5.5.1 Types of Data Quality Checks

The following are the types of Data Quality Checks and their definitions:

Table 5-5 Data Quality Checks

Data Quality Check	Definition
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 a specific value or with another attribute of the base table.
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.
Duplicate Check	Is used when a combination of the column is unique and identifies all duplicate data of a base table in terms of the columns selected for the duplicate check.
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. It ranges from "-1,000,000,000 to 1,000,000,000".
Uniqueness Check for Numeric Identifiers in Dimension	Check to identify duplicates in Numeric Identifier Attribute for a Dimension Entity. Check to identify changes in Numeric Identifier Attribute for a Dimension Entity for the same Business Key member.
Special Character Check	Identify business term contains only the allowed set of special characters. Currently, AFCS has preconfigured rules for the following Business Terms: Legal Entity Code Legal Entity Description Legal Entity Name Data Source Code Data Source Description For more details on allowed set of special characters, see Allowed Special Characters list.

The controls are specific to reports.