An analyst is summarizing the results from a recently completed survey. The results have been validated, but the analyst notices a few rows of data are missing at the bottom. For which of the…

Data completeness refers to whether all required data is present in the dataset; missing rows directly indicate that the dataset is incomplete, which is the greatest issue here.

Data integrity refers to the overall accuracy, consistency, and reliability of data, and while missing data can affect it, completeness is the more specific and direct concern.

Data consistency refers to whether data values are uniform and do not contradict each other across different datasets or over time, which is not directly described by missing rows.

Data accuracy refers to whether the data values are correct and reflect the real-world facts, which is distinct from data simply being absent.