Which principle addresses the need for data quality and correction of flaws?

The Data Quality Principle is fundamental in ensuring that data is accurate, relevant, and maintained with a high standard of integrity. This principle specifically emphasizes the importance of collectivity and maintaining the accuracy of data. It mandates that organizations must not only strive to gather high-quality data but also take proactive measures to correct any flaws or inaccuracies that may emerge over time.

This principle is vital because effective decision-making and compliance with regulations heavily depend on the trustworthiness of data. Poor data quality can lead to incorrect conclusions, ineffective strategies, and, ultimately, harm to an organization’s reputation and operational effectiveness. Thus, adhering to the Data Quality Principle strengthens data governance practices, ensuring that the information at hand serves its intended purpose effectively.

The other principles, while important in their own rights—such as the Collection Limitation Principle, which focuses on the limited collection of data; the Use Limitation Principle, which addresses how data should be used sparingly; and the Security Safeguards Principle, which ensures proper measures are in place to protect data—do not specifically target the rectification and ongoing quality control of data itself like the Data Quality Principle does.