Retail — Automated Data Quality Assurance for E-commerce

Overview

The primary purpose of this DAG is to ensure data quality through automated testing processes tailored for e-commerce data. It ingests various data sources, including customer transaction records, product inventories, and user behavior logs, to verify that each field meets established expectations. The architecture consists of a robust ingestion pipeline that collects data from these sources and feeds it into a series of processing steps designed to conduct quality checks. Each field is assessed

The primary purpose of this DAG is to ensure data quality through automated testing processes tailored for e-commerce data. It ingests various data sources, including customer transaction records, product inventories, and user behavior logs, to verify that each field meets established expectations. The architecture consists of a robust ingestion pipeline that collects data from these sources and feeds it into a series of processing steps designed to conduct quality checks. Each field is assessed against predefined criteria, and any discrepancies are logged in a quality register. Alerts are generated for non-compliance incidents, enabling swift remediation actions. Key performance indicators (KPIs) monitored include compliance rates and processing times, which provide insights into the efficiency of the data quality assurance process. In the event of a failure, the DAG incorporates an incident recovery mechanism to facilitate quick resolution and maintain data integrity. This structured approach not only enhances the reliability of data used for analytics and reporting but also supports governance and compliance efforts within the retail sector, ultimately driving better business outcomes.

Part of the Governance & Compliance solution for the Retail industry.

Use cases

• Improved data integrity for better decision-making
• Enhanced compliance with regulatory standards
• Increased operational efficiency through automation
• Reduced risk of data-related issues impacting sales
• Timely identification of data quality issues