Start with a contract: what is a ‘signup’, an ‘activated user’, a ‘qualified lead’? Encode these definitions in your warehouse with dbt so every report matches. Build core marts—funnel, retention, revenue—then layer a semantic model so BI stays consistent. Keep freshness SLAs and lineage visible so stakeholders trust numbers.
Use cases that move money: activation funnel drop-off analysis, cohort retention (D1/D7/D30), LTV by segment, churn drivers from tickets and product usage, and pricing elasticity tests.
Use cases that move money: activation funnel drop-off analysis, cohort retention (D1/D7/D30), LTV by segment, churn drivers from tickets and product usage, and pricing elasticity tests. For ops, monitor stockouts, lead times, and on-time-in-full; for finance, build top-down + bottom-up forecasts that reconcile. Automate anomaly detection on KPIs and page SRE-style when revenue metrics deviate.
Data culture is a product. Provide self-serve dashboards with drilldowns, notebook recipes for analysts, and a request SLA for new metrics. Instrument experiments by default and maintain a KPI wiki. When everyone can ask and answer with confidence, decisions accelerate—and so does growth.