How I turned a multi-year building invoice ledger into an interactive analytics dashboard — and why it changed how I think about operations, data, and engineering.
"The best code is the code that quietly removes friction from people's work."
🏢 Context: Assistant Building Manager, Real Data, Real Stakes
Over a six-week stretch, I was working as an Assistant Building Manager at a large residential building in the south of Sydney, closely shadowing an experienced Building Manager with 25+ years across construction, water systems, and large-scale facilities operations.
Alongside day-to-day operations, I also built small internal tools — like a Lift Finder utility and myRoster (a shift automation app) — whenever I noticed repetitive friction in the workflow.
This role exposed me to the full operational lifecycle of a high-rise building:
- Stakeholder management: Owners Corporation, committee members, residents, strata, contractors
- Maintenance workflows: diagnosis → contractor selection → approval → execution → validation
- Compliance & regulation: AFSS, fire services, inspections, reporting
- Financial reality: invoices, budgets, approvals, recurring vs reactive spend
And obviously — massive amounts of data.
Around the same time, I accepted a Data Analyst role at St Catherine’s School (Read more), which reinforced the same mindset: treat operational noise as structured data waiting to be explored.
Every single decision eventually traced back to one place.
📁 The Starting Point: An Excel Invoice Ledger
Inside the building's shared drive (S://BuildingName/Finances/Invoices) lived an unassuming file:
- A multi-sheet invoice ledger
- Spanning 4+ years
- Thousands of rows
- Dozens of contractors
- Hundreds of services
- GST, dates, approvals, variations, reworks
On paper, it was "just Excel."
In reality, it was:
The financial memory of the building.
Every question led back to it:
- How much are we spending on fire services?
- Is this contractor consistently expensive or just a one-off?
- Why did costs spike mid-2023?
- Are we reacting to problems or investing preventatively?
⚠️ The Problem: Excel Doesn't Scale with Questions
Why Excel Became the Bottleneck
| Excel Reality | Building Management Reality |
|---|---|
| Manual filters | Questions come fast |
| Pivot tables break | Context changes constantly |
| One question at a time | Multiple stakeholders need answers |
| 10-minute turnaround | Decisions need justification now |
| Version control chaos | Audit trail required |
The typical workflow:
- Open Excel (wait for thousands of rows to load...)
- Navigate to the right sheet (Building A? B? C?)
- Apply filters (Year... Contractor... Service...)
- Create pivot table (if you remember how)
- Screenshot or copy-paste results
- Repeat for the next question 5 minutes later
This wasn't analysis.
It was manual overhead.
And in building management, manual overhead means:
- Slower contractor evaluations
- Delayed budget approvals
- Missed spending patterns
- Reactive instead of preventative decisions
🎓 The Engineering Lens: Treating Excel as a Dataset
At the same time, I'm pursuing a Master's in Software Engineering & Artificial Intelligence (see my open-source repo) — so my instinct kicked in:
This isn't an Excel problem.
This is a data exploration problem.
✅ The ledger already had:
- Time-series data (4+ years of invoices)
- Categorical dimensions (building, contractor, service)
- Natural aggregations (monthly spend, contractor totals)
- Long-term trends (seasonal patterns, cost escalation)
- Outliers that matter financially (unexpected spikes, recurring issues)
The data was already structured.
Microsoft Excel was just the wrong interface for exploration.
So I built a tool in Python that lets non-technical users explore it safely.
🛠️ The Solution
The goal was simple: turn a static spreadsheet into a safe, visual, self-service analytics tool for non-technical users.
I built an interactive analytics dashboard using Python + Pandas + Streamlit to read from the ledger.xlsx file.
In minutes, I could answer questions that used to take 10–15 minutes of Excel wrestling — and export the evidence for emails, audits, or committee meetings.
What It Does
Upload a raw .xlsx invoice ledger → Instantly:
- 🏢 Filter by building (or view "All" for consolidated insights)
- 📅 Filter by year(s) (multi-select: 2023 + 2024)
- 👷 Filter by contractor (compare spending across vendors)
- 🔧 Filter by service (HVAC vs. Plumbing vs. Fire Services)
- 🔍 Search by invoice number (quick lookups)
- 📆 Date range picker (Q3 analysis, seasonal trends)
- 💰 Amount range slider (focus on high-value invoices)
Auto-compute:
- Total spend (GST inc.)
- Invoice count
- Unique contractors
- Service diversity
Visualize:
- 📊 Contractor spend breakdown (bar chart + color-coded heatmap)
- 📈 Monthly expense timeline (spot trends, anomalies)
- 🎨 Cost concentration (which contractors dominate spend?)
- 🔄 Multi-year comparisons (year-over-year changes)
Export:
- 📥 Download filtered results as CSV (for reports, audits, approvals)
No pivot tables.
No broken formulas.
No "give me 10 minutes to check."
🏗️ Tech Stack & Architecture
The app follows clean software engineering principles — modular, maintainable, production-ready.
Technology Choices
| Layer | Technology | Why |
|---|---|---|
| Language | Python 3.10+ | Standard for data + automation |
| Web Framework | Streamlit | Rapid UI development, zero JavaScript |
| Data Processing | Pandas | Industry-standard DataFrames |
| Excel Integration | openpyxl | Multi-sheet Excel parsing |
| Visualization | Streamlit charts + Pandas styling | Built-in, no external dependencies |
| Deployment | Streamlit Cloud | Free hosting, GitHub integration |
Project Structure
invoice-ledger/
├── app.py # Main UI orchestration
├── data_loader.py # Excel parsing & data cleaning
├── filters.py # Interactive filter components
├── analytics.py # Metrics, charts, visualizations
└── requirements.txt # Dependencies
Why modular?
- ✅ Single Responsibility — Each file does one thing well
- ✅ Testable — Unit test each component independently
- ✅ Maintainable — Know exactly where to make changes
- ✅ Reusable — Port components to other PropTech projects
- ✅ Readable — Onboard new devs in minutes, not hours
🔍 Full module-by-module breakdown available here → docs/ARCHITECTURE.md
📊 The Impact: Before vs. After
| Metric | Before (Excel) | After (Dashboard) | Improvement |
|---|---|---|---|
| Query Time | 10-15 minutes | ~2 minutes | 80% faster |
| Multi-building Analysis | Open 3 files manually | Single "All" view | 3x faster |
| Visualizations | Manual pivot tables | Auto-generated charts | 100% automated |
| Reproducibility | "How did I filter this again?" | Click filters → Export CSV | 100% consistent |
| Contractor Comparison | Side-by-side spreadsheets | Color-coded heatmap | Instant insights |
| Trend Analysis | Copy-paste into separate tool | Built-in timeline chart | Native support |
| User Training | "Here's how Excel works..." | "Upload and click" | Zero onboarding |
🎯 Real-World Use Cases
1. Contractor Performance Review
Question:
"How much did we spend with ABC Plumbing across all buildings in 2024?"
Old way:
- Open 3 Excel files (Building A, B, C)
- Filter each by contractor
- Sum manually
- 5 minutes
New way:
- Select "All buildings"
- Filter contractor: "ABC Plumbing"
- Filter year: "2024"
- Answer in 30 seconds
The result isn’t just faster — it’s far more presentable, making it suitable for committee meetings, audits, and stakeholder discussions.
2. Budget Planning
Question:
"What's our average monthly HVAC spending?"
Old way:
- Filter by service
- Create pivot table by month
- Calculate average
- Hope you didn't break formulas
- 10 minutes
New way:
- Filter service: "HVAC"
- View monthly timeline chart
- Answer visible immediately
3. Audit Trail for Committee
Question:
"Show me all fire services invoices over $5,000 from Q4 2024"
Old way:
- Filter by service
- Filter by date range
- Filter by amount
- Screenshot or print
- 12 minutes
New way:
- Apply 3 filters
- Click "Download CSV"
- Attach to email
- Answer + deliverable in 2 minutes
4. Anomaly Detection
Question:
"Why was November 2023 spending so high?"
Old way:
- Create pivot table by month
- Spot the spike
- Filter November 2023
- Manually inspect rows
- 15 minutes
New way:
- View monthly timeline chart (spike visible instantly)
- Filter date range: November 2023
- Heatmap shows which contractor(s) caused it
- Root cause in 3 minutes
Fun Fact
Built in 1 day as a side project during my working hours.
Origin story:
Started in the southB/ directory of my masters-swe-ai repo as a quick experiment. When I realized how useful it was, I:
- Cleaned up the code
- Made it modular
- Created standalone repo
- Wrote comprehensive documentation
- Deployed publicly
🔗 Links & Resources
| Resource | Link |
|---|---|
| GitHub Repo | github.com/lfariabr/invoice-ledger |
| Source Code (southB origin) | masters-swe-ai/southB |
| Live Demo | streamlit app |
| Excel Template (fake data) | download & explore the data safely - fake data |
🚀 Future Roadmap: From Dashboard to PropTech Platform
While the current version solves the immediate problem, here's the possible expansion plan:
1. Database Backend (PostgreSQL/Supabase)
Current: Upload Excel each time
Future: Persistent database with incremental updates
Benefits:
- Historical version control
- Audit trail (who queried what, when)
- Multi-user access with authentication
- API for integration with other building systems
2. Predictive Analytics (ML)
Use cases:
- "Based on 4 years of data, predict next quarter's HVAC spending"
- "Which contractors are trending expensive year-over-year?"
- "Seasonal patterns: fire services spike in winter?"
Technical approach:
- Time-series forecasting (Prophet)
- Contractor spending clustering
- Anomaly detection for unusual invoices
3. Automated Reporting
What it does:
Schedule weekly/monthly reports via email
Example workflows:
- Every Monday: Summary of last week's spending
- End of month: PDF report with charts for Owners Corporation
- Budget alerts: Email if spending exceeds threshold
4. Integration with Building Management Systems
Current: Standalone dashboard
Future: Connect to existing PropTech stack
Integrations:
- AFSS systems — Auto-import fire inspection costs
- Strata software — Sync budget approvals
- Contractor portals — Pull invoices directly
- Power BI — Feed data to enterprise dashboards
Let's Connect!
Building Invoice Ledger Analytics was a perfect case for me to turn operational friction into engineering opportunity. If you're:
- Working in PropTech or building management
- Building internal tools for finance or operations
- Interested in Python automation and data visualization
- Looking for practical Streamlit examples
- Hiring for backend/data/PropTech roles
I'd love to connect:
- LinkedIn: linkedin.com/in/lfariabr
- GitHub: github.com/lfariabr
- Portfolio: luisfaria.dev
Tech Stack Summary:
| Current | Future Extensions |
|---|---|
| Python, Streamlit, Pandas, openpyxl | PostgreSQL/Supabase, ML (Prophet/LangChain), Building System APIs (AFSS, Strata), React Native/PWA |
Built with ☕ and firsthand building management experience
"The best code is the code that quietly removes friction from people's work."
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