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Practice · Data & Analytics

From raw data to decision.

Modern data engineering, predictive models, and visualization. Cloud-agnostic, with a certified Tableau practice on the consumption layer.

Back to practices
Modern stack
Cloud-agnostic
Self-service
BI designed for decisions
20+
Years of market experience among the partners
100+
Projects delivered successfully
Our take

Data becomes value when someone decides from it. Everything before that is path — not destination.

The data industry spent a decade selling the path as if it were the destination. Warehouse, lake, lakehouse, catalog, lineage, governance — important pieces, but useless if the only decision drawn from them is "let's modernize the stack next quarter".

We deliver the data practice the other way around: we start from the decision that needs to happen and work back to the raw data. Dimensional modeling where it makes sense, lakehouse where it saves cost, dashboards only when they become action. Cloud-agnostic — we pick the right tool for the problem, not for the portfolio.

The consumption layer matters as much as the foundation. That's why we have a certified Tableau practice — not as a banner, but as a language to translate models into executive decisions. And we're honest: good BI is the one that kills the next dashboard, not the one that creates the twentieth.

What we deliver

Three fronts that cover ingestion to decision.

01

Data engineering

Modern data stack designed to scale without turning into debt. Pipelines, modeling, quality, documentation.

  • Cloud-agnostic (Snowflake, BigQuery, Databricks)
  • dbt as the modeling backbone
  • Modern ELT, no fragile overnight ETL
  • Data contracts between teams
02

Advanced analytics and insight

Predictive models, segmentation, propensity. Statistics applied to the problem, not the paper.

  • Demand forecasting and time series
  • Purchase/churn segmentation and propensity
  • Recommendation and ranking
  • Rigorous evaluation before production
03

Visualization and dashboards

Self-service BI designed to generate decisions, not vanity dashboards. Every view earns its place.

  • Headline number before chart
  • One-line comparative context
  • Drill that ends in action
  • Quality metric: decisions generated
How we deliver

Method 360 applied to Data.

Five verbs. The same method disciplines data pipelines, predictive models, and executive dashboards.

01
Map
Business decision first. What data must exist for that decision to happen?
02
Prototype
Minimal model, minimal dashboard, minimal hypothesis. Validate before generalizing.
03
Validate
Did the decision come out? Does the model beat the baseline? Kills or advances.
04
Deploy
Pipeline in production, model monitored, dashboard adopted by the team.
05
Sustain
Continuous quality, model drift monitored, dashboards updated as the business changes.
Content

Our library on Data.

See full blog
Next step

Want to discuss data with a partner?

A direct conversation with a partner of the practice. No intermediate qualification, no proposal commitment at the first meeting.