What aspect of data quality does 'accuracy' pertain to?

What aspect of data quality does 'accuracy' pertain to?

• A. Data completeness
• B. Data representation of real-life entities
• C. Data accessibility
• D. Data usability

Answer: (B)

Accuracy in data quality refers specifically to how closely data values represent the real-life entities or concepts they are intended to reflect. This means that accurate data provides a true and reliable depiction of the information it is supposed to convey. For instance, if a database contains customer addresses, the accuracy of these records would depend on whether they match the actual locations of the customers. When assessing data quality, accuracy is a crucial component because if the data does not accurately represent the real world, decisions based on that data can lead to erroneous conclusions and actions. Ensuring data accuracy is fundamental for organizations that rely on data-driven insights to operate effectively. In contrast, the other options look at various dimensions of data quality that don't define accuracy. Completeness refers to whether all necessary data is present; accessibility deals with how easily data can be retrieved and used; and usability focuses on the ease with which data can be utilized in tasks or decision-making. While these aspects are important, they do not specifically address the correctness of data in relation to real-life entities, which is the essence of accuracy.

Accuracy in data quality refers specifically to how closely data values represent the real-life entities or concepts they are intended to reflect. This means that accurate data provides a true and reliable depiction of the information it is supposed to convey. For instance, if a database contains customer addresses, the accuracy of these records would depend on whether they match the actual locations of the customers.

When assessing data quality, accuracy is a crucial component because if the data does not accurately represent the real world, decisions based on that data can lead to erroneous conclusions and actions. Ensuring data accuracy is fundamental for organizations that rely on data-driven insights to operate effectively.

In contrast, the other options look at various dimensions of data quality that don't define accuracy. Completeness refers to whether all necessary data is present; accessibility deals with how easily data can be retrieved and used; and usability focuses on the ease with which data can be utilized in tasks or decision-making. While these aspects are important, they do not specifically address the correctness of data in relation to real-life entities, which is the essence of accuracy.