Business Intelligence Consulting

Spreadsheets pile up, dashboards age out, and the answers business needs sit one query away — but no one trusts the data enough to act on it. Today's data stack has more tools than ever, which makes the choice harder, not easier.

We help SMEs and startups build the data foundation they actually need — not the over-engineered platform a Fortune-500 vendor would sell them. After a consultation, you'll know which problems are worth solving with data, which tools fit your scale, and what to invest in over the next 12 months.

• design a data foundation that matches your scale — warehouse choice, ELT, orchestration, governance
• clean up data quality and lineage so people trust the numbers
• pick the visualization tool that fits your team (Tableau, Power BI, Looker, Metabase, Superset)
• design a semantic layer so revenue, churn, and MRR mean the same thing everywhere

• assess data analytics pipelines on GCP, AWS, Azure, and Databricks
• evaluate where real-time and streaming are worth the operational cost
• design RAG and natural-language analytics layers on top of your warehouse
• roadmap data governance and privacy (Swiss DSG, GDPR)

An accounting firm with three branches in Europe needed one real-time view of its operations. Its data sat in ERP exports and spreadsheets, and every report was built by hand.

• a secure API ingests the CSV exports from the ERP automatically;
• dashboards on Apache Superset cover sales, staff workload and marketing activity across the three branches;
• non-specialists build their own reports, and reports can be embedded in other web tools;
• open source, so no recurring licence fees and full control over the data.

The result: management follows activity across branches from a single web interface, with figures that update on their own instead of being compiled each month. Read the case study.

The same approach — start from the questions management actually asks, then build only the pipeline those answers need — is how we work with every client. For the engineering side, see our data pipeline on Google Cloud walkthrough.

1. Data audit. We map your sources (ERP, CRM, accounting, spreadsheets), the reports you produce today and the decisions they should support.

2. Roadmap. A short written recommendation: which questions are worth answering with data, which tools fit your size and budget, and what it costs to run.

3. First dashboard. One business area end to end — sources, pipeline, definitions, dashboard — in production, used by your team.

4. Handover or ongoing support. Documentation and training so your team owns it, or our data analytics team keeps running and extending it.