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data_quality_dimension:consistency
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Consistency
Definitions
- Temporal consistency is the degree to which data values of a data element of a dataset at different points in time comply with a data quality rule.
- Consistency across datasets is the degree to which data values of data elements of different datasets comply with a data element of a dataset at different points in time comply with a data quality rule.
- Cross records consistency is the degree to which data values between data records comply with a data element of a dataset at different points in time comply with a data quality rule.
- Record level consistency is the degree to which two data values within a data record comply with a data element of a dataset at different points in time comply with a data quality rule.
Relation(s)
Examples
Inconsistancy within a record
| Adress | Postcode | City |
|---|---|---|
| Veldweg 11 | 8051 NK | Hattem |
- Postcode doesn't match with adress.
- Data quality rule: The postcode matches the address according to the postcode table
Inconsisteny within a file
| ID | Customer | City name | |
|---|---|---|---|
| 248 | Johson | Alphen aan den rijn | |
| 2342 | Smith | Alphen a/d rijn |
- Different in spelling in city names.
- Data quality rule: City names are spelled out in a uniform manner.
Inconsistency between two files
| Customer | Initials | Name |
|---|---|---|
| 4320 | T | Sanders |
| Customer | Initials | Name |
|---|---|---|
| 4320 | T.M.D | Sanders |
- Inconstistency: Different initials of the same customer.
- Data quality rule: Initials of the same person are the same.
Reference(s)
DAMA NL (2020). Dimensions of Data Quality (DDQ). Research paper. https://www.dama-nl.org/wp-content/uploads/2020/09/DDQ-Dimensions-of-Data-Quality-Research-Paper-version-1.2-d.d.-3-Sept-2020.pdf
data_quality_dimension/consistency.1678641793.txt.gz · Last modified: 2024/03/08 13:33 (external edit)