What process is oriented towards defect prevention in data management?

Quality assurance is a systematic approach focused on preventing defects in data management processes. This is achieved by establishing a set of processes, procedures, and standards that guide data handling practices to ensure that high-quality data is produced from the beginning. Unlike methods that identify and correct issues after they arise, quality assurance emphasizes proactive measures and continuous improvement of processes to avoid defects altogether.

This preventive focus involves designing workflows that minimize the chance of errors, providing training for data handlers, and implementing rigorous protocols that guide data entry, processing, and analysis. As a result, quality assurance not only enhances the quality of the data but also increases overall efficiency in operations, ultimately leading to better decision-making and improved outcomes.

In contrast, data quality control typically focuses on the inspection and testing of data after it has been created, while data validation confirms that data meets specific criteria or standards at a given point in time. Regular auditing is an important practice for assessing compliance and quality of existing data but does not inherently incorporate preventive measures. Hence, quality assurance stands out as the process aimed at defect prevention.