metrics recently published
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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:
- To learn: What are OKRs? The complete 2024 guide
- Blog posts: ODT Blog
- Success metrics: KPIs examples