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Recommended Practices for Data Management and Quality

Media Article
Publisher : DNV

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This article outlines recommended practices for data management and quality, focusing on governance, validation, and continuous improvement. It emphasizes the importance of implementing robust data governance structures that ensure data integrity, accuracy, and accessibility. The article explores strategies for validating data at various stages of its lifecycle and suggests continuous improvement practices that help organizations maintain high data standards over time. By adhering to these recommended practices, organizations can ensure that their data management systems support effective decision-making and lead to better outcomes across sectors such as healthcare, development, and public policy.

Author(s) :

Karl John Pedersen

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Key topics

Data Science for social impact

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Technology for Social Change and Organisational Efficiency

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