What best describes data quality management?

What best describes data quality management?

• A. Storing data without setting standards
• B. Managing data only during storage
• C. Setting standards and measuring data against them
• D. Monitoring data after issues arise

Answer: (C)

Data quality management involves establishing standards for data and continuously measuring data against those established criteria to ensure accuracy, completeness, consistency, reliability, and timeliness. This proactive approach not only sets a clear benchmark for the quality of data, but it also enables organizations to identify any deviations from these standards promptly. By implementing data quality management practices, organizations can enhance data integrity and make more informed decisions based on reliable information. It ensures that data aligns with business rules and requirements, ultimately leading to better data-driven outcomes. In contrast, simply storing data without setting standards fails to address the fundamental aspects of data quality; it can lead to inconsistencies and inaccuracies. Managing data only during storage ignores the necessity of ongoing evaluation and improvement of data quality throughout its lifecycle. Lastly, monitoring after issues arise is a reactive approach that does not prevent problems from occurring, therefore limiting the effectiveness of data quality management strategies.

Data quality management involves establishing standards for data and continuously measuring data against those established criteria to ensure accuracy, completeness, consistency, reliability, and timeliness. This proactive approach not only sets a clear benchmark for the quality of data, but it also enables organizations to identify any deviations from these standards promptly.

By implementing data quality management practices, organizations can enhance data integrity and make more informed decisions based on reliable information. It ensures that data aligns with business rules and requirements, ultimately leading to better data-driven outcomes.

In contrast, simply storing data without setting standards fails to address the fundamental aspects of data quality; it can lead to inconsistencies and inaccuracies. Managing data only during storage ignores the necessity of ongoing evaluation and improvement of data quality throughout its lifecycle. Lastly, monitoring after issues arise is a reactive approach that does not prevent problems from occurring, therefore limiting the effectiveness of data quality management strategies.