Build a Power BI sales dashboard with free sample data

Get the dataset

This tutorial uses realistic B2B sales data — invoice line items with customers, products, categories, quantities, prices, costs and margins. It's correlated (segment-driven buying, real price/margin relationships), so your charts will actually tell a story.

Download sample sales data (CSV/Excel) → Customize in the generator

Tip: set a seed (e.g. b2b-demo) so you can regenerate the exact same file later or share it with a teammate.

Steps

1. Load the CSV into Power BI

   In Power BI Desktop, choose Home → Get data → Text/CSV, pick your downloaded b2b_invoices.csv, then Transform Data to open Power Query. Confirm order_date and ship_date are Date types and the numeric columns (quantity, unit_price, revenue, cost, margin) are Decimal/Whole number. Click Close & Apply.

2. Add a date table

   Create a proper calendar so time intelligence works. In Modeling → New table:

   Calendar =
   ADDCOLUMNS(
       CALENDAR ( MIN ( Sales[order_date] ), MAX ( Sales[order_date] ) ),
       "Year", YEAR ( [Date] ),
       "Month", FORMAT ( [Date], "MMM" ),
       "MonthNo", MONTH ( [Date] )
   )

   Then relate Calendar[Date] → Sales[order_date] (one-to-many), and mark Calendar as the date table.

3. Write the core DAX measures

   Create these in a measures table:

   Total Revenue = SUM ( Sales[revenue] )
   Total Cost    = SUM ( Sales[cost] )
   Total Margin  = SUM ( Sales[margin] )
   Margin %      = DIVIDE ( [Total Margin], [Total Revenue] )
   Orders        = DISTINCTCOUNT ( Sales[invoice_no] )
   Avg Order Value = DIVIDE ( [Total Revenue], [Orders] )
   
   Revenue MoM % =
   VAR Prev = CALCULATE ( [Total Revenue], DATEADD ( Calendar[Date], -1, MONTH ) )
   RETURN DIVIDE ( [Total Revenue] - Prev, Prev )

4. Build the visuals

   Lay out a one-page report: KPI cards for Total Revenue, Margin %, and Avg Order Value; a line chart of Total Revenue by Calendar[Date]; a bar chart of Total Revenue by category; a matrix of customer × Total Revenue sorted descending; and a slicer on segment (segA–segD). Because the data is segment-driven, you'll immediately see the long tail of small accounts and the handful of whales.

5. Add interactivity & polish

   Add a category slicer and a date-range slicer. Conditional-format the customer matrix on Margin % to spot low-margin accounts. Want a bigger or different file? Regenerate with more rows or turn on anomaly labels in the generator to demo exception reporting.

Why this dataset makes a better demo

Random mock data produces flat, meaningless charts. This file is generated from a simulated distributor: customer segments drive order frequency and size, big accounts get thinner margins, and occasional large buys create realistic spikes. So your dashboard shows genuine patterns — margin compression on top accounts, category mix, and a believable revenue trend — exactly what you want when presenting BI skills.

Keep going