COMMUNICATION METADATA AS A DATA PRODUCT: PLATFORM DESIGN FOR INTERACTION ANALYTICS AND AI FEATURE GENERATION

Communication systems such as email, chat, calendaring, and voice generate a continuous stream of metadata: who communicated with whom, when, through which channel, and with what response latency. This metadata is a rich source of signal for interaction analytics and for machine learning features, yet in most organizations it is scattered across application logs, collected without a stable contract, and reused without clear governance. This paper treats communication metadata as a first-class data product and proposes a reference platform design for producing it. The design separates ingestion and normalization from a privacy and policy layer, publishes the product under a versioned contract with explicit quality objectives, and serves two consumer families: interaction analytics workloads that compute interaction structure and responsiveness measures, and AI feature generation workloads that require point in time correct, reproducible features. We define the product contract, the identity and event model, the minimization and pseudonymization controls, and the feature generation path, and we describe an evaluation framework built on contract conformance, freshness, completeness, and reproducibility rather than on any single deployment. The contribution is a practical, technology-neutral blueprint that platform teams can adapt to existing streaming and lakehouse infrastructure while keeping message content out of scope and keeping privacy obligations enforceable by construction.