Analytics & BI Modernization
Trustworthy numbers for the whole company.
We rebuild the numbers layer: a versioned semantic model, tested dbt transformations and a BI stack that business users actually trust — with documented lineage from raw source to dashboard.
100%
Transformation coverage in dbt
8×
Query speed improvement post-migration
Zero
Conflicting KPI definitions post-launch
4 weeks
Average migration timeline
What we ship
Capabilities
dbt-driven semantic layers, warehouse migrations (Snowflake / BigQuery / ClickHouse) and BI rebuilds that finally give every team the same definition of revenue.
- 01
dbt models, tests & docs
Every transformation versioned in Git, tested with dbt expectations and documented with column-level lineage — no more mystery SQL in the warehouse.
- 02
Semantic layer & metric catalogues
One definition of revenue, conversion and churn — exposed to BI tools through a semantic layer so every dashboard agrees on the numbers.
- 03
Snowflake / BigQuery / ClickHouse
We migrate from legacy EDW platforms and optimize for your query patterns — ClickHouse for high-concurrency analytics, BigQuery for cloud-native scale.
- 04
Self-serve BI in Metabase / Looker
Business users write their own questions — against a semantic model with guardrails — without going through the data team for every report.
Outcomes
- One trusted definition of core KPIs
- Documented, testable transformations
- Self-serve analytics across teams
Tech we use
In the field
- 1
Regional bank KPI unification
14 conflicting revenue definitions collapsed into one dbt semantic layer — CFO signed off the numbers for the first time in 3 years.
- 2
E-commerce BI migration
Legacy Oracle OBIEE migrated to ClickHouse + Metabase in 6 weeks — query times dropped from 4 minutes to 8 seconds.
- 3
NGO donor reporting
Self-serve dashboards for 12 programme teams — data team time spent on reports dropped by 70%.
How we deliver
Our delivery process
Every engagement follows the same rigorous four-stage approach — so you know exactly what to expect, and when.
- Step01
KPI alignment workshop
We run a structured session with finance, product and ops to agree on canonical metric definitions before touching the warehouse.
- Step02
dbt model build
Staged, tested dbt models from raw source to gold-layer business entities — reviewed and signed off by domain owners.
- Step03
Warehouse migration
Parallel-run validation between legacy and new warehouse until every dashboard shows identical numbers — then cutover.
- Step04
BI training & handoff
Business users trained on self-serve BI with governed semantic models — data team freed from report requests.
Ready to get started?
Build analytics & bi modernization for your product
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