Retail — Taxonomy Extraction Pipeline for Retail Entities

Overview

The purpose of the 'Taxonomy Extraction Pipeline for Retail Entities' DAG is to systematically extract named entities from diverse data sources and build a comprehensive taxonomy that improves information retrieval and classification within the retail industry. The pipeline ingests data from multiple sources, including ERP transaction logs, CRM records, and external market research documents. The architecture consists of several stages: data ingestion, entity extraction, taxonomy construction, q

The purpose of the 'Taxonomy Extraction Pipeline for Retail Entities' DAG is to systematically extract named entities from diverse data sources and build a comprehensive taxonomy that improves information retrieval and classification within the retail industry. The pipeline ingests data from multiple sources, including ERP transaction logs, CRM records, and external market research documents. The architecture consists of several stages: data ingestion, entity extraction, taxonomy construction, quality control, and data storage. During the ingestion phase, raw data is collected from the specified sources. The entity extraction process employs advanced natural language processing techniques to identify and categorize relevant entities. Following extraction, the taxonomy construction step organizes these entities into a structured format that facilitates easy access and understanding. Quality control measures are implemented to ensure the accuracy and reliability of the extracted data, including validation checks and consistency assessments. The final outputs are stored in a centralized data warehouse, making them accessible through a robust API for various applications, including analytics and reporting tools. Key performance indicators (KPIs) such as extraction accuracy, processing time, and user engagement metrics are monitored to evaluate the effectiveness of the pipeline. The business value of this DAG lies in its ability to streamline information classification, enhance data-driven decision-making, and ultimately improve operational efficiency in retail.

Part of the Data & Model Catalog solution for the Retail industry.

Use cases

• Improves data retrieval efficiency for retail operations
• Enhances decision-making through structured insights
• Facilitates better customer understanding and segmentation
• Reduces time spent on manual data classification
• Increases operational efficiency by automating data processes