1.1.1 Types of Data Quality Checks
The following are the types of Data Quality checks and their definitions:
Table 1-1 Data Quality Checks
Data Quality Check | Definition |
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Blank Value check | Identifies if the base column is empty considering the blank space. |
Column Reference / Specific Value check | Compares the base column data with another column of the base table or compare with any attribute of compatible data type from a referenced dimension of a base entity. |
Data Length Check | Checks for the length of the base column data by using a minimum and maximum value, and identifies if it falls outside the specified range. |
List of Value Check | It can be used to verify values where a dimension/master table is not present. This check identifies if the base column data does not match with a value or specified code in a list of values. |
NULL value Check | Identifies if NULL is specified in the base column. |
Referential Integrity Check | Identifies all the base column data that has not been referenced by the selected column of the referenced table. Here, the user specifies the reference table and columns. |
Range Check | Identifies if the base column data falls outside a specified range of a Minimum and Maximum value. Value Needs to be between 0 and 100. |
Uniqueness Check for Numeric Identifiers in Dimension |
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Special Character Check |
Identify business term contains only the allowed set of special characters. Currently, AFCS has preconfigured rules for the following Business Terms:
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The controls are specific to reports.
Note:
The check category for custom DQ check referencing to dimensions will be shown as Custom Check in the Data Quality Result reports.