Retail — Data Lineage Tracking for Compliance in Retail

Overview

The primary purpose of this DAG is to ensure comprehensive data lineage tracking within the retail sector, thereby supporting compliance with industry regulations. It ingests metadata from various retail systems, including point-of-sale systems, inventory management databases, and customer relationship management platforms. The ingestion pipeline begins with collecting raw metadata, followed by a normalization process to standardize the data format across different sources. This is crucial for m

The primary purpose of this DAG is to ensure comprehensive data lineage tracking within the retail sector, thereby supporting compliance with industry regulations. It ingests metadata from various retail systems, including point-of-sale systems, inventory management databases, and customer relationship management platforms. The ingestion pipeline begins with collecting raw metadata, followed by a normalization process to standardize the data format across different sources. This is crucial for maintaining consistency and reliability in data reporting. After normalization, the data is stored in a centralized registry, which serves as a single source of truth for all data lineage information. Quality control measures are applied throughout the process, including validation checks and anomaly detection, to ensure the accuracy and integrity of the data. The outputs of this DAG are visualized through an interactive dashboard, which provides insights into data flows and lineage, facilitating audits and compliance checks. Key performance indicators (KPIs) for monitoring include data accuracy rates, processing times, and the number of compliance alerts generated. The business value of this DAG lies in its ability to streamline compliance processes, reduce audit risks, and enhance data governance, ultimately leading to improved decision-making and operational efficiency in retail.

Part of the Enterprise Search solution for the Retail industry.

Use cases

• Ensures compliance with industry regulations and standards
• Reduces risks associated with data inaccuracies
• Enhances operational efficiency through streamlined processes
• Improves decision-making with reliable data insights
• Facilitates easier audits and regulatory reporting