What type of procedures are designed to improve quality in data management?

Quality control procedures are specifically designed to ensure that data management processes produce high-quality, accurate, and reliable data. These procedures involve the application of systematic evaluations and checks at various stages of data management to identify errors or discrepancies, assess performance, and implement corrective actions. By focusing on evaluating the outputs and maintaining the integrity of data, quality control is vital for meeting established standards and ensuring that the data collected is valid and useful for analysis.

While quality assurance procedures also play a role in improving overall data quality, they focus more on the processes and systems to prevent errors before they occur. Quality assurance ensures that the standards, practices, and processes are in place to promote good data management and reduce the likelihood of issues arising. However, the specific emphasis on controlling data quality through systematic checks and corrections is what defines quality control procedures.

Given that the question focuses on procedures that improve data management quality, the emphasis on actively managing and rectifying issues aligns particularly well with the principles of quality control.