Data quality dimensions
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.
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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: Accuracy, Completeness, Consistency, Timeliness, Uniqueness, Validity
In addition to these core dimensions, IBM Knowledge Catalog provides the dimension Homogeneity.
The following table describes the data quality dimensions and lists the data quality checks in metadata enrichment that can identify issues associated with a specific dimension. In addition, these dimension can be evaluated by running individual data quality rules.
| Dimension | Description | Types of data quality checks |
|---|---|---|
| Accuracy | Data values are as close as possible to real values. | None. |
| Completeness | All required data values are present. | Completeness check |
| Consistency | Data values within a column comply with a rule. | Capitalization style check Missing values representation check Referential integrity check Suspect values check |
| Homogeneity | Data within a data asset is uniform and consistent over time. All data points share similar characteristics, formats, or structures. | Historical stability |
| Timeliness | Data represents the reality from a required point in time. | None. |
| Uniqueness | Distinct values appear only once. | Uniqueness check |
| Validity | Data conforms to the format, type, or range of its definition. | Data class check Data type check Format check Length check Possible values check Range check Regex check |
IBM Match 360 (if deployed) contributes the Entity confidence dimension. This dimension indicates how confident the system is that the entity matches within your data are correct. The dimension score represents the percentage of entities of the particular entity type that have no records with potential match issues as member.
You can create your own data quality dimensions by using the IBM Knowledge Catalog API Create a data quality dimension.