How do data quality and data governance differ?

How do data quality and data governance differ?

• A. Data governance defines who can access data; data quality is not related.
• B. Data quality refers to how quickly data is captured; data governance is audits.
• C. Data quality is the accuracy, completeness, and timeliness of data; data governance is the framework of policies and roles ensuring data quality.
• D. Data governance and data quality perform the same function.

Answer: (C)

Data quality and data governance sit at different levels of managing data. Data quality refers to the condition of the data itself—the accuracy, completeness, and timeliness (and other attributes like consistency and validity) of the data values. It’s about how trustworthy the data is for its intended use. Data governance is the organizational framework that ensures those quality characteristics are achieved and maintained. It includes the policies, standards, processes, and roles (like data owners and data stewards) that define how data is defined, managed, secured, and used. Governance sets the rules for data quality, assigns responsibility, and provides the mechanisms to measure and improve it. So, data quality answers the question “is the data accurate, complete, and timely?” while data governance answers “how do we ensure that quality through policies, roles, and processes?” That’s why the best choice describes data quality as the data’s accuracy, completeness, and timeliness, and data governance as the framework of policies and roles that ensure that quality.

Data quality and data governance sit at different levels of managing data. Data quality refers to the condition of the data itself—the accuracy, completeness, and timeliness (and other attributes like consistency and validity) of the data values. It’s about how trustworthy the data is for its intended use.

Data governance is the organizational framework that ensures those quality characteristics are achieved and maintained. It includes the policies, standards, processes, and roles (like data owners and data stewards) that define how data is defined, managed, secured, and used. Governance sets the rules for data quality, assigns responsibility, and provides the mechanisms to measure and improve it.

So, data quality answers the question “is the data accurate, complete, and timely?” while data governance answers “how do we ensure that quality through policies, roles, and processes?” That’s why the best choice describes data quality as the data’s accuracy, completeness, and timeliness, and data governance as the framework of policies and roles that ensure that quality.