Configuring the tMatchGroup component - 7.2

Data matching

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Data Governance > Third-party systems > Data Quality components > Matching components > Data matching components
Data Quality and Preparation > Third-party systems > Data Quality components > Matching components > Data matching components
Design and Development > Third-party systems > Data Quality components > Matching components > Data matching components


  1. Click the tMatchGroup component to display its Basic settings view.
  2. From the Matching Algorithm list, select t-Swoosh.
  3. Click Sync columns to retrieve the schema from the preceding component.
  4. Click the Edit schema button to view the input and output schema and do any modifications in the output schema, if necessary.
    In the output schema of this component there are output standard columns that are read-only. For more information, see the tMatchGroup properties.
  5. Click OK to close the dialog box.
  6. Click the Advanced settings tab and select the following check boxes:
    1. Separate output

      The component will have three separate output flows: Uniques, Matches and Suspects.

      If this check box is not selected, the tMatchGroup component will have only one output flow where it groups all output data. For an example scenario, see tgenkey_tmatchgroup-trowgenerator-tlogrow_comparing-columns-and-grouping-in-the-output-flow-duplicate-recor_standard_component_enterprise_this-secon_c.html.

    2. Sort the output data by GID
    3. Output distance details
    4. Display detailed labels
    5. Deactivate matching computation when opening the wizard
  7. Click the […] button next to Configure match rules to define the component configuration and the match rule(s).
    You can use the configuration wizard to import match rules created and tested in the studio and stored in the repository, and use them in your match Jobs. For further information, see Importing match rules from the studio repository.
    It is important to import or define the same type of the rule selected in the basic settings of the component. Otherwise the Job runs with default values for the parameters which are not compatible between the two algorithms.
  8. Define the match rule as the following:
    1. In the Key definition table, click the [+] button to add to the list the column(s) on which you want to do the matching operation, fname and lname.
      Note: When you select a date column on which to apply an algorithm or a matching algorithm, you can decide what to compare in the date format.

      For example, if you want to only compare the year in the date, in the component schema set the type of the date column to Date and then enter "yyyy" in the Date Pattern field. The component then converts the date format to a string according to the pattern defined in the schema before starting a string comparison.

    2. Click in the Matching Function column and select from the list Jaro-Winkler as the method to be used for the matching operation.

      If you select custom as a matching type, you must set in the Custom Matcher column the path pointing to the custom class (external matching algorithm) you need to use. This path is defined by yourself in the library file (.jar file).

    3. From the Tokenized measure list, select No.
    4. Click in the cell of the Threshold column and enter 0.7 for fname and 0.4 for lname.
    5. Click in the cell of the Confidence Weight column to set the numerical weights for the two columns used as key attributes: 1 for fname and 4 for lname.
    6. Click in the cell of the Handle Null column and select the null operator you want to use to handle null attributes in the columns. In this example, select Null Match NONE in order to have matching results where null values have minimal effect.
    7. Select Most common in the Survivorship Function.
  9. Follow the same procedure in the above step to define the second match rule and set the parameters as follows:
    1. Click the [+] button (Duplicate Rule).
    2. Input Key Attribute: address
    3. Matching Function: Jaro
    4. Tokenized Measure: No
    5. Threshold: 0.8
    6. Confidence Weight: 1
    7. Handle Null: Null Match NONE
    8. Survivorship Function: Most common
  10. Set the Match Threshold parameter of each Match Rule to 0.8.
  11. Set the Hide groups of less than parameter to 2. This parameter enables you to hide groups of small size.
  12. Click the Chart button to execute the Job in the defined configuration and have the matching results directly in the wizard.
    The matching chart gives a global picture about the duplicates in the analyzed data. The matching table indicates the details of items in each group and colors the groups in accordance with their color in the matching chart.
    The Job conducts an OR match operation on the records. It evaluates the records against the rule. The MATCHING_DISTANCES column allows you to understand which rule has been used on what records. 

    For example, in the second data group (brick red), the last Amic record is matched according to the second rule that uses address1 as a key attribute, whereas the other records in the group are matched according to the first rule which uses the lname and fname as key attributes.

    As you can see in this example, the value in the GRP_QUALITY column can be less than the Match Threshold parameter. That is because a group is created from record pairs with a matching score greater than or equal to the Match Threshold but the records are not all compared to each other; whereas GRP_QUALITY takes into account all record pairs in the group.