What are the core components of data governance essential to CPMAI project success?

What are the core components of data governance essential to CPMAI project success?

• A. Data quality, lineage, privacy, security, access controls, and stewardship policies
• B. User interface design, performance metrics, load testing, and deployment frequency
• C. Budgeting, scheduling, risk logs, and stakeholder maps
• D. Model selection, hyperparameters, evaluation metrics, and drift monitoring

Answer: (A)

Data governance for AI projects hinges on establishing trustworthy data through six interconnected elements: data quality, lineage, privacy, security, access controls, and stewardship policies. Data quality sets the standards for accuracy, completeness, consistency, and timeliness, ensuring the AI system learns from reliable information and that outputs remain dependable. Data lineage provides a clear record of where data came from, how it was transformed, and who touched it, enabling reproducibility, debugging, and auditability—vital for regulatory checks and model risk management. Privacy safeguards protect personal data and uphold compliance with laws and ethical norms, while security measures defend data from unauthorized access and breaches. Access controls restrict who can view or modify data based on roles and need-to-know, maintaining control over sensitive assets. Stewardship policies assign responsibility for data assets, creating accountability and ongoing governance, including data definitions, quality rules, and remediation processes. Together, these components give CPMAI projects a solid foundation for trustworthy, auditable, and compliant data use, which is essential for successful AI delivery. The other options describe aspects of product development, testing, or project execution rather than the governance framework that underpins reliable AI work.

Data governance for AI projects hinges on establishing trustworthy data through six interconnected elements: data quality, lineage, privacy, security, access controls, and stewardship policies. Data quality sets the standards for accuracy, completeness, consistency, and timeliness, ensuring the AI system learns from reliable information and that outputs remain dependable. Data lineage provides a clear record of where data came from, how it was transformed, and who touched it, enabling reproducibility, debugging, and auditability—vital for regulatory checks and model risk management. Privacy safeguards protect personal data and uphold compliance with laws and ethical norms, while security measures defend data from unauthorized access and breaches. Access controls restrict who can view or modify data based on roles and need-to-know, maintaining control over sensitive assets. Stewardship policies assign responsibility for data assets, creating accountability and ongoing governance, including data definitions, quality rules, and remediation processes. Together, these components give CPMAI projects a solid foundation for trustworthy, auditable, and compliant data use, which is essential for successful AI delivery. The other options describe aspects of product development, testing, or project execution rather than the governance framework that underpins reliable AI work.