Precision in data quality refers to which of the following?

Precision in data quality refers to which of the following?

• A. Exactness of data
• B. Relevance to the subject
• C. Frequency of updates
• D. Consistency across datasets

Answer: (A)

Precision in data quality specifically emphasizes the exactness of data. It refers to how closely data values agree with the true values or the intended representation of the information being measured. High precision implies that the data points are closely clustered together, indicating that the measurements or representations are reliable and accurate. When considering the other options, relevance to the subject deals with how pertinent the data is to the specific analysis or decision-making process, rather than its exactness. Frequency of updates pertains to how often data is revised or refreshed to ensure that it remains current and accurate, rather than focusing on the precision of individual data points. Consistency across datasets relates to how uniform and stable data is when compared across different sets, which is a separate quality aspect from precision. Thus, the correct focus on precision is solely on the accuracy and exactness of the data itself.

Precision in data quality specifically emphasizes the exactness of data. It refers to how closely data values agree with the true values or the intended representation of the information being measured. High precision implies that the data points are closely clustered together, indicating that the measurements or representations are reliable and accurate.

When considering the other options, relevance to the subject deals with how pertinent the data is to the specific analysis or decision-making process, rather than its exactness. Frequency of updates pertains to how often data is revised or refreshed to ensure that it remains current and accurate, rather than focusing on the precision of individual data points. Consistency across datasets relates to how uniform and stable data is when compared across different sets, which is a separate quality aspect from precision. Thus, the correct focus on precision is solely on the accuracy and exactness of the data itself.