In what way does 'connected to SLA' relate to data quality governance?

In what way does 'connected to SLA' relate to data quality governance?

• A. It promotes customer feedback initiatives
• B. It defines the relationship between service levels and data quality standards
• C. It reduces data processing times significantly
• D. It enhances the visual representation of data

Answer: (B)

The option regarding the relationship between service levels and data quality standards is correct because it directly addresses the integral framework of data quality governance. Service Level Agreements (SLAs) outline the expected performance and quality metrics for data management activities. By establishing clear expectations and standards within SLAs, organizations can ensure that the quality of data aligns with operational goals and stakeholder requirements. SLA connections to data quality governance serve to codify the agreed-upon standards for data accuracy, consistency, completeness, and timeliness, making it clear what is required to meet business objectives. This structured approach to governance ensures that data management practices not only achieve compliance but also foster continual improvement in data quality, thereby increasing trust and reliability in data for decision-making processes. The other options do not encapsulate this specific relationship. For instance, customer feedback initiatives focus on user input rather than the governance structure itself, while reducing processing times relates to efficiency rather than quality standards. Similarly, enhancing visual representation pertains more to data presentation than to the governance frameworks underlying data quality.

The option regarding the relationship between service levels and data quality standards is correct because it directly addresses the integral framework of data quality governance. Service Level Agreements (SLAs) outline the expected performance and quality metrics for data management activities. By establishing clear expectations and standards within SLAs, organizations can ensure that the quality of data aligns with operational goals and stakeholder requirements.

SLA connections to data quality governance serve to codify the agreed-upon standards for data accuracy, consistency, completeness, and timeliness, making it clear what is required to meet business objectives. This structured approach to governance ensures that data management practices not only achieve compliance but also foster continual improvement in data quality, thereby increasing trust and reliability in data for decision-making processes.

The other options do not encapsulate this specific relationship. For instance, customer feedback initiatives focus on user input rather than the governance structure itself, while reducing processing times relates to efficiency rather than quality standards. Similarly, enhancing visual representation pertains more to data presentation than to the governance frameworks underlying data quality.