Retail — Customer Data Exposure for Analytics and Reporting

Overview

The primary purpose of the 'Customer Data Exposure for Analytics and Reporting' DAG is to securely expose customer data through APIs and dashboards, enabling comprehensive analysis and reporting for enhanced customer personalization in the retail industry. The architecture is designed to ensure that sensitive customer information is protected while providing valuable insights to stakeholders. Data sources include customer transaction records, user interaction logs, and demographic information.

The primary purpose of the 'Customer Data Exposure for Analytics and Reporting' DAG is to securely expose customer data through APIs and dashboards, enabling comprehensive analysis and reporting for enhanced customer personalization in the retail industry. The architecture is designed to ensure that sensitive customer information is protected while providing valuable insights to stakeholders. Data sources include customer transaction records, user interaction logs, and demographic information. The ingestion pipeline begins with the extraction of data from these sources, followed by transformation steps that include data cleansing, normalization, and aggregation. Role-Based Access Control (RBAC) is implemented to enforce strict access controls, ensuring that only authorized personnel can access sensitive data. The processing logic includes the generation of key performance indicators (KPIs) such as the number of users accessing the data and the API response times, which are critical for monitoring the system's performance. In case of failures or anomalies, alerts are triggered to notify administrators, allowing for prompt resolution of issues. The outputs of this DAG include interactive dashboards, detailed reports, and API endpoints that provide real-time access to customer insights. Monitoring KPIs are essential for assessing the effectiveness of the data exposure and ensuring that the system meets business requirements. Overall, this DAG adds significant business value by enabling data-driven decision-making and enhancing customer engagement through personalized experiences.

Part of the Customer Personalization solution for the Retail industry.

Use cases

• Enhanced customer personalization through data insights
• Improved decision-making based on accurate reporting
• Increased operational efficiency with automated monitoring
• Strengthened data security and compliance measures
• Greater customer engagement through targeted marketing