This repository contains my Business Intelligence and Data Analytics portfolio, focused on turning raw database records into strategic, actionable business insights. The work here covers the full end-to-end analytical workflow: from SQL data extraction and transformation to DAX modeling and advanced Power BI visualization.
My core philosophy is that dashboards should not just display numbers; they must solve business problems, identify revenue leaks, and drive decision-making.
Each project in this repository includes:
- Data Engineering: SQL scripts for data cleaning, type casting, and view creation.
- Data Modeling: Relational model design optimized for performance.
- Analytical Logic: DAX measures handling filter contexts and time intelligence.
- Data Storytelling: Dashboards designed with UI/UX best practices for executive scanning.
- Business Insights: Translation of visual trends into strategic recommendations.
- Power BI: Data modeling, advanced DAX (Variables, Context manipulation), Conditional Formatting.
- SQL (PostgreSQL / SQL Server): Data extraction, cleaning (e.g., Date casting), and analytical views.
- Data Architecture: Relational database querying and star-schema principles.
An end-to-end Business Intelligence solution analyzing over $100M+ in global sales data to evaluate market health, product efficiency, and geographical growth.
Focus Areas & Technical Implementations:
- Volume vs. Profitability Analysis: Developed dual-axis logic to distinguish between gross revenue and net profit. Implemented a Top N Dynamic Filter with conditional formatting (Red/Green) to instantly flag "False Heroes" (high-volume products with negative profit margins).
- Time Intelligence & KPIs: Engineered custom DAX measures using
VAR,CALCULATE, and filter overrides to track Year-over-Year (YoY) and Month-over-Month (MoM) performance against baseline targets, avoiding the pitfalls of standard auto-date functions. - Geospatial & Category Tracking: Designed cross-filtered visual grids (Maps and Treemaps) allowing stakeholders to drill down into specific regional performances, revealing high-yield markets like Australia.
- Executive UI/UX: Applied the "F-Pattern" layout for readability, utilizing high-visibility KPI cards for absolute metrics and isolating complex trends into intuitive scatter and line charts.
Key Business Insight: Revenue volume does not equal business health. The dashboard successfully highlighted that while the "Bikes" category drove top-line revenue, specific SKUs in the "Clothing" segment generated high sales volume but operated at a deficit, indicating a need for urgent pricing or logistical adjustments.
My analytical projects follow a consistent, business-first approach:
- Understand the Business Question: Define what stakeholders need to know (e.g., "Are we actually making money on our top-selling items?").
- Data Preparation (SQL): Clean and structure data at the source to ensure Power BI performance.
- Modeling & DAX: Build resilient measures that adapt to complex user filtering.
- Visual Design: Prioritize cognitive ease, removing visual noise and using color strategically (Exception Reporting).
- Insight Generation: Deliver clear, data-backed recommendations.
- SQL: Querying, Views, Data Cleansing.
- Power BI DAX:
CALCULATE,DIVIDE,VAR,MAX,REMOVEFILTERS. - Data Visualization: Exception Reporting, Scatter Plots, Geographic mapping, Dynamic Tooltips.
- Business Acumen: Translating technical data points into Profit & Loss (P&L) realities.
The datasets used (such as the AdventureWorks sample database) are utilized to demonstrate the analytical process, the clarity of the data model, and the ability to extract executive-level insights from raw transactional systems.