DQ TASK C360

Data Quality Implementation

Overview

• Objective: Improve customer data quality using Informatica.

Steps to Address Data Quality Issues

1. Identify Customer Data with DQ Issues

   • Collect data flagged with DQ issues.

2. Load Data with DQ Issues

   • Ingest the problematic data into the system.

3. Apply DQ Rules

   • Create mapping with DQ rules to segregate valid and invalid records.

4. Reverse Integration

   • Save valid data back to the original source and report back invalid entries for correction.

DQ Rules and Mapping

Invalid Sample Data

• Examples of invalid data:

  • Null Values

  • Special Characters

  • Undefined Characters

• Data Fields Affected:

  • Id, FirstName, LastName, Company, Title, City, State, Postal Code, Country, Email, etc.

  • Samples of invalid records listed, showcasing issues with format and special characters.

Mapping Process

• Configure source and target mapping for data correction.

• Customize DQ rules based on required standards to manage invalid data and valid data stream.

• Success target directs valid data to Customer360; invalid records are flagged for review.

Task Flow

Setting Up the Taskflow

• Create a mapping task in the system.

• Initialize task flow with required parameters to manage data extraction and correction.

Data Loading and Ingress Process

Source System Setup

• Ensure source system is configured correctly to run the Ingress job.

• Publish task flow and execute the job to extract and process records.

Records Update

• Examine the outcomes by assessing success and error records during transformations.

Results

Valid vs Invalid Data Records

• Invalid Data Handling

  • Detailed tracking of invalid records from the initial dataset to correction actions taken.

• Successful Data Entries

  • Valid records successfully processed and stored in the Customer360 profile, highlighting key attributes:

  • FirstName, LastName, Company, Email, etc.

  • Examples of successfully corrected records presented.