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Establishing robust data foundations is crucial for companies looking to leverage artificial intelligence (AI). Business leaders emphasize that a well-defined data strategy is essential to avoid poor outcomes from AI initiatives. The article outlines three key strategies for creating effective data strategies that support AI integration.
The first strategy focuses on prioritizing people and ensuring data quality through collaboration between data and IT teams. The second highlights the importance of mastering transactional data to create a reliable source of truth. Lastly, working with industry peers fosters a cohesive approach to data management, enhancing the overall effectiveness of AI applications.
• Strong data foundations are essential for successful AI integration.
• Collaboration between data and IT teams enhances data quality.
• Mastering transactional data creates a reliable source of truth.
• Industry collaboration improves data management across sectors.
A data strategy outlines how data is managed and utilized to support business goals.
Transactional data refers to the data generated from business transactions, serving as a source of truth.
Digital transformation involves integrating digital technology into all areas of a business.
APIs enable different software applications to communicate and share data effectively.
L&G focuses on data governance and quality to enhance customer experiences through AI.
DWF utilizes cloud-based SaaS products and APIs to master transactional data for AI applications.
NSTA collaborates across sectors to create a cohesive digital data strategy for the oil and gas industry.
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