Designed around an actual business problem rather than a purely academic exercise.
Data Science · Business Analytics · Data Analytics
Turning data into business decisions.
Applied analytics, decision support, and business-facing data projects built around real commercial use cases.
About me
Analytics with business context.
I build business-facing analytics that turn commercial data into clearer decisions, structured visibility, and more useful reporting.
My background combines recent training in Data Science and Business Analytics with a strong interest in solving real business problems through structured analytical work.
I am especially interested in projects where data is not only explored, but translated into tools, decision-support systems, and outputs that companies can actually use.
Flagship project
From fragmented commercial data to a usable decision-support system for SMEs.
This master’s project was built around a real business need: turning scattered commercial data into a practical system for visibility, analysis, and decision-making.
From raw data and validation to dashboarding, analytical outputs, and recommendations.
Created to improve visibility, support action, and make commercial decisions clearer.
The starting point
Different channels, different data, no single decision-support layer.
Sales, customers, and product data were spread across multiple operational sources. The challenge was not only to analyse the data, but to transform it into something structured, reliable, and actually useful for business decisions.
WinOmega ERP PrestaShop eBay Miravia Commercial Reporting
How the system works
One pipeline, one analytical layer, multiple business-facing outputs.
The project combined data ingestion, cleaning, quality control, harmonisation, and analytical modelling into a structured workflow capable of supporting reporting, segmentation, recommendation logic, and decision-ready outputs.
- Data integration across multiple business sources
- Validation and quality-control checks
- Standardised analytical dataset creation
- Customer and commercial analysis
- Dashboard-ready and recommendation-ready outputs
Visual proof
What the system actually produces
Each dashboard view supports a different layer of decision-making: overview, diagnosis, customer understanding, and next-best commercial action.
Business problem
Data existed, but visibility did not.
The project addressed a common SME problem: having commercial data available, but lacking a structured system to convert it into useful visibility and decisions.
Technical execution
More than a dashboard.
It required data engineering, validation logic, analytical modelling, dashboard design, and the creation of outputs that could actually be reused and scaled.
Business value
Designed for action.
The final objective was not just insight generation, but helping a business see more clearly, prioritise better, and act with more confidence.
General business overview
A high-level commercial view of revenue, customers, repeat purchasing, ticket size, and channel distribution.