The degree to which all required data values are present and not missing in a dataset.
A dataset is complete when every field that should have a value actually has one. Completeness is measured as a percentage: a column with 950 values out of 1,000 expected records has 95% completeness. Low completeness in critical fields can make a dataset unusable for its intended purpose.
Example: A dataset of environmental monitoring stations should have a reading for every station every hour. If 15% of hourly readings are missing, the dataset has 85% completeness for that field. An analyst studying pollution trends must account for those gaps or risk drawing incorrect conclusions.
Completeness is one of the core dimensions of data quality, alongside accuracy, consistency, timeliness, and validity. It is typically assessed during data profiling.