Power BI Sales Dashboard

Key Metrics

$2.4M

Revenue

18.5%

Profit Margin

4.2K

Orders

3

Regions

Business Context

Problem & Objective

Business Question

How is the company performing across regions, product lines, and time? Where are the revenue and profitability gaps that leadership should prioritize for action?

Business Value

A single self-service dashboard that lets commercial and operations teams filter by region, category, and time period, revealing top performers and areas requiring attention without relying on ad-hoc reports.

Methodology

STAR Framework

S

Situation

A retail business tracked sales across multiple regions and categories in raw spreadsheets, making it hard to compare performance and detect trends quickly.

T

Task

Design an interactive Power BI dashboard that consolidates sales data, computes KPIs, and enables dynamic filtering by region, category, and time.

A

Action

Cleaned and shaped data with Power Query, built a star-schema model, created DAX measures (Revenue, Profit, Margin, YoY), and designed a multi-page KPI dashboard.

R

Result

A self-service BI solution that surfaces top-performing regions and categories, supports profitability analysis, and accelerates commercial decision-making.

Dataset

Data Overview

Data Model

Star-schema: fact sales + dimension tables
12 months of transactional data
3 sales regions
Product categories & sub-categories
Customer segments

Tools & Techniques

Power Query for ETL and data cleaning
DAX measures & calculated columns
Relationship & star-schema modeling
Slicers & report-level filters
Row-Level Security configuration

Process

Methodology

1

Data PreparationConnected and shaped raw data sources with Power Query: handled missing values, standardized formats, and merged related tables.

2

Data ModelingBuilt a star-schema model with clear primary-to-foreign key relationships between fact and dimension tables to optimize performance.

3

DAX MeasuresCreated reusable measures: Total Revenue, Total Profit, Profit Margin %, orders, and Year-over-Year change for time intelligence.

4

Dashboard DesignDesigned an interactive report with KPI cards, trend charts, and region/category breakdowns connected through slicers for dynamic filtering.

Findings

Key Insights

Top-performing regionOne region consistently drives the largest revenue share and highest profit margin, making it a benchmark for others.

Marginal categoriesSome high-revenue categories show thin margins, flagging pricing or cost optimization opportunities.

Seasonal patternsClear monthly seasonality in revenue and orders enables better inventory and promotion planning.

Segment behaviorCustomer segments differ sharply in basket size and profitability, guiding targeted commercial actions.

Dashboard

Report Overview

The report includes the following core visuals, all interactive and connected to report-level slicers for dynamic filtering.

KPI cards: Revenue, Profit, Margin, Orders

Revenue & profit trend over time

Sales by region

Revenue by product category

Profitability by customer segment

Geographic distribution of sales

Margin analysis with dynamic filtering

Slicers: region, category, period

Limitations

Aggregated dataset limits customer-level granularity.
Forecast accuracy depends on historical data stability.
Data model could be extended with more dimensions.

Next Steps

Add predictive forecasting with DAX time intelligence.
Integrate scheduled real-time data refresh.
Publish to Power BI Service for broader sharing.