README.md
March 30, 2026 · View on GitHub
📊 Project Dashboards & Insights
This Qlik Sense application provides a comprehensive analysis of sales, inventory, and employee performance. Below are the key analytical views:
| Current vs. Previous Year | Sales & Stock Overview |
|---|---|
 |  |
| Comparison of year-over-year performance, highlighting growth trends and KPIs. | Analysis of inventory levels vs. sales volume across different product groups. |
| Stores Map: Sales & Returns | Store Efficiency Analysis |
|---|---|
 |  |
| Geospatial visualization of sales distribution and return rates by location. | Deep dive into store performance metrics and operational efficiency. |
| Employee Performance | Top Item by Sales |
|---|---|
 |  |
| Scatter plot analysis of sales volume vs. retention and employee KPIs. | Identification of best-selling products and stock-out risks. |
🏗️ Data Architecture & Modeling
The project is built on a robust associative data model, ensuring high performance and data integrity across all dimensions.
Model Highlights:
- Star Schema Optimization: Designed to leverage Qlik’s associative engine while avoiding synthetic keys or circular references.
- Fact & Dimension Separation: Clear architecture separating transactional data (Sales & Returns) from master data (Products, Stores, and Employees).
- Data Integrity: Implementation of unique keys to ensure accurate aggregations across multi-source tables.
🛠️ Technical Highlights
- Dynamic KPIs: Built with advanced Set Analysis to handle YOY comparisons and growth metrics.
- Geospatial Analysis: Integrated maps to identify regional sales trends and return hot-spots.
- Operational Tracking: Correlating stock levels with sales performance to optimize inventory management.
- Data Modeling: Optimized associative model to handle multi-dimensional analysis across stores, products, and employees.