Prepare your data architecture for the demands of generative AI

This infographic highlights challenges in preparing data architectures for generative AI. Key concerns include:

• Quality of Data (42%): Ensuring accuracy and reliability
• Data Privacy and Protection (40%): Safeguarding sensitive information
• AI Ethics (38%): Addressing ethical considerations
• Domain-specific Training Data (38%): Obtaining specialized data for fine-tuning models
• AI Governance (38%): Establishing responsible AI frameworks

These percentages likely show the proportion of organizations identifying each factor as a challenge. Modernizing data architectures requires attention to these elements for AI-ready environments.

Review the infographic for insights into GenAI success.