The degree to which all required data is present and no essential information is missing.
Data completeness is defined as the degree to which all required information is available. Incomplete data can lead to incorrect conclusions and poor decision-making.
Common causes of incompleteness include:
- Missing records
- Missing attribute values (null or empty fields)
- Partial data extraction or loading
- System failures during data capture
Completeness can be measured at different levels:
- Schema completeness: All required tables and columns exist
- Column completeness: Percentage of non-null values in required fields
- Population completeness: All expected entities are represented in the dataset
[Adapted from ISO/IEC 25012 and data quality literature]
Completeness is particularly critical in:
- Healthcare systems (complete patient records)
- Financial reporting (all transactions recorded)
- Regulatory compliance (all required data elements present)
- Master data management (complete entity profiles)
See also: data-quality, functional-completeness