This summary provides a streamlined version of the comprehensive checklist, highlighting key actions for preparing data and infrastructure for AI and advanced analytics in the pharmaceutical industry.

1. Data Governance and Management

• Define AI and analytics objectives.
• Establish data ownership and stewardship.
• Implement data privacy, security, and quality standards.

2. Data Collection and Integration

• Identify relevant data sources (clinical trials, R&D, etc.).
• Ensure accurate data collection and automate where possible.
• Integrate data sources into a unified system.

3. Data Quality and Validation

• Clean data by removing duplicates and correcting errors.
• Validate data through regular checks and audits.

4. Data Storage and Management

• Use scalable, secure storage solutions.
• Implement robust data management tools and metadata management.

5. Data Security and Privacy

• Encrypt data and implement access controls.
• Anonymize patient data and comply with regulations (HIPAA, GDPR).

6. Data Analysis and AI Readiness

• Prepare data for analysis and create subsets for AI training.
• Choose appropriate AI and analytics tools.
• Ensure computational resources are adequate.

7. Model Development and Validation

• Develop models using suitable AI/ML algorithms.
• Validate models using cross-validation and monitor performance.

8. Regulatory Compliance and Ethics

• Ensure compliance with regulatory bodies (FDA, EMA).
• Address ethical implications and ensure transparency in AI decisions.

9. Training and Change Management

• Train staff on new tools and processes.
• Develop a change management strategy to handle transitions.

10. Continuous Improvement

• Establish feedback mechanisms for continuous improvement.
• Encourage innovation and stay updated with AI advancements.

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