Retail — Retail E-Commerce Data Quality Assessment Pipeline

Overview

The Retail E-Commerce Data Quality Assessment Pipeline is designed to ensure the integrity, accuracy, and completeness of data used in supply and demand forecasting. The primary purpose of this DAG is to collect data from various sources, including ERP transaction logs, customer behavior analytics, and inventory management systems. The ingestion pipeline begins with data extraction from these sources, followed by a series of processing and transformation steps that apply quality tests to evaluat

The Retail E-Commerce Data Quality Assessment Pipeline is designed to ensure the integrity, accuracy, and completeness of data used in supply and demand forecasting. The primary purpose of this DAG is to collect data from various sources, including ERP transaction logs, customer behavior analytics, and inventory management systems. The ingestion pipeline begins with data extraction from these sources, followed by a series of processing and transformation steps that apply quality tests to evaluate the data. These tests include checks for missing values, outlier detection, and consistency validation. Non-compliant data triggers notification mechanisms to alert relevant stakeholders, enabling timely corrective actions. The outputs of this DAG include comprehensive quality reports that detail the findings of the data quality assessments, along with recommendations for improvements. Key performance indicators (KPIs) such as data accuracy rates, completeness percentages, and the frequency of non-compliance notifications are monitored to track the effectiveness of the data quality initiatives. By ensuring high-quality data, this DAG adds significant business value by enabling more accurate forecasting, reducing inventory costs, and improving customer satisfaction through better demand alignment.

Part of the Supply/Demand Forecast solution for the Retail industry.

Use cases

• Enhances forecasting accuracy for better inventory management
• Reduces costs associated with poor data quality
• Improves customer satisfaction through reliable data insights
• Enables data-driven decision-making across the organization
• Strengthens compliance with data governance standards