Case Studies
Real problems. Real solutions. Results you can take to a board meeting.
Transition Monitor — a 15-day manual scorecard, redesigned as a governed process
A country scorecard that took 15 working days, used a method living in one analyst's head, and silently went stale whenever the data updated. I mapped the AS-IS honestly, designed the TO-BE around a human review gate, and traced 20 business requirements through 43 functional requirements to a live Jira backlog — with a decision log recording why each call was made.
EV Charging Network Performance — which sites actually earn their hardware?
Nine years of Palo Alto public charging data in a seven-page report. The biggest site isn't the best one, fees cut demand but freed up capacity, and the validation page shows the correction that changed which site came out on top. Three-minute video walkthrough included.
Demo case study built on fully synthetic data. "Bottega Nord S.r.l." is a fictional client — every document was generated for the demo. The pipeline, and the problems it solves, are entirely real.
Bottega Nord, a specialty food distributor in Lombardy, receives ~800 supplier order confirmations a month — PDF invoices in three different layouts, Excel exports that never match, semicolon CSVs with Italian decimal commas, and orders typed straight into emails. Every Friday, ~6 hours disappeared into manual reconciliation. Duplicate invoices still got paid. Price increases still went unnoticed.
The Pain
- ~800 supplier confirmations/month across 4 incompatible formats
- ~6 hours of manual reconciliation every Friday
- Duplicate invoices still slipped through and got paid
- Silent price increases went unnoticed
The Build
- Five-stage Python pipeline, built with AI-assisted development (Claude Code)
- Ingestion → extraction → normalization → anomaly detection → reporting
- One command, no manual steps
- Fuzzy-matches supplier & product names against a reference catalog
The Numbers
The Kicker
The data is synthetic. The pipeline isn't. If your team runs on supplier paperwork, spreadsheets nobody trusts, or an inbox that eats a day a week — this same architecture drops onto your data. Let's talk →
An inherited Excel model covering 10 EU markets had become unmaintainable — riddled with hardcoded values, broken as soon as a country changed a rate or assumption, and impossible to audit. Finance, Operations, and Country Leads were all working from different versions of the truth. The business needed a single, governed source of analytical truth that could answer utilisation and revenue questions across an entire European network — in real time.
The Problem
- 10-country model with hundreds of hardcoded formula cells
- No single version of truth — teams used conflicting spreadsheets
- Manual refresh cycle: hours of work per market update
- No auditability or change tracking — high risk in regulated environment
What I Built
- Full star-schema data model on Azure Databricks
- Zero hardcoded formulas — all assumptions governed as parameters
- Power BI front-end delivering live cross-country views
- Automated ETL pipelines replacing manual data collection
Outcomes & Impact
Approach
- Discovery: mapped all 10 country models to identify shared vs. market-specific logic
- Architecture: designed star-schema (fact + dimension tables) to handle multi-country complexity cleanly
- Build: developed on Azure Databricks with parameterised assumption tables instead of hardcoded cells
- Reporting layer: Power BI dashboards with cross-country and drill-down views
- Governance: version control, documentation, and stakeholder training across 4+ country teams
SWARCO's Finance and Operations leadership needed visibility they couldn't get from their existing reports — scattered across systems, too slow to generate, and not designed for decision-making. On top of that, strategic investment decisions on EV charging infrastructure required a rigorous analytical framework that could survive board-level scrutiny. I was brought in to build both: the operational reporting layer and the investment analysis that went to the board.
The Problem
- Finance & Ops reporting fragmented across multiple source systems
- No self-service BI — leadership dependent on manual data pulls
- Investment decisions made without a structured analytical framework
- Board needed a clear, defensible view of infrastructure ROI
What I Built
- Power BI dashboards for Finance leadership — P&L, cost tracking, KPIs
- Operations dashboard — utilisation, deployment status, performance trends
- Investment analysis model evaluating EV infrastructure opportunities
- Board-ready outputs: scenario modelling, risk view, recommendation layer
Outcomes & Impact
Approach
- Stakeholder discovery: workshops with Finance and Ops leads to map reporting needs and decision workflows
- Data architecture: connected disparate source systems into a clean Power BI data model
- Dashboard design: built for decision-making — not just data display. KPIs surfaced at the top, drill-through for detail
- Investment model: structured ROI framework with scenario sensitivity analysis and risk flags
- Board delivery: translated analytical findings into clear executive narrative with recommendation layer