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
- 01Three departments reporting three different numbers for the same KPI
- 02Executive reports arriving 5 days after close
- 03Ingestion pipeline breaking every time a vendor changes
- 04Analytics team 80% consumed cleaning Excel
- 05Executive committee deciding by intuition because "the data is not ready"
What we deliver
- 01Single semantic model + governance layer with lineage
- 02Idempotent ingestion pipelines (Airbyte / Fivetran / custom)
- 03Governed warehouse / lake-house (Snowflake, Databricks, BigQuery)
- 04Actionable executive dashboards (Power BI, Tableau, Looker)
- 05Data-quality program with per-dataset SLAs
- 06Training for the internal team to maintain the stack
Concrete cases where we did this
- Case 01
Retail with 12 data sources: unification in Snowflake + dbt, time-to-insight dropped from 4 days to 4 hours
- Case 02
Bank: automated regulatory reporting pipeline (10 monthly reports that took 2 weeks of team effort)
- Case 03
B2B SaaS: actionable KPI definition + executive Looker dashboard — pricing decisions instead of pricing meetings
- Case 04
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
01Power 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).
02Snowflake 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.
03And 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? We tell you honestly.
30 minutes online with a senior consultant. No sales pitch. We tell you if we fit.