Data, Information and Knowledge Management

• Providing the appropriate level of data governance and stewarship
• Adopting standards human and machine interpretable formats
• Utilizing controlled terminology for integration and interoperability
• Ensuring that the data are accurate, accessible, complete, consistent, current, timely, precise, at the appropriate level of granularity, reliable, relevant, conforming, and understandable across all data-quality management domains
• Ensuring the consistent use of maps to internal and external standards and reference data
• Ensuring that system architecture supports data interchange
• Ensuring that data, information, and knowledge are audited, measured, and evaluated for effectiveness
• Ensuring that data, information, and knowledge assets are validated, integrated, normalized, consolidated, and routinely optimized
• Developing infrastructure for knowledge, metadata, and terminology management
• Ensuring that information is readily and rapidly understood and accessed within the workflow
• Ensuring that information and knowledge are centrally managed collaboratively developed, and easily disseminated and maintained
• Ensuring that information and knowledge are platform independent
• Developing tools to effectively maintain and manage data, information, and knowledge