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Why Data Is the Unsung Hero of AI Strategy
- AI adoption in enterprises is transitioning from pilots/experiments to strategic enterprise-scale integration.
- Data is a critical component for contextual, intelligent, and enterprise-specific AI models.
- AI's economic impact is projected to reach $20 trillion by 2030, largely influenced by investments in data and infrastructure.
- Biased, outdated, or poor-quality data can lead to ineffective AI outcomes, making data the foundation of AI strategy.
- The success of scaling AI depends on building trust in data and leveraging it strategically.
- Key considerations for preparing data for AI strategy include reusing existing data assets, metadata/data lineage, governance/compliance, master data, and data value.
- Metadata and data lineage are crucial for AI governance and scalability.
- Master data should serve as the foundation for AI strategies to ensure completeness and accuracy.
- Data should be viewed in terms of its value contribution to AI and the business, rather than just a cost center.
- Emphasizing the enduring importance of data over AI models, it's essential to prioritize data readiness when crafting an AI strategy.
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