Executive Sales & Operations Analysis (Power BI)

Power BI | SQL Thinking | Business Analytics

This page documents the Exploratory Data Analysis (EDA) and analytical layout created for the Superstore Sales dataset. The primary objective is to transform raw sales records into structured operational insights.

Approach

Data Modeling & Architecture (Star Schema)

To ensure optimal calculation speeds and clean filtering, the flat dataset was broken down into a structured Star Schema within Power Query and the Model View:

• Dimension Extraction: Created Dim_Customers, Dim_Products, and Dim_Locations as separate queries. Removed all duplicate primary keys to build clean reference attributes.
• Fact Cleanliness: Retained foreign keys in Fact_Orders but hid the raw columns from the report view to force clean, dimension-driven filtering.
• Temporal Tracking: Generated a dedicated Dim_Calendar table to support multi-level time intelligence.

Key Highlights

• Financials: Total Sales ($2.26M)
• Volumes: Total Orders (4,922)
• Operations: Average Days for Order Fulfillment (3.96 Days)

Superstore Sales & Analysis Performance Dashboard.pdf

                                               Fig. Superstore Sales & Analysis Performance Dashboard

Visual Layout Strategy

• Trend Nuances: Month-wise Total Sales mapped against time taken to ship to track operational bottlenecks.
• Segment Share: Distribution breakdown of sales by Customer Segment (Consumer vs. Corporate vs. Home Office).