Telecom — Customer Data Ingestion and Personalization Pipeline

Overview

The primary purpose of this DAG is to facilitate the ingestion of customer data from various sources, including Customer Relationship Management (CRM) systems, Enterprise Resource Planning (ERP) systems, and business APIs. By consolidating data from these diverse origins, the pipeline enables effective customer personalization strategies in the telecom industry. The architecture consists of a multi-step ingestion pipeline that begins with data extraction from the specified sources. Once the data

The primary purpose of this DAG is to facilitate the ingestion of customer data from various sources, including Customer Relationship Management (CRM) systems, Enterprise Resource Planning (ERP) systems, and business APIs. By consolidating data from these diverse origins, the pipeline enables effective customer personalization strategies in the telecom industry. The architecture consists of a multi-step ingestion pipeline that begins with data extraction from the specified sources. Once the data is ingested, it undergoes a normalization process to standardize formats and structures, ensuring consistency across datasets. Quality control measures are applied to validate the integrity of the data, identifying any discrepancies or errors during the ingestion process. The processed data is then stored in a dedicated data warehouse, making it readily accessible for analytics and reporting purposes. Key performance indicators (KPIs) monitored throughout this process include the ingestion error rate and processing time, which provide insights into the efficiency and reliability of the data pipeline. In the event of failures, a robust recovery mechanism is implemented to ensure minimal disruption to operations. This DAG not only streamlines data ingestion but also enhances the overall customer experience by enabling tailored marketing and service offerings based on accurate and comprehensive customer insights.

Part of the Customer Personalization solution for the Telecom industry.

Use cases

• Enhances customer engagement through personalized services.
• Improves data-driven decision-making capabilities.
• Reduces operational inefficiencies in data management.
• Facilitates compliance with data governance standards.
• Increases customer satisfaction and loyalty through tailored experiences.