“I’d love to use Power BI, but I don’t know where to start.”
I hear this all the time from demand planners. They see the value of using Power BI dashboards in their demand reviews with Sales and during S&OP meetings. Most currently use Excel but want to level up to move from manual updates to live and dynamic dashboards that drive decisions.
After these conversations, I shared a practical guide on LinkedIn recently to help demand planners take their first steps. If you have been following my content (thank you so much!), you already know I’m very hands-on and action-oriented.
With this same approach, in this edition of my newsletter, I’m going one layer deeper, showing you exactly what actions you can take today (yes, now) and the wins you will see tomorrow.
We will start simple and scrappy, but you will make visible progress and achieve key milestones, even if it’s your very first time using Power BI.
Let’s go step by step.
Step 1. Prepare Just One File
Start today:
Open Excel and save one clean file called SalesHistory.xlsx.
Include four columns only:
| Date | SKU | Region | Actual Sales |
| Jan-2024 | A123 | North | 1,000 |
| Feb-2024 | A123 | North | 1,100 |
| Mar-2024 | A123 | North | 950 |
Keep it simple. No extra sheets. No formatting.
Tomorrow’s win:
You will have a clean, reliable dataset ready to plug into Power BI. No manual cleanup. No mixed formats.
Step 2. Connect It to Power BI
Start today:
- Open Power BI Desktop.
- Go to Get Data → Excel → SalesHistory.xlsx → Load.
- Click Transform Data to open Power Query.
- In Power Query, click:
- Remove Rows → Remove Duplicates
- Use First Row as Headers
- Close & Apply
Tomorrow’s win:
You have automated your data cleaning. When you add new months to Excel, all it takes is one click → Refresh All.
Step 3. Add Your Forecast File
Start today:
Create another file named Forecast.xlsx with these columns:
| Date | SKU | Region | Forecast Qty |
| Jan-2024 | A123 | North | 900 |
| Feb-2024 | A123 | North | 1,050 |
| Mar-2024 | A123 | North | 1,100 |
Import it into Power BI the same way as Step 2.
Then in Power Query, merge both files:
Home → Merge Queries → SKU + Date
Add a new column:
Variance = [Forecast Qty] – [Actual Sales]
Tomorrow’s win:
You have created your first mini-data model. No VLOOKUPs. No broken links. Both datasets refresh together automatically.
Step 4. Build Relationships (Your Model Foundation)
Start today:
In Model View, check that:
- Forecast[SKU] is linked to SalesHistory[SKU].
- Forecast[Date] is linked to SalesHistory[Date].
If not, drag and drop to connect them manually.
Tomorrow’s win:
You now have a working data model. Power BI understands how your tables relate so filters and visuals will behave correctly.
Step 5. Create Your First KPI: Forecast Accuracy
Start today:
Go to Modeling → New Measure and paste this formula:
Forecast Accuracy =
1 – DIVIDE(ABS(SUM(Data[Forecast Qty]) – SUM(Data[Actual Sales])), SUM(Data[Actual Sales]))
Numeric example:
If forecast = 900 and actual = 1,000 → Accuracy = 1 – (100 ÷ 1,000) = 90%
Tomorrow’s win:
You will have a live KPI that updates automatically whenever new data arrives. No Excel recalculations needed.
Step 6. Build One Visual
Start today:
- Go to Report View.
- Drag Date to the X-axis, Actual Sales and Forecast Qty to the Y-axis → choose a Line Chart.
- Add a Card Visual and drag your new measure Forecast Accuracy.
Now you will see:
Forecast Accuracy = 90%
Tomorrow’s win:
You have built a living, breathing dashboard. Update Excel → refresh Power BI → numbers and visuals adjust instantly.
Step 7. Make It Interactive
Start today:
Add a Slicer for “Region” or “SKU.”
Now you can click through North, South, East and watch the chart update instantly.
Tomorrow’s win:
You have turned static reports into interactive insight. Your managers can slice and filter data in seconds instead of waiting for new versions.
Step 8. Add Time Intelligence
Start today:
Create a simple Calendar Table with all dates for the year. Load it into Power BI.
Then add this measure:
Accuracy LY =
CALCULATE([Forecast Accuracy], SAMEPERIODLASTYEAR(Calendar[Date]))
and another:
Accuracy YoY Change =
[Forecast Accuracy] – [Accuracy LY]
Tomorrow’s win:
You can instantly compare how forecast accuracy is trending versus last year without rebuilding anything.
Step 9. Automate Your Refresh
Start today:
After publishing your dashboard to Power BI Service, go to:
Datasets → Schedule Refresh → Add Refresh → Daily at 7 a.m.
Tomorrow’s win:
Your dashboard updates itself every morning automatically. No need for “Can you refresh the report?” messages before the S&OP meeting.
Step 10. Reflect and Expand
Start today:
Open your new dashboard and ask one question:
“What’s one new insight I can share from this?”
It might be that Product A is consistently under-forecasted, or that the South region always overshoots.
Tomorrow’s win:
You will walk into your next meeting with data that’s fresh, visual, and actionable and you will never want to go back to Excel-only reports again.
Final Thought
Power BI is about building a system that learns from your data, automates your manual work, and frees you to think.
Start today with one file and one chart.
By tomorrow, you will have your first working model and a taste of what automated planning feels like.
