Data quality dimensions describe a measurable characteristic of data and help defining data quality requirements. Use data quality dimensions to determine the expected results of data quality assessment, whether initial assessment or ongoing monitoring.
The state that you want your data to be in usually can be defined as fit for use, defect free, corresponds to specification, or meeting expectations and requirements. When you measure data quality, you compare the actual state of your data to this wanted state. The standards, expectations, and requirements that are important to your business processes are expressed as characteristics or dimensions of the data.
The Data Management Association (DAMA) International published a paper that describes 6 core dimensions of data quality:
| Dimension | Description | Predefined data quality checks that identify issues associated with this dimension |
|---|---|---|
| Accuracy | Data values are as close as possible to real values. | None. |
| Completeness | All required data values are present. | Unexpected missing values |
| Consistency | Data values within a column comply with a rule. | Inconsistent capitalization Inconsistent representation of missing values Suspect values |
| Timeliness | Data represents the reality from a required point in time. | None. |
| Uniqueness | Distinct values appear only once. | Unexpected duplicated values |
| Validity | Data conforms to the format, type, or range of its definition. | Data class violations Data type violations Format violations Values out of range |
You can create your own data quality dimensions by using the Watson Data API Create a data quality dimension.
Learn more
- Data quality analysis results
- Predefined data quality checks
- Configuring master data workflows
- Watson Data API: List all data quality dimensions
- Watson Data API: Create a data quality dimension
Parent topic: Managing data quality