Data Science · Business Analytics · Data Analytics
Applied analytics, decision support, and business-facing data projects built around real commercial use cases.
About me
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.
Selected work
A selection of work focused on structured problem solving, decision support, and business-facing analytics.
Finance-focused analytics project designed to demonstrate source-to-target logic, variance analysis, reconciliation controls, and stakeholder-facing reporting.
A full analytical system turning fragmented commercial data into structured visibility, segmentation, recommendation logic, and business-facing outputs.
Flagship project
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.
Designed around an actual business problem rather than a purely academic exercise.
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
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.
How the system works
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.
Visual proof
Each dashboard view supports a different layer of decision-making: overview, diagnosis, customer understanding, and next-best commercial action.
Business problem
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
It required data engineering, validation logic, analytical modelling, dashboard design, and the creation of outputs that could actually be reused and scaled.
Business value
The final objective was not just insight generation, but helping a business see more clearly, prioritise better, and act with more confidence.
A high-level commercial view of revenue, customers, repeat purchasing, ticket size, and channel distribution.