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Establishing a robust data foundation is crucial for companies looking to leverage artificial intelligence (AI). Business leaders emphasize that without a solid data strategy, AI projects risk failure, as poor data quality leads to ineffective outcomes. Key strategies include prioritizing people, mastering transactional data, and collaborating with industry peers.
Claire Thompson from L&G highlights the importance of governance and data quality in driving value from AI initiatives. Jon Grainger of DWF stresses the need for a well-defined data strategy that incorporates cloud-based solutions and APIs to ensure accurate transactional data. Nic Granger from NSTA advocates for cross-sector collaboration to enhance data accessibility and quality across the oil and gas industry.
• Strong data foundations are essential for successful AI implementation.
• Collaboration across sectors enhances data quality and accessibility.
It is critical for ensuring that data used in AI systems is accurate and reliable.
Mastering this data is essential for effective AI applications.
They are vital for integrating various data sources and ensuring seamless data flow in AI systems.
The company emphasizes the importance of data governance in enhancing customer experiences through AI.
The firm is implementing a data strategy that incorporates cloud-based solutions and APIs to enhance operational efficiency.
The organization promotes collaboration across industries to improve data quality and accessibility for AI applications.
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