What are Data Governance metrics? Identifying the optimal Data Governance metrics can be challenging, especially when everyday tasks consume your time. To help you, we've assembled a list of examples to ignite your creativity.
Copy these examples into your preferred app, or you can also use Tability to keep yourself accountable.
Find Data Governance 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 Data Governance metrics and KPIs 1. Data quality score Represents the accuracy, completeness, and reliability of data. Calculated by evaluating data against predefined quality criteria.
What good looks like for this metric: 95% or higher
Ideas to improve this metric Implement data validation rules Conduct regular data quality audits Utilise data cleansing tools Ensure consistent data entry procedures Provide regular training for data handlers 2. Compliance rate Measures the percentage of data processes in compliance with relevant regulations and policies.
What good looks like for this metric: 98% or higher
Ideas to improve this metric Establish clear data governance policies Regularly review and update compliance guidelines Implement automated compliance monitoring tools Conduct periodic compliance training Schedule regular internal audits 3. Data breach incidents Tracks the number of data breaches or security incidents within a specified period.
What good looks like for this metric: Zero breaches
Ideas to improve this metric Strengthen data security protocols Conduct regular vulnerability assessments Use encryption for sensitive data Implement multi-factor authentication Train employees on security best practices 4. Data access control Measures the effectiveness of access controls by tracking unauthorised access attempts.
What good looks like for this metric: Less than 2% unauthorised attempts
Ideas to improve this metric Regularly review and update access control policies Implement role-based access control Monitor and log access attempts Conduct regular access audits Use secure authentication methods 5. Data retention adherence Assesses how closely data retention practices align with data governance policies.
What good looks like for this metric: 100% adherence
Ideas to improve this metric Develop and communicate clear data retention policies Implement automated data retention tools Regularly review data retention schedules Conduct training on data retention practices Monitor and enforce compliance with retention policies
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1. Data Accuracy This measures how often the data in the dataset is correct and reliable
What good looks like for this metric: Typically around 95% accuracy
Ideas to improve this metric Implement data validation checks Conduct regular audits Train staff on data entry standards Automate error reporting Create a feedback loop for corrections 2. Data Completeness This assesses the extent to which all required data is available within the dataset
What good looks like for this metric: Ideal benchmark is 100% completeness
Ideas to improve this metric Identify required data fields and ensure they are collected Use mandatory fields in data entry forms Conduct gap analysis regularly Educate data providers on requirements Implement systems for data capture automation 3. Data Consistency Measures how uniformly the same data is recorded across the dataset
What good looks like for this metric: Aim for 100% consistency
Ideas to improve this metric Standardise data entry procedures Use consistent formats (e.g., date format) Analyse and resolve discrepancies Provide training on consistency importance Establish a single source of truth 4. Data Timeliness Assesses whether the data is up-to-date and available when needed
What good looks like for this metric: Data should be updated daily or in real-time
Ideas to improve this metric Define clear timelines for data updates Use automated data upload mechanisms Ensure prompt data entry by staff Monitor data update times Provide alerts for stale data 5. Data Accessibility Evaluates the ease with which data can be accessed and utilised by authorized personnel
What good looks like for this metric: 95% of users should be able to access needed data without issue
Ideas to improve this metric Implement role-based access control Ensure systems are user-friendly Provide training on data retrieval methods Use data catalogues for easy search Regularly test access protocols
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1. Migration Time Time taken to migrate network infrastructure and services to the isolated network environment
What good looks like for this metric: Less than or equal to 1 day
Ideas to improve this metric Streamline migration processes Use automation tools to reduce manual work Conduct trial runs to identify potential issues Provide adequate training to the migration team Develop clear migration documentation 2. Platform Services in Isolated Network Percentage of platform services successfully transferred to an isolated network environment
What good looks like for this metric: 100% of services
Ideas to improve this metric Create a detailed list of all platform services Use project management tools for tracking migration progress Allocate dedicated resources for network transition tasks Regular audit and feedback sessions Implement a tracking dashboard for stakeholders 3. Domain Services Migration Number of domain-specific services migrated to the isolated network
What good looks like for this metric: At least 2 services
Ideas to improve this metric Identify domain services with highest value impact Establish priorities based on current dependencies Develop an incremental migration plan Test services in the isolated environment iteratively Foster collaboration between domain and platform teams 4. Observability Noise Reduction Rate of noise reduction and signal improvement in observability tools
What good looks like for this metric: Annually decreased noise with improved signals
Ideas to improve this metric Adopt AI-based noise reduction solutions Regularly review and update monitoring configurations Implement automated alert tuning mechanisms Conduct team workshops on effective data interpretation Focus on continuous learning from alert incidents 5. Disaster Recovery Time Total time required to complete the disaster recovery process
What good looks like for this metric: Complete within 2 days
Ideas to improve this metric Increase automation in backup/recovery processes Develop efficient recovery scripts and workflows Invest in better infrastructure for faster processing Establish regular disaster recovery drills Monitor and optimise AWS usage to reduce limitations
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Tracking your Data Governance metrics Having a plan is one thing, sticking to it is another.
Don't fall into the set-and-forget trap. It is important to adopt a weekly check-in process to keep your strategy agile – otherwise this is nothing more than a reporting exercise.
A tool like Tability can also help you by combining AI and goal-setting to keep you on track.
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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:
Tability's check-ins will save you hours and increase transparency
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