From data to decisions, measurably
Modern data architectures, self-service BI and predictive models. Start from a concrete use case, scale once value is proven.
What we do
Data strategy & governance
Goals, ownership, quality and GDPR compliance. Strategy first, technology second.
Data lake, warehouse & lakehouse
Scalable architectures on Snowflake, Databricks, BigQuery, Azure and AWS.
BI & dashboards
Tableau, Power BI, Qlik and Looker: self-service dashboards for managers and operations.
Machine learning & AI
Demand forecasting, churn prediction, predictive maintenance, recommendation engines.
Real-time & streaming
Kafka, Event Hubs and Kinesis for live monitoring, alerting and fraud detection.
Data quality & MDM
Cleansing, de-duplication and golden records: the foundation of reliable analysis.
Where to start
- 01
Use case
A measurable goal: cut churn by 10%, improve forecasting, reduce stock.
- 02
Assessment
Data sources, quality, existing tools and in-house skills.
- 03
Pilot
2–3 months to prove value with real data.
- 04
Scale
Extend the platform to more use cases and departments.
Frequently asked questions
Where do I start if I have never run a data project?
With a specific, measurable use case and a 2–3 month pilot. If it works, scale it.
My data is messy. Can I still do analytics?
Yes, but it needs a data quality and governance phase first, which we always include. Getting data in order is already a win.
How much does a Big Data project cost?
It depends on data sources, data quality, BI tools and the use cases in scope. That is why we start with an assessment: within a few weeks we define scope, priorities and an accurate estimate for the pilot.
Ready to turn your data into value?
We will propose a data maturity assessment.