#data-pipeline

Build comprehensive analytics infrastructure for data-driven decisions. Analytics architecture: 1. Data collection: event tracking, user interactions, system metrics. 2. Data pipeline: ETL processes, data validation, transformation. 3. Data warehouse: centralized storage, dimensional modeling. 4. Business intelligence: dashboards, reports, self-service analytics. Key metrics framework: 1. Acquisition: traffic sources, conversion rates, cost per acquisition. 2. Activation: onboarding completion, time-to-first-value, feature adoption. 3. Retention: DAU/MAU, cohort retention, churn analysis. 4. Revenue: ARPU, LTV, conversion rates, expansion revenue. 5. Referral: viral coefficient, NPS, organic growth rate. Reporting strategy: 1. Executive dashboards: KPIs, trends, alerts. 2. Product dashboards: feature usage, user flows, experimentation results. 3. Operational reports: performance monitoring, error tracking. Tools: Segment for data collection, Snowflake for warehousing, Tableau for visualization. Data governance: quality monitoring, access controls, privacy compliance, documentation standards.