Why Scatter plots in Power BI? - Purpose & Use Cases

The Big Idea

What if you could spot hidden sales opportunities just by looking at a simple graph?

The Scenario

Imagine you have a big list of sales data with numbers for each product and region. You try to find patterns by writing down numbers on paper or using a simple table. It's hard to see how sales relate to advertising or customer visits just by looking at rows and columns.

The Problem

Manually scanning tables or spreadsheets to find relationships is slow and tiring. You might miss important trends or outliers because numbers alone don't show how two things connect. It's easy to make mistakes or overlook key insights when you can't see the data visually.

The Solution

Scatter plots let you put two sets of numbers on a graph, showing how they relate. You can quickly spot patterns, clusters, or unusual points. Power BI makes it easy to create interactive scatter plots that update as you filter data, helping you understand complex relationships at a glance.

Before vs After

✗ Before

Product | Sales | Advertising
A       | 100   | 50
B       | 200   | 80
C       | 150   | 60

✓ After

Scatter plot: X = Advertising, Y = Sales, each dot = Product

What It Enables

Scatter plots enable you to instantly see connections and trends between two variables, making data-driven decisions faster and clearer.

Real Life Example

A marketing manager uses a scatter plot to compare advertising spend and sales revenue across regions, quickly identifying which areas get the best return on investment.

Key Takeaways

Manual tables hide relationships between data points.

Scatter plots visualize two variables together clearly.

Power BI scatter plots update interactively for deeper insight.

Practice

1. What is the main purpose of a scatter plot in Power BI?

easy

A. To show the relationship between two numeric variables

B. To display data in a pie chart format

C. To list data in rows and columns

D. To create a map visualization

Solution

1. Step 1: Understand scatter plot basics

   A scatter plot uses two numeric axes to show how two variables relate to each other.

2. Step 2: Compare with other chart types

   Pie charts show parts of a whole, tables list data, and maps show locations, not relationships between numbers.

3. Final Answer:

   To show the relationship between two numeric variables -> Option A

4. Quick Check:

   Scatter plot = relationship between numbers [OK]

Hint: Scatter plots always compare two numeric values [OK]

Common Mistakes:

• Confusing scatter plots with pie charts
• Thinking scatter plots show categories only
• Using scatter plots for non-numeric data

2. Which of the following is the correct way to assign data fields in a Power BI scatter plot?

easy

A. Assign date data to Size and text data to X Axis

B. Assign text data to X Axis and Y Axis, numeric data to Legend

C. Assign numeric data to Legend only

D. Assign numeric data to X Axis and Y Axis, optional Legend for categories

Solution

1. Step 1: Identify correct field types for axes

   Scatter plots require numeric data on X and Y axes to plot points in 2D space.

2. Step 2: Understand optional fields

   Legend can be used for categories (text), and Size can represent numeric values for point size.

3. Final Answer:

   Assign numeric data to X Axis and Y Axis, optional Legend for categories -> Option D

4. Quick Check:

   Numeric on axes + Legend for categories [OK]

Hint: X and Y axes must be numeric for scatter plots [OK]

Common Mistakes:

• Putting text data on X or Y axes
• Using Legend for numeric axes
• Assigning date data incorrectly

3. Given a scatter plot with Sales on X Axis and Profit on Y Axis, what pattern would you expect if higher sales generally lead to higher profit?

medium

A. Points are randomly scattered with no pattern

B. Points form a horizontal line

C. Points trend upward from left to right

D. Points trend downward from left to right

Solution

1. Step 1: Understand relationship between Sales and Profit

   If higher sales lead to higher profit, as sales increase (X axis), profit (Y axis) should also increase.

2. Step 2: Visualize the scatter plot pattern

   This creates an upward trend from left (low sales) to right (high sales).

3. Final Answer:

   Points trend upward from left to right -> Option C

4. Quick Check:

   Positive correlation = upward trend [OK]

Hint: Positive correlation means points go up left to right [OK]

Common Mistakes:

• Confusing upward and downward trends
• Expecting horizontal line for correlation
• Ignoring relationship direction

4. You created a scatter plot but all points appear in a single vertical line. What is the most likely cause?

medium

A. X Axis has the same value for all data points

B. Y Axis has the same value for all data points

C. Legend field is missing

D. Size field is assigned text data

Solution

1. Step 1: Analyze vertical line pattern

   A vertical line means all points share the same X coordinate but vary in Y.

2. Step 2: Identify cause in data assignment

   If X Axis data is constant for all points, they align vertically.

3. Final Answer:

   X Axis has the same value for all data points -> Option A

4. Quick Check:

   Same X value = vertical line [OK]

Hint: Same X values cause vertical line in scatter plot [OK]

Common Mistakes:

• Thinking missing Legend causes vertical line
• Confusing vertical with horizontal line causes
• Assigning text to Size field causing this

5. You want to highlight different product categories in a scatter plot showing Sales (X Axis) vs Profit (Y Axis). Which field should you add to best achieve this?

hard

A. Add Product Category to X Axis

B. Add Product Category to Legend

C. Add Product Category to Size

D. Add Product Category to Y Axis

Solution

1. Step 1: Understand how to differentiate categories visually

   Legend assigns colors or markers to categories, making groups visible in scatter plots.

2. Step 2: Evaluate other field assignments

   Adding categories to Size or axes is incorrect because axes require numeric data, and Size is numeric for point size.

3. Final Answer:

   Add Product Category to Legend -> Option B

4. Quick Check:

   Legend = category colors in scatter plot [OK]

Hint: Use Legend for categories to color points [OK]

Common Mistakes:

• Putting categories on numeric axes
• Using Size for categories instead of numeric values
• Not using Legend to differentiate groups