What Should You Create in Power BI Desktop DP900?

Jumping into Power BI Desktop for the first time can feel like you’re sitting at the control panel of a spaceship, with countless buttons and options you don’t yet understand. You know it’s powerful, but where do you even begin? This guide will walk you through what you should build first, focusing on core concepts that align with data fundamentals (like those in exams such as the DP-900) to ensure you start your journey on solid ground.

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Start with Strong Foundations: Connect and Shape Your Data

Before you create a single chart, the most critical work happens in the background. A great report is built on clean, well-structured data. Rushing this step is like building a house on a shaky foundation - it’s bound to cause problems later.

1. Connect to Your Data Source

Power BI can connect to an incredible number of data sources. For your first project, keep it simple. A straightforward Excel file or CSV is perfect for learning the ropes.

• Flat Files (Excel/CSV): This is the easiest starting point. You can download sample datasets for something universal, like a simple sales or marketing campaign report. This represents non-relational data, where all the information is typically in one big table.
• Relational Databases (like Azure SQL, SQL Server): If you’re preparing for something like the DP-900, you understand the importance of relational data. This is data split into multiple tables (e.g., a Customers table, an Orders table, and a Products table) that are linked together. While slightly more advanced to start, Power BI is specifically designed to handle this flawlessly.

To connect, simply go to the Home tab, click Get Data, and select your source.

2. The Most Important First Stop: Power Query Editor

Once you’ve connected to your data, you’ll be prompted to either “Load” it or “Transform Data.” Always, always choose Transform Data first. This opens the Power Query Editor, which is where the real magic begins.

Power Query is where your data gets cleaned and prepared for analysis. Spending time here will save you hours of headaches down the line. Here are a few essential steps you should always perform:

Review and Correct Data Types

Power BI makes a guess at the data type for each column (Text, Whole Number, Decimal Number, Date, etc.). Often, it gets it wrong.

• Look for numbers that are incorrectly tagged as text. You can't perform calculations on text!
• Ensure your date columns are actually set to the "Date" or "Date/Time" type.
• Proper data types are non-negotiable for accurate calculations and time-based analysis.

Handle Nulls and Errors

Real-world data is messy. There will be blank cells (nulls) and errors. Decide what to do with them. You can filter them out, replace them with a zero or another placeholder, or leave them if it’s appropriate for your analysis. Use the “Replace Values” and “Remove Errors” functions in the ribbon.

Clean Up Unnecessary Columns and Rows

Did your export include empty rows at the bottom? An irrelevant "Notes" column? Remove them now. A lean dataset is faster and easier to work with. Use the “Remove Columns” and “Remove Rows” options.

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3. Build Your Data Model

After you’ve cleaned your data in Power Query and clicked “Close & Apply,” you’ll return to the main Power BI window. The next step is to check your Data Model view represented by three icons on the left-hand pane.

If you connected to a single Excel file, you’ll just see one table. But if you pulled in multiple related tables (like Orders and Products), this is where you establish the relationships between them.

Power BI often auto-detects these, but it's important to verify them. Think of it like this: your Orders table has a ProductID column, and so does your Products table. You need to create a relationship (a line) between these two columns. This tells Power BI how the tables are connected, allowing you to, for example, analyze sales figures from the Orders table by product category from the Products table.

Your First Meaningful Report: Creating a Sales Dashboard

Now for the fun part: visualization! With a clean and well-structured data model, building useful visuals becomes incredibly straightforward. A sales dashboard is a perfect first project because the metrics are intuitive and universally understood.

Step 1: Define Your Key Performance Indicators (KPIs)

Before dragging and dropping, ask yourself: “What are the most important questions I need to answer?” For a sales report, this might be:

• What are our total sales?
• How much profit have we made?
• Which product categories are selling the most?
• Which regions are our top performers?
• How are sales trending over time?

These questions directly translate into visuals.

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Step 2: Build Your Visuals One by One

Select a visual type from the Visualizations pane and drag your data fields into the appropriate boxes (like Axis, Legend, Values).

Headline Numbers with the Card Visual

Your main KPIs, like "Total Sales" and "Total Profit," should be front and center. The best visual for this is the Card.

• Drag a Sales field into a Card visual. It will automatically sum it up.
• Create another Card for Profit. These big-number callouts give an immediate snapshot of performance.

Comparing Categories with a Bar or Column Chart

To see which product categories perform best, a Clustered Column Chart is perfect.

• Drag the Product Category field to the X-axis.
• Drag the Sales field to the Y-axis.
• Instantly, you have a clear comparison of your categories. You can sort it largest to smallest for even faster insights.

Visualizing Trends with a Line Chart

To see how sales are trending over time, a Line Chart is the go-to choice.

• Drag your OrderDate field to the X-axis. Power BI will automatically create a date hierarchy for you (Year, Quarter, Month, Day).
• Drag the Sales field to the Y-axis.
• Now you can see your sales performance over time and spot any interesting patterns or seasonality.

Displaying Geographic Data with a Map

If your data includes geographic information like city, state, or country, the Map visual is an impressive and effective way to display it.

• Drag a geographic field, like State or Country, to the Location field.
• Drag a numeric field, like Sales, to the Bubble size field. Now you’ll see bubbles on the map, with larger bubbles representing higher sales in that area.

Go Beyond Static Charts: Making Your Report Interactive

One of Power BI's most powerful features is interactivity. Your report shouldn't just be a static image, it should be a dynamic tool for exploration.

Add Slicers for Easy Filtering

A slicer is essentially a visual filter on a report page. Instead of using a clunky filter pane, users can click buttons to segment the data.

Select the Slicer visual from the Visualizations pane. Drag in your OrderDate field. Now users have an easy-to-use slider to select a specific date range. You can even create slicers for product categories or regions.

Embrace Cross-Filtering

This happens by default in Power BI, and it's spectacular. Click on a bar in your "Sales by Category" chart. Watch how every other visual on the page instantly filters to show data only for that category. Your map will update, your line chart will change, and your KPI cards will reflect the numbers for just that category. This allows you to naturally discover insights as one question leads to another.

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Adding Intelligence with DAX (Data Analysis Expressions)

DAX is Power BI’s formula language, similar to Excel formulas but far more powerful. It can seem intimidating, but you can get started with some simple, high-value calculations.

A great first measure to create is Profit Margin. It wasn't in your original dataset, but you can calculate it! In the Home tab, click New Measure. A formula bar will appear. Type in:

Profit Margin = (SUM(Sales[Profit]) / SUM(Sales[Revenue]))

Hit enter. You've created a new measure! Now you can use this in your visuals, just like any other field. For example, in a card visual to show your overall margin or in a line chart to show it as your KPI to track. You can also convert it to a percentage to properly visualize the numbers.

Final Thoughts

By following these steps, you've gone from raw data to a fully interactive report. You've learned how to connect, clean, and model your data, build core visuals that answer key business questions, make your report dynamic, and even write your first DAX measure. This foundational knowledge provides a powerful platform to build virtually any report you can imagine.

While mastering Power BI is a valuable skill, the reality is that the learning curve can be steep, and much of your time is spent on manual data prep instead of uncovering insights. At Graphed , we’ve found that many marketing and sales teams just need quick answers without enduring a complex setup. We built our platform to address this by allowing you to connect sources like Google Analytics, Shopify, and Salesforce in seconds. Instead of navigating report builders and learning DAX, you just ask questions in plain English - like "create a dashboard showing my Shopify revenue by marketing channel last quarter" - and watch a live, interactive dashboard build itself in real-time.