The problem
Product data in one tool, billing in another, marketing in a third — and three different revenue numbers in every meeting. Analytics debt compounds quietly until nobody trusts any chart.
The system
Managed ELT lands raw sources into a warehouse (BigQuery/Snowflake/Postgres — sized to you, not to fashion); versioned, tested transformations produce documented models; a semantic layer defines each metric once; dashboards and AI assistants read from the same definitions.
How it's built
- Source connectors with freshness monitoring and schema-change alerts
- dbt-style transformation layer: tested, documented, code-reviewed
- Metrics defined once, consumed everywhere — including by LLM query tools
- Cost discipline: partitioning, clustering, and query budgets from day one
Delivery
Build engagement, typically 6–8 weeks to first governed dashboards; Run keeps sources, tests, and costs healthy.
What to expect
- One definition per metric — meetings argue about decisions, not numbers
- New questions answered with SQL or natural language against modeled data
- Data quality failures alert engineers before executives notice