Consumer Products — Multi-Source ERP and CRM Data Ingestion Pipeline

Overview

The purpose of this DAG is to facilitate the ingestion of data from multiple ERP and CRM systems, allowing for a unified analysis of consumer product data. The primary data sources include internal databases and business APIs, which provide essential transactional and customer information. The ingestion pipeline begins with data extraction from these diverse sources, followed by a transformation phase where the data is standardized and cleansed to ensure consistency and quality. This is achieved

The purpose of this DAG is to facilitate the ingestion of data from multiple ERP and CRM systems, allowing for a unified analysis of consumer product data. The primary data sources include internal databases and business APIs, which provide essential transactional and customer information. The ingestion pipeline begins with data extraction from these diverse sources, followed by a transformation phase where the data is standardized and cleansed to ensure consistency and quality. This is achieved through rigorous quality controls that validate data integrity and accuracy throughout the process. Once the data is transformed, it is loaded into a centralized data warehouse where it can be accessed for further analysis and reporting. The outputs of this pipeline are exposed via a robust API, enabling seamless integration with analytical tools and applications. Key performance indicators (KPIs) for monitoring this pipeline include data accuracy rates, processing times, and the volume of data ingested. By leveraging this DAG, organizations in the Consumer Products sector can enhance their decision-making processes, improve customer targeting, and optimize inventory management, ultimately driving business growth and efficiency.

Part of the Recommendations solution for the Consumer Products industry.

Use cases

• Improves data-driven decision-making capabilities
• Enhances customer insights and targeting strategies
• Optimizes inventory management and supply chain efficiency
• Reduces data silos and improves collaboration
• Increases operational efficiency through automation