Retail — Retail Sales and Inventory Data Ingestion for Forecasting

Overview

The primary purpose of this DAG is to facilitate the ingestion of sales and inventory data from multiple sources, including ERP systems, CRM platforms, and other internal databases, to power forecasting models in the retail sector. The architecture consists of a robust data pipeline that begins with the extraction of raw data from these diverse sources. Once ingested, the data undergoes a series of processing and transformation steps to ensure it is standardized and validated for quality assuran

The primary purpose of this DAG is to facilitate the ingestion of sales and inventory data from multiple sources, including ERP systems, CRM platforms, and other internal databases, to power forecasting models in the retail sector. The architecture consists of a robust data pipeline that begins with the extraction of raw data from these diverse sources. Once ingested, the data undergoes a series of processing and transformation steps to ensure it is standardized and validated for quality assurance. Quality controls are implemented through validation tests and security checks, ensuring that only reliable data is stored. The processed data is then stored in a centralized data warehouse, making it readily available for future analysis and forecasting model applications. Key performance indicators (KPIs) for monitoring this pipeline include data ingestion speed, error rates during validation, and the overall accuracy of the forecasting models generated from the ingested data. The business value derived from this DAG is significant, as it enables retailers to make data-driven decisions, optimize inventory levels, and enhance sales forecasting accuracy, ultimately leading to improved operational efficiency and profitability.

Part of the Market & Trading Intelligence solution for the Retail industry.

Use cases

• Improved forecasting accuracy enhances inventory management.
• Data-driven insights lead to better sales strategies.
• Streamlined operations reduce excess stock and shortages.
• Increased operational efficiency through automated data processes.
• Enhanced customer satisfaction from optimized product availability.