Accelerator Guide

Back Next

Germany General Data Cleansing Rules

Use the general data cleansing rules to identify the type of information contained in input fields.

Find the general data cleansing rules in the following repository location:

[Informatica_DQ_Content]\Rules\General_Data_Cleansing

The following table describes the general data cleansing rules in the Germany accelerator:

Name	Description
rule_DEU_NER_Field_Identification	Identifies the type of information contained in an input field. The rule can identify names, Personal IDs, company names, dates, and German address data. The rule returns a label that describes the type of input data. The rule uses reference data to identify the types of information. The rule uses probabilistic matching techniques to identify the types of information.

The Germany accelerator depends on the following general data cleansing rules from the Core accelerator:

rule_Assign_DQ_GeocodingStatus_Description

rule_Assign_DQ_Mailability_Score_Description

rule_Assign_DQ_Match_Code_Descriptions

rule_Remove_Extra_Spaces

rule_Remove_Hyphen

rule_Remove_Leading_Zero

rule_Remove_Parentheses

rule_Remove_Period_Parentheses

rule_Remove_Punctuation

rule_Remove_Punctuation_and_Space

rule_Remove_Space

rule_Replace_Limited_Punct_with_Space

rule_UpperCase

For more information about these rules, see Core General Data Cleansing Rules.

Watch

Comments

0 COMMENTS

We’d like to hear from you! Log in to comment.