Retail — Customer Data Audit Trail Generation

Overview

The primary purpose of this DAG is to generate detailed audit trails for all actions performed on customer data within the retail sector. By capturing every interaction, the DAG ensures that businesses maintain compliance with regulatory standards and can trace data usage effectively. The data sources include customer interaction logs, transaction records, and system access logs, which are ingested into the pipeline for processing. The ingestion pipeline securely collects and stores these logs,

The primary purpose of this DAG is to generate detailed audit trails for all actions performed on customer data within the retail sector. By capturing every interaction, the DAG ensures that businesses maintain compliance with regulatory standards and can trace data usage effectively. The data sources include customer interaction logs, transaction records, and system access logs, which are ingested into the pipeline for processing. The ingestion pipeline securely collects and stores these logs, ensuring data integrity and confidentiality. The processing steps involve validating the incoming data for accuracy, transforming the data into a standardized format, and aggregating the logs to create comprehensive audit records. Quality controls are implemented at each stage to ensure that any discrepancies are flagged and addressed. The final outputs include an audit report detailing the number of entries logged, timestamps of actions, and any errors encountered during processing. Monitoring KPIs include the total number of entries in the audit log and the time taken to generate audit reports. This allows businesses to assess the efficiency of their data handling processes. In the event of a failure, an error report is generated to facilitate quick analysis and resolution. The business value of this DAG lies in its ability to strengthen data governance, enhance compliance readiness, and build customer trust through transparent data management practices.

Part of the Customer Personalization solution for the Retail industry.

Use cases

• Ensures compliance with data protection regulations
• Enhances customer trust through transparency
• Facilitates efficient audit processes
• Reduces risk of data mishandling penalties
• Improves overall data governance strategies