← Back to case studies

A subscription streaming business operating across regions and content catalogs needed trustworthy, comparable metrics for acquisition, engagement, and revenue—while product and marketing pushed for fresher signal than overnight batch alone could provide.

What leadership was trying to fix

Marketing, finance, and product on different clocks

Marketing required reliable daily—and sometimes intra-day—visibility into campaigns and engagement by region. Finance needed subscription revenue, churn, and trial conversion that reconciled to billing. Product teams building personalization and experimentation needed feature-ready data that matched what analysts used in the warehouse, not a shadow copy refreshed on different schedules.

Executive forums had become a debate over which spreadsheet or dashboard reflected “the” week, not a discussion of what to do next.

Friction in the data estate

Overlapping paths and informal mega-joins

Play and subscriber events arrived through multiple ingestion paths and regional pipelines. Some marts were tuned for BI, others for ad hoc exports; the same metric labels did not always tie out when filters or currencies changed. Excel bridged gaps for urgent questions but could not scale or be governed. Near–real-time experiments were tempting to stand up outside the core platform—risking yet another source of truth.

Design of the response

Medallion layout with explicit latency SLAs

We implemented a medallion-style pipeline on the client’s cloud analytics stack: Bronze for raw, replayable landing; Silver for conformed subscriber, session, title, and regional dimensions; Gold for curated marts serving BI, finance reporting, and downstream feature consumers. Where campaigns or product use cases required lower latency, incremental and streaming paths fed Silver and Gold with explicit latency SLAs—not informal “best effort” loads.

A semantic layer defined subscriber, trial, engagement, and revenue measures once; regional and currency logic lived in conformed dimensions rather than in every report. Data quality checks concentrated on revenue-impacting joins and late-arriving events that could distort conversion or churn.

How we ran delivery

Waves of sources and regions

We prioritized source families and regions in waves, proving end-to-end path, test coverage, and owner runbooks before expanding. Data owners signed off on semantic definitions; promotions ran through automated checks with clear failure handling. Lineage was wired from dashboard KPIs back to landing zones for auditability and incident response.

Our repeatable pattern is: contracts and tests at boundaries, incremental vertical slices, and handover artifacts platform teams can operate—whether the workload is batch-heavy or mixed streaming and batch, as it was here.

Impact

One semantic layer for steering and features

Stakeholders could anchor discussions on shared definitions instead of competing extracts. Product and analytics teams drew features and board-level metrics from the same conformed entities. Finance and marketing cut time spent reconciling regional cuts of the same period. Operations received clear ownership of pipelines the business depended on, with escalation paths after go-live.

Specific figures are client-internal; directionally, the program reduced reconciliation overhead and sped up insight cycles for campaign and catalog decisions.