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The goal of this document is to understand and implement the Global Address cleanse Transforms to Assess the quality of the Address data and to figure out the bad address data

Step 1: Create a New Job and add a Dataflow

Addr1.jpg

Step 2: Inside the Dataflow, Select the source of address data from a table as shown below

Addr2.jpg

Step 3: Select the Data Quality Transforms from the local object library pane and select the Global Address cleanse Transform i.e., UK_Address_Cleanse(Based on your address directory & licensing) as shown below

Addr3.jpg

Step 4: Connect the Source data via Query Transform (if needed) and connect with UK_Address_cleanse Transform and then with the Target Table.

Addr4.jpg

Step 5: Now open the Global Address Cleanse Transform and assign the Inbound attributes in the Input Editor as shown below.

Addr5.jpg

Step 6: Goto the Options Tab and select the Address directory path and change the Generate Report data option as ‘YES’

Addr6.jpg

Step 7: Go to Output Tab and select the 4 columns(Status Code, Info_Code, Quality_Code, Assignment Level) as shown below

Addr7.jpg

Step 8: Save & Execute the Job. In the Execution properties, select the Export Data Quality Reports option.

Addr8.jpg

Step 9: once the Job is executed, then log in to the Data Services Admin Console and click the Data Quality Reports

Addr9.jpg


Now you can able to see your Job and corresponding Quality Reports got exported.

Addr10.jpg

If you select the Quality Code Summary Report, this will show the Quality of your address data

Addr11.jpg

Each Quality code implies different meaning

  1. Q2 :  Corrected Address
  2. Q3 : High likely that this address is deliverable
  3. Q4 : Fair likely that this address is deliverable
  4. Q6 : Highly Unlikely that this address is deliverable


And the below Summary chart shows the same.

Addr12.jpg

You can click on each Quality code and drill down up to the record level for making further analysis and corrections.

Cheers

John

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