"Uber Engineering redesigned its mobile analytics architecture to standardize event instrumentation across iOS and Android, addressing fragmented ownership, inconsistent semantics, and unreliable cross-platform data. The goal was to simplify engineering effort, improve data quality, and provide trustworthy insights for product and data teams across rider and driver applications. According to Uber engineers, mobile analytics are critical for decision-making, feature adoption, and measuring user experience. As apps and teams grew, instrumentation became decentralized."
"Feature teams defined and emitted events independently, shared UI components often lacked analytics hooks, and similar interactions were logged differently across different teams. As a result, over 40% of mobile events were custom or ad-hoc, complicating analysis and reducing confidence in aggregated metrics. To address these challenges, engineers moved core analytics responsibilities from feature-level code to shared infrastructure. Working with product, design, and data science teams, they defined standardized event types such as taps, impressions, and scrolls."
Mobile analytics were centralized to eliminate fragmented ownership, inconsistent semantics, and unreliable cross-platform data. Feature teams had been emitting ad-hoc events, with over 40% of events custom and logged inconsistently, complicating analysis and reducing confidence in aggregated metrics. Core analytics responsibilities moved from feature-level code to shared infrastructure with standardized event types like taps, impressions, and scrolls. Events are code-generated from shared schemas, instrumented at the UI component level, emitted through a centralized reporting layer, enriched by backend services, and consumed by analytics pipelines. Platform-level UI components include analytics builders to manage event lifecycle, enabling standardized instrumentation without custom code. Performance testing showed no CPU or frame-rate regressions.
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