https://github.com/arun-data-analyst/finance-reporting-sql
End-to-end SQL project for project/portfolio finance: schema, seed data, validation, data-quality checks, business queries, and KPI views (Power BI–ready).
https://github.com/arun-data-analyst/finance-reporting-sql
data-analysis data-modeling data-quality database finance kpi portfolio-management powerbi sql sql-server ssms
Last synced: 3 days ago
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End-to-end SQL project for project/portfolio finance: schema, seed data, validation, data-quality checks, business queries, and KPI views (Power BI–ready).
- Host: GitHub
- URL: https://github.com/arun-data-analyst/finance-reporting-sql
- Owner: arun-data-analyst
- License: mit
- Created: 2025-09-22T17:39:30.000Z (14 days ago)
- Default Branch: main
- Last Pushed: 2025-09-22T19:23:04.000Z (14 days ago)
- Last Synced: 2025-09-22T21:17:49.513Z (14 days ago)
- Topics: data-analysis, data-modeling, data-quality, database, finance, kpi, portfolio-management, powerbi, sql, sql-server, ssms
- Language: TSQL
- Homepage:
- Size: 82 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Finance Reporting (SQL + SSMS)
This end‑to‑end SQL portfolio project demonstrates **schema design**, **idempotent seeding**, **validation and data‑quality checks** and **business insights** for a portfolio‑finance use case. The goal is to create a reliable database foundation that can feed reporting tools like Power BI.
> **Tech:** Microsoft SQL Server Management Studio (SSMS).
> **Scope:** Projects, managers, budgets, spend logs, milestones, forecasts, purchase orders and completion status.
## 💂 Repository Structure
```
Finance-Reporting-SQL/
├── sql/
│ ├── 01_create_database.sql
│ ├── 02_create_table.sql
│ ├── 03_insert_data.sql
│ ├── 04_validation_checks.sql
│ ├── 05_data_quality_checks.sql
│ └── 06_business_queries.sql
├── erd/
│ └── finance_reporting.png # ERD exported from Lucidchart
├── docs/
│ └── kpi_reference_explainer.md
├── LICENSE
└── README.md # Project overview (this file)
```
## 🚀 Quickstart
1. Open **SSMS** and connect to your SQL Server instance.
2. Run the scripts in the following order (they are **idempotent** — safe to re‑run):
1. `sql/01_create_database.sql` – creates the `FinanceReporting` database.
2. `sql/02_create_table.sql` – creates all tables & constraints.
3. `sql/03_insert_data.sql` – seeds a realistic demo dataset (~50 projects, managers, spend, milestones, forecasts, POs, completion and KPI glossary).
4. `sql/04_validation_checks.sql` – referential & logic checks.
5. `sql/05_data_quality_checks.sql` – duplicate, null and outlier checks (designed to return **no rows** if clean).
6. `sql/06_business_queries.sql` – business analysis queries and **KPI views** ready for BI tools.
## 🔧 Data Model (ERD)
**Entities:** `manager`, `project`, `spend_log`, `milestone`, `forecast`, `purchase_order`, `project_completion` and `kpi_reference`. The `kpi_reference` table is intentionally standalone as a glossary of KPI definitions and targets.
## 📈 Sample KPI Queries
After running `06_business_queries.sql`, the database exposes several reusable views. Here’s an example of how to inspect projects staying within their approved budgets:
```sql
SELECT project_id,
project_name,
budget,
spend_to_date,
CASE WHEN spend_to_date <= budget THEN 'On budget' ELSE 'Over budget' END AS budget_status
FROM v_BudgetUtilization;
```
These views feed directly into Power BI dashboards for real‑time portfolio monitoring.
## ✅ Data Quality & Trust
- `04_validation_checks.sql` verifies referential integrity, date ranges and other invariants.
- `05_data_quality_checks.sql` inspects duplicates, nulls and improbable values; it is designed to return **no rows** if the data is clean.
## 🔎 What This Demonstrates
- **Schema design** with clear accountability and analysis tables.
- **Idempotent DDL/DML** with readable prints and safeguards.
- **Validation & quality** checks for trustworthy analytics.
- **Business queries & KPI views** that plug directly into BI tools.
- **Polished ERD and documentation** for non‑technical audiences.
## 🚃 Roadmap
Future improvements may include:
- Adding `kpi_result` or `project_kpi` tables to store KPI values over time.
- Introducing earned value and benefit‑realisation tables (CPI/SPI/ROI).
- Implementing indexes and performance tuning as the dataset scales.
## 👤 Author & Acknowledgments
**Arun Acharya** – *Data Analyst (Ottawa, Canada)*
This project is open‑sourced under the **MIT License**. Some parts of the SQL scripts were generated with the assistance of OpenAI Codex, but all scripts were reviewed, refined and finalised by Arun Acharya.
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*If you build upon this project or have suggestions, feel free to open an issue or connect with me on [LinkedIn](https://www.linkedin.com/in/arun-acharya-26077a362).*