AI-Augmented Customer Data Platforms: Engineering for Scale, Speed, and Compliance

Abstract

Customer Data Platforms (CDPs) have quickly become the cornerstone of enterprise marketing, which gives organizations the opportunity to create personalized, cross-channel customer experiences in the existing data-driven world. The following article reviews the state-of-the-art CDP infrastructure that is capable of handling large numbers of daily events in both AWS and GCP cloud platforms. The system uses event-driven processing with Kafka, can resolve identity in a real-time fashion, and applies high standards of data privacy controls and regulations, and the issues of scale, speed, and regulations. The notable advancements are infrastructure-as-code provisioning with the help of Terraform, an integrated machine learning engine to make real-time segmentation, a predictive workload engine to support Apache Spark data processing, and a schema-aware historical data model based on Databricks. Privacy-by-design is applied in the architecture of the pipeline as a whole, and extensive governance controls across the architecture are provided, such as policy enforcement, automated audit log, and consent management. The results of implementation indicate the exemplary technical performance and demonstrable business impact that make this approach a guideline for next-generation CDP implementations to find the balance of performance, flexibility, and governance demands of contemporary marketing technology landscapes.