Decisions with data, not with gut feeling.
We build the full data stack: ingestion, dimensional modeling, governance, quality and visualization. Power BI, Tableau, Looker, Snowflake, Databricks — we use what fits your organization rather than what we prefer to sell. The goal is constant: committees making decisions with numbers that agree across departments.
— Recurring problems
- Three departments reporting three different numbers for the same KPI
- Executive reports arriving 5 days after close
- Ingestion pipeline breaking every time a vendor changes
- Analytics team 80% consumed cleaning Excel
- Executive committee deciding by intuition because "the data is not ready"
— What we deliver
- Single semantic model + governance layer with lineage
- Idempotent ingestion pipelines (Airbyte / Fivetran / custom)
- Governed warehouse / lake-house (Snowflake, Databricks, BigQuery)
- Actionable executive dashboards (Power BI, Tableau, Looker)
- Data-quality program with per-dataset SLAs
- Training for the internal team to maintain the stack
— Concrete cases where we did this
Retail with 12 data sources: unification in Snowflake + dbt, time-to-insight dropped from 4 days to 4 hours
Bank: automated regulatory reporting pipeline (10 monthly reports that took 2 weeks of team effort)
B2B SaaS: actionable KPI definition + executive Looker dashboard — pricing decisions instead of pricing meetings
E-commerce: 30k-SKU catalog with recommendation engine (vector DB + collaborative filtering)
Figures and companies anonymized or public with permission. Detailed references under NDA.
— Typical stack we master
— Questions we get the most
Power BI, Tableau or Looker?
It depends on context: Microsoft team → Power BI (cheaper, native integration); mature data team with existing licenses → Tableau (better visual analytics); Google Cloud shops or those valuing semantic layer → Looker (LookML modeling).
Snowflake or Databricks?
Snowflake wins for pure SQL and predictable storage/compute separation. Databricks wins when you need serious ML and Spark processing. For most LATAM mid-sized companies, Snowflake is the safer first bet — simpler to operate.
And if we already have a broken warehouse?
We start with a model debt diagnostic (is it dimensional? is there lineage? data-quality tests?). It is almost always faster to refactor by domain than to migrate entirely. We say so honestly.
— How we engage on this pillar
— Industries where we apply data most
Does your data challenge fit what we do?
30 minutes online with a senior consultant. No sales pitch. We tell you if we fit.