Data Science
Business Intelligence, predictive models and data architecture
From scattered data to measurable outcomes. BI dashboards with Power BI and Tableau, predictive models in Python/R, ETL pipelines and CRISP-DM methodology.
Business Intelligence dashboards
Power BI, Tableau, Looker Studio. Connection to ERP, CRM, database, APIs. Executive dashboards + operational dashboards + scheduled reports.
Predictive models
Demand forecasting, fraud detection, churn prediction, lead scoring. Methodology CRISP-DM, validation with backtesting, deployment with monitoring.
ETL and data pipelines
From spreadsheets to PostgreSQL, BigQuery, Snowflake. Tools: Python, n8n, Airbyte, Fivetran, dbt. Validated data quality at each layer.
Historical analysis and BI strategy
For organizations with scattered data and no clear analytics direction. Diagnosis of sources, prioritization of use cases, roadmap to ROI.
Frequently asked questions
What is the difference between Business Intelligence (BI) and predictive models?
BI looks at the past and present: how much I sold last month, which customers are most profitable, where operational costs go. It supports informed decisions with data you already have. Predictive models look forward: how much I will sell next month, which customer is about to churn, which product will run out. They help anticipate and act in advance. Most serious projects combine both.
Can you connect Power BI or Tableau to my ERP, CRM or SQL database?
Yes. Native connectors for SQL Server, PostgreSQL, MySQL, MariaDB, Oracle, SAP, Salesforce, HubSpot, Google Sheets, Excel, REST APIs, and almost any source. For legacy sources without standard connector, we build ETL pipelines with Python or tools like n8n, Airbyte or Fivetran.
Do you build demand, fraud, churn or lead scoring prediction models?
Yes. Demand forecasting (regression, ARIMA, Prophet, gradient boosting), fraud detection (supervised classification + rules), churn (classification + survival analysis), lead scoring (classification). Each model requires minimum history and sufficient data quality; first step is always to evaluate feasibility before proposing.
How does a data project start if I don't have a pipeline yet?
We start with a diagnosis of available data sources (Excel, database, system, paper) and a concrete use case. First we organize the richest source and build an initial dashboard (results in 2-4 weeks). In parallel, we define a more robust pipeline to scale. The classic mistake is wanting to organize EVERYTHING before showing results: kill that idea, show value fast and deepen later.
What technical stack do you work with?
Python (pandas, scikit-learn, statsmodels, PyTorch for advanced cases), R when statistical analysis is more demanding, SQL in all variants (PostgreSQL, SQL Server, BigQuery), Spark for large volumes, and visualization tools: Power BI, Tableau, Looker Studio, Grafana depending on the case.
How long until a useful dashboard is visible?
A simple BI dashboard with clean data: 1 to 2 weeks. With source cleanup and ETL: 4 to 8 weeks. Predictive models: 6 to 12 weeks, depending on data volume and validation. All projects start with a free 30-minute diagnosis to define realistic scope.
Free data diagnosis
30 minutes without commitment. We map your data sources and prioritize a use case with realistic ROI.