Retail — Retail E-Commerce Data Cataloging Pipeline

Overview

The primary purpose of the Retail E-Commerce Data Cataloging Pipeline is to streamline the cataloging process of ingested data within the retail sector. By establishing a robust metadata registry, this DAG ensures that data can be easily traced and accessed for analysis and reporting. The data sources include ERP transaction logs, customer behavior analytics, and inventory management systems. The ingestion pipeline begins with data collection from these diverse sources, followed by normalization

The primary purpose of the Retail E-Commerce Data Cataloging Pipeline is to streamline the cataloging process of ingested data within the retail sector. By establishing a robust metadata registry, this DAG ensures that data can be easily traced and accessed for analysis and reporting. The data sources include ERP transaction logs, customer behavior analytics, and inventory management systems. The ingestion pipeline begins with data collection from these diverse sources, followed by normalization to standardize formats and enhance consistency. Next, the data undergoes classification, which organizes it into relevant categories for improved searchability. Quality control measures are implemented to ensure the accuracy and reliability of the cataloged data. Key performance indicators (KPIs) monitored throughout the process include cataloging time and access rates to the cataloged data. The outputs of this DAG consist of an updated metadata registry, detailed data access reports, and classification summaries. By providing a structured approach to data cataloging, this DAG delivers significant business value by improving data discoverability, enhancing operational efficiency, and supporting data-driven decision-making in the retail industry.

Part of the Scientific ML & Discovery solution for the Retail industry.

Use cases

• Improved data accessibility for analytics and reporting
• Enhanced decision-making capabilities based on accurate data
• Increased operational efficiency through streamlined processes
• Better compliance with data governance regulations
• Higher customer satisfaction through personalized experiences