How are data quality standards typically defined?

How are data quality standards typically defined?

• A. By consensus among users
• B. By a governing authority
• C. By individual departments
• D. On an ad-hoc basis

Answer: (B)

Data quality standards are generally established by a governing authority because such an authority typically has the expertise, resources, and overall perspective necessary to create comprehensive standards that can be uniformly applied across an organization or industry. This centralized approach helps ensure that the standards take into account diverse requirements, compliance regulations, and industry best practices. When a governing authority sets data quality standards, those standards are often formalized and documented, thus providing a basis for consistency and reliability in data management processes. This ensures that data quality is not left to individual interpretation, which can lead to variations and potentially compromise data integrity. In contrast, defining data quality standards by consensus among users may lead to a lack of clarity and uniformity, as each user may have different opinions or needs. When established by individual departments, the resulting standards may not align with broader organizational goals, potentially creating silos. Ad-hoc approaches can result in inconsistencies and unreliable data quality, as they lack a structured method for evaluation and enforcement of the standards.

Data quality standards are generally established by a governing authority because such an authority typically has the expertise, resources, and overall perspective necessary to create comprehensive standards that can be uniformly applied across an organization or industry. This centralized approach helps ensure that the standards take into account diverse requirements, compliance regulations, and industry best practices.

When a governing authority sets data quality standards, those standards are often formalized and documented, thus providing a basis for consistency and reliability in data management processes. This ensures that data quality is not left to individual interpretation, which can lead to variations and potentially compromise data integrity.

In contrast, defining data quality standards by consensus among users may lead to a lack of clarity and uniformity, as each user may have different opinions or needs. When established by individual departments, the resulting standards may not align with broader organizational goals, potentially creating silos. Ad-hoc approaches can result in inconsistencies and unreliable data quality, as they lack a structured method for evaluation and enforcement of the standards.