This project showcases the end-to-end development of a Business Intelligence ecosystem, focused on the Gold Layer (Business Layer) architecture. By transforming raw transactional data into high-performance analytical views, I engineered a robust foundation for strategic decision-making. The project concludes with an interactive dashboard that translates complex SQL logic into actionable insights regarding sales growth, product lifecycles, and customer demographics.
View the final result: >
The engine of this project lies in the business logic implemented via SQL Server. Advanced views were created to automatic key metrics:
- Year-Over-Year (YoY) Performance: Utilization of window functions (LAG, OVER) to calculate annual growth per product.
- Cumulative Analysis: Implementation of Running Totals and Moving Averages to identify long-term sales trends.
- Data Segmentation: Dynamic classification of products by cost ranges and customers by geographic location.
- Participation Metrics: "Part-to-Whole" calculations to determine the revenue impact of each product category.
The main script generates the following logical tables:
| Vista | Propósito Analítico |
|---|---|
gold.cumulative_analysis |
Historical trends and monthly cumulative totals. |
gold.performance_analysis |
Comparison of current sales vs. average and previous year. |
gold.part_to_analysis |
Contribution percentage by product category. |
gold.data_segmentation |
Inventory grouping by cost ranges. |
gold.customers_country |
Demographic distribution of the customer base. |
Based on the integration of SQL views with the visualization tool:
- Category Dominance: Bikes accounted for 96.46% of total sales, identifying them as the primary business driver.
- Temporary Growth: A significant peak in sales and customer acquisition is observed between 2013 and 2014.
- Cost Efficiency: 37.29% of products sold fall within the "Below 100" range, suggesting high sales volume for entry-level products.
- Global Distribution: North America stands out as the market with the highest customer density.
- Clone the repository:
git clone https://github.com/leandrogallo-dev/data-analytics-dashboard.git
sanoyfresco-sales-analytics
│
├── dashboard/ # dashboard from power BI
├── datasets/ # Dataset used for the analysis
├── scripts/ # SQL scripts with analytical queries
│
├── docs/ # Project documentation
│
├── README.md # Project documentation
└── LICENSE
This project is licensed under the MIT License. You are free to use, modify, and share this project with proper attribution.
Hi! I'm Leandro Gallo, a Systems Engineering student from Argentina with a strong interest in:
- Data Engineering
- Data Analytics
- Backend Development
- Cybersecurity
- Software Development
I enjoy building data pipelines, automation tools, and data-driven systems, and I am currently developing projects to strengthen my skills in data architecture, SQL development, and analytics.
This repository is part of my technical portfolio, where I showcase projects related to:
- Data Warehousing
- ETL Pipelines
- Data Modeling
- Analytics
📧 Email
leandrogallo698@gmail.com