Evaluate your organization's data management capabilities across five key areas. Answer all questions to receive a comprehensive maturity assessment with actionable recommendations.
Not Started - No formal data strategy exists
Basic - Informal data strategy with limited documentation
Developing - Documented data strategy with some alignment to business goals
Mature - Comprehensive data strategy aligned with business objectives
Advanced - Data strategy is fully integrated with business strategy and regularly updated
Not Started - No formal prioritization process
Basic - Ad-hoc prioritization based on immediate needs
Developing - Some criteria for prioritizing data projects
Mature - Clear prioritization framework based on business value
Advanced - Dynamic prioritization with regular review and adjustment
Not Started - No clear connection between data investments and business outcomes
Basic - Limited understanding of data investment impact
Developing - Some tracking of data investment ROI
Mature - Clear measurement of data investment business impact
Advanced - Continuous optimization of data investments based on business outcomes
Not Started - No formal data governance in place
Basic - Informal data governance with limited policies
Developing - Basic governance framework with some documented policies
Mature - Comprehensive governance framework with clear roles and responsibilities
Advanced - Mature governance with automated compliance monitoring and continuous improvement
Not Started - No defined data ownership or stewardship
Basic - Informal data ownership with unclear responsibilities
Developing - Some data owners identified with basic responsibilities
Mature - Clear data ownership and stewardship roles across the organization
Advanced - Active data stewardship community with regular training and accountability
Not Started - No formal data privacy or compliance measures
Basic - Basic awareness of privacy requirements with minimal controls
Developing - Some privacy controls and compliance processes in place
Mature - Comprehensive privacy and compliance framework with regular audits
Advanced - Proactive privacy-by-design approach with automated compliance monitoring
Not Started - No formal data quality monitoring
Basic - Manual data quality checks on an ad-hoc basis
Developing - Some automated data quality monitoring for critical datasets
Mature - Comprehensive data quality framework with regular monitoring
Advanced - Proactive data quality management with predictive quality analytics
Not Started - No systematic approach to identifying quality issues
Basic - Quality issues identified reactively when problems occur
Developing - Some proactive quality monitoring with basic remediation
Mature - Systematic quality issue identification with defined resolution processes
Advanced - Automated quality issue detection and resolution with root cause analysis
Not Started - No data standards or inconsistent data formats
Basic - Limited standardization with multiple data formats
Developing - Some data standards defined for key datasets
Mature - Comprehensive data standards with consistent implementation
Advanced - Enterprise-wide data standards with automated enforcement
Not Started - Legacy systems with no integrated data architecture
Basic - Basic data storage with limited integration capabilities
Developing - Some modern data infrastructure with basic integration
Mature - Scalable data architecture supporting most business needs
Advanced - Cloud-native, flexible architecture with real-time capabilities
Not Started - Data is siloed and difficult to access
Basic - Limited data access through manual processes
Developing - Some self-service capabilities for basic data access
Mature - Good data accessibility with self-service analytics tools
Advanced - Seamless data access with advanced analytics and AI capabilities
Not Started - Manual data integration with significant delays
Basic - Basic ETL processes with limited automation
Developing - Some automated data pipelines for key processes
Mature - Comprehensive data integration platform with good automation
Advanced - Real-time data processing with advanced pipeline orchestration
Not Started - Decisions are primarily based on intuition or experience
Basic - Some use of data in decision making but not systematic
Developing - Data is regularly used to support key decisions
Mature - Data-driven decision making is the norm across most functions
Advanced - Advanced analytics and AI are embedded in decision processes
Not Started - Limited data literacy with few people comfortable with data
Basic - Basic data literacy in some roles but not widespread
Developing - Growing data literacy with some training programs
Mature - Good data literacy across most roles with regular training
Advanced - High data literacy organization-wide with advanced analytics skills
Not Started - Data is rarely shared between departments
Basic - Limited data sharing with significant barriers
Developing - Some data sharing initiatives with mixed success
Mature - Good data sharing culture with collaborative platforms
Advanced - Seamless data collaboration with strong sharing incentives
Not Started - Limited leadership awareness or support for data initiatives
Basic - Some leadership interest but limited active support
Developing - Growing leadership support with some investment
Mature - Strong leadership support with adequate resources and sponsorship
Advanced - Leadership champions data-driven culture with significant investment