Transform Raw Sales Data into an Interactive Power BI DashboardTransform Raw Sales Data into an Interactive Power BI Dashboard
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Overview For this project, I transformed raw, fragmented sales data, from a dataset from Github, into a high-impact interactive dashboard. This wasn't just about visualization; it was about building a reliable data journey. I handled the entire process from querying the raw database to cleaning the records and finally designing a user-centric interface that identifies market leaders and revenue trends. The Tech Stack & Workflow • Data Extraction & Initial Cleaning (MySQL): I used SQL to extract the relevant datasets and perform the initial "heavy lifting." This involved filtering out noise and identifying structural errors within the database. • Quality Assurance (Excel): I utilized Excel for rapid error-checking and data validation, ensuring that the numbers were accurate before moving into the visualization phase. • Transformation & ETL (Power Query): Using Power Query within Power BI, I performed data modeling and transformation to ensure the relationship between "Revenue" and "Sales Quantity" was seamless across different timeframes. • Visualization (Power BI): Developed an interactive UI that tracks KPIs such as Revenue Trends, Top 5 Products, and Market-specific performance. Key Features: • Drill-Down Capabilities: Users can filter by year (2017–2020) to see how market dynamics shifted over time. • Market Analysis: Instant visibility into top-performing regions like Delhi and Mumbai. • Product & Customer Insights: Side-by-side comparisons of the top 5 revenue-generating products and customers.
Strategic Improvements: While the dashboard provides a clear historical view, I have identified the following areas for future optimization to provide even deeper business value:
Profit Margin Analysis: Integrating "Cost of Goods Sold" (COGS) to move from tracking Revenue to tracking actual Profitability.
Predictive Forecasting: Implementing DAX-based forecasting to predict future sales trends based on the 2017-2020 historical data.
Data Label Optimization: Refining display units for smaller markets to ensure granular visibility across all regions.
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