Starting something new always feels a little overwhelming — but once you take the first step, it gets exciting very quickly.

This was exactly my experience when I created my first ever dashboard in Power BI.
Instead of following a ready-made template, I decided to work with a raw dataset and build everything step by step.



This blog post is a walkthrough of how I did it.

📂 Dataset: Two Separate CSV Files

For this project, I used two CSV files — Orders and Details.

The combined dataset included:

  • Date (stored as text)
  • Amount
  • Profit
  • Quantity
  • Category and Sub-category
  • Customer Name
  • Payment Mode
  • State

I imported both files into Power BI and created a relationship between the tables. This was my first experience building a proper data model, and it helped me understand how different tables interact inside Power BI.

🧹 Step 1: Cleaning and Preparing the Data

1.1 Converting Text to Date

One of the first challenges I faced was with the Date column.
The dates were stored as text, and Power BI wouldn’t convert them directly into a proper date format.

My solution:

  • I split the column into three parts — Day, Month, and Year.
  • Then I merged these columns back and used DAX to convert them into a valid Date type.

This simple workaround gave me better control over how dates were displayed and sorted in the visuals.

🧮 Step 2: Creating a Custom Column with DAX

The dataset didn’t have an “Average” value, which I needed to make the dashboard more meaningful.
So I created a custom DAX column:

Average = [Amount] / [Quantity]

This calculation gave me a new field that could be used in KPIs and visualizations — a small but powerful addition that improved the insights in my dashboard.

📊 Step 3: Building the Dashboard

With the data model ready, I started designing the dashboard layout.

Visualizations used:

  • Bar Chart — Sales by Category and Sub-Category
  • Pie & Donut Charts — Distribution by Category and Payment Mode
  • Line & Area Charts — Trends over time
  • Map — Sales by State
  • Slicers — For Quarter and State filtering

I also added card visuals for total Amount, Profit, Quantity, and Average.

🖼️ Step 4: Conditional Formatting and Interactivity

I wanted the dashboard to feel dynamic, not static.
Here’s what I added:

  • Conditional formatting to highlight profit values — Power BI automatically handled positive and negative ranges.
  • Slicers and filters to allow users to interact with the data.
  • Table relationships between Orders and Details to make drill-downs smooth.

The dashboard now allowed me to explore sales performance from multiple angles — time, location, product category, and payment mode.

🚀 Final Dashboard

The final output looked clean, simple, and interactive.

Included elements:

  • KPIs for total amount, profit, quantity, and average
  • Visual breakdown by category, payment mode, and customer
  • Trend analysis over time
  • Map visualization by state
  • Easy drill-down and filtering

📸 (Insert screenshots of your dashboard here with alt text like “Power BI Sales Dashboard” — this helps with SEO)

🧠 Key Learnings

  • Working with multiple CSV files taught me how to build a proper data model.
  • Converting text to date manually gave me confidence in data transformation.
  • A single DAX formula can make a dashboard more meaningful.
  • Interactivity through slicers and conditional formatting turns a static report into a dynamic experience.
  • Clean design + clear metrics = better storytelling with data.

🏁 Final Thoughts

This was my first Power BI project, but it gave me a solid foundation to build on.

If you’re a beginner like me, my advice is simple:
Start small. Pick a dataset. Experiment. Break things. Fix them. Learn.

I’ll be sharing more of my Power BI learning journey here on the blog, including advanced DAX, real-world dashboards, and tips to build a portfolio.

👉 Follow along if you want to learn Power BI step by step.

🔍 SEO Keywords Used

Power BI dashboard • Power BI beginner project • Power BI data modeling • Power BI slicers and filters • Power BI date conversion • DAX calculation • Power BI portfolio project