Retail — Customer Data Quality Assurance Pipeline

Overview

The Customer Data Quality Assurance Pipeline is designed to validate and cleanse customer data ingested from various sources, ensuring compliance with predefined quality standards. The primary purpose of this DAG is to enhance the reliability of customer data, which is critical for effective personalization strategies in the retail industry. The data sources include customer transaction records, CRM data, and loyalty program information. Upon ingestion, the data undergoes a series of processing

The Customer Data Quality Assurance Pipeline is designed to validate and cleanse customer data ingested from various sources, ensuring compliance with predefined quality standards. The primary purpose of this DAG is to enhance the reliability of customer data, which is critical for effective personalization strategies in the retail industry. The data sources include customer transaction records, CRM data, and loyalty program information. Upon ingestion, the data undergoes a series of processing steps that include validation checks against established criteria, cleansing of non-compliant entries, and transformation to standard formats. Quality controls are integrated throughout the process, with non-compliant data flagged for review and correction. The outputs of this pipeline include a report on data quality metrics, a cleansed dataset for further analytics, and audit logs for traceability. Key performance indicators (KPIs) monitored include the percentage of valid data entries and the processing time for validations. In the event of validation failures, alerts are dispatched to responsible personnel for immediate action. By ensuring high-quality customer data, this DAG significantly contributes to improved personalization, customer satisfaction, and ultimately, increased sales.

Part of the Customer Personalization solution for the Retail industry.

Use cases

• Enhances customer personalization through accurate data
• Reduces operational costs associated with poor data quality
• Increases customer satisfaction and loyalty
• Facilitates compliance with data governance standards
• Improves decision-making with reliable data insights