Completeness
How much of the data that should be present is present. It applies to values, such as a blank email, and to whole records, such as a month of orders that never arrived.
Missing rows are harder to see than missing cells, because nothing on the page is blank. Comparing row counts against the source is the usual way to catch them.
Where it comes up
- True blanksA cell is really empty, and the file does not say whether the value is unknown, not applicable, or was never asked for.
- Partially filled rowsA row has some of its fields and lacks others, so it is usable for some purposes and not for others.
- Disguised missing valuesA cell is not empty, but what it holds means there is no value: N/A, a dash, 999, or the word none.
See also
- Missing valueA cell where a value should exist and does not. The reason matters: not collected, not applicable, and lost are different situations.
- Data qualityHow well a dataset serves the purpose it is used for. Usually judged by accuracy, completeness, consistency, validity, uniqueness, and timeliness.
- NullThe explicit marker a database or file uses for no value. It is different from zero, from an empty string, and from the text NULL typed into a cell.