Scatter plots in Power BI - Deep Dive

Overview - Scatter plots

What is it?

A scatter plot is a type of chart that shows the relationship between two different sets of numbers. Each point on the chart represents one item with two values: one on the horizontal axis and one on the vertical axis. This helps you see patterns, trends, or clusters in your data. In Power BI, scatter plots help visualize how two measures relate to each other across categories.

Why it matters

Scatter plots exist to help people quickly understand how two things might be connected or affect each other. Without scatter plots, it would be hard to spot trends or unusual points in large data sets just by looking at numbers. They make complex data easy to grasp visually, which helps businesses make better decisions based on patterns and outliers.

Where it fits

Before learning scatter plots, you should understand basic charts like bar and line charts and know how to load data into Power BI. After mastering scatter plots, you can explore more advanced visualizations like bubble charts, correlation analysis, and custom visuals that build on these concepts.

Mental Model

Core Idea

A scatter plot places each data point on a grid using two values, letting you see how those values relate across many items at once.

Think of it like...

Imagine throwing a handful of small balls onto a flat table where the table’s length and width represent two different measurements. Where each ball lands shows the combination of those two measurements for that ball.

Scatter Plot Layout:

  Y-axis (Measure 2)
  ↑
  │       •     •
  │   •       •   •
  │       •
  │  •
  │
  └────────────────→ X-axis (Measure 1)

Each • is a data point showing two values.

Build-Up - 6 Steps

1

Foundation

Understanding Scatter Plot Basics

Concept: Learn what a scatter plot is and how it shows two measures as points on a grid.

A scatter plot uses two axes: horizontal (X) and vertical (Y). Each point on the plot represents one record with two values, one for each axis. For example, sales amount on X and profit margin on Y. This helps you see if higher sales relate to higher profits.

Result

You can visually identify if there is a pattern, like points clustering or spreading out.

Understanding that each point is a pair of values helps you see how two variables interact visually.

2

Foundation

Creating a Scatter Plot in Power BI

Concept: Learn how to build a scatter plot using Power BI’s visual tools.

In Power BI Desktop, select the scatter chart visual. Drag one measure to the X-axis field and another to the Y-axis field. Optionally, add a category to the Details field to separate points by groups. Adjust size or color to add more data dimensions.

Result

A scatter plot appears on your report showing points for each data record based on the measures chosen.

Knowing how to assign data fields to axes is key to making meaningful scatter plots.

3

Intermediate

Using Size and Color for Extra Dimensions

🤔Before reading on: do you think adding size and color to points can show more than two variables? Commit to yes or no.

Concept: Scatter plots can show more than two variables by changing point size and color.

In Power BI, you can add a measure to the Size field to make points bigger or smaller based on a value, like sales volume. You can also add a field to the Color saturation to color points differently, like by region. This adds depth to your analysis.

Result

The scatter plot now shows points with varying sizes and colors, revealing more data insights at once.

Understanding that size and color add dimensions lets you analyze complex data relationships visually.

4

Intermediate

Interpreting Patterns and Outliers

🤔Before reading on: do you think points far from others always mean errors? Commit to yes or no.

Concept: Learn how to read clusters, trends, and outliers in scatter plots.

Clusters of points suggest groups with similar values. A clear upward or downward slope shows correlation between variables. Outliers are points far from others and may indicate special cases or errors. Recognizing these helps in data-driven decisions.

Result

You can spot meaningful patterns and unusual data points quickly.

Knowing how to interpret visual patterns turns scatter plots from pictures into powerful insights.

5

Advanced

Adding Play Axis for Time Analysis

🤔Before reading on: do you think scatter plots can show changes over time without losing clarity? Commit to yes or no.

Concept: Use the Play Axis feature in Power BI to animate scatter plots over time.

Add a date or time field to the Play Axis in Power BI. This creates an animation showing how points move as time changes. It helps track trends and shifts dynamically, like sales growth by region over months.

Result

The scatter plot animates, showing data evolution over time clearly.

Understanding animation in scatter plots reveals trends that static charts might hide.

6

Expert

Handling Large Data and Performance

🤔Before reading on: do you think scatter plots always perform well with millions of points? Commit to yes or no.

Concept: Learn techniques to optimize scatter plots with large datasets in Power BI.

Large datasets can slow down scatter plots and clutter visuals. Use data aggregation, filtering, or sampling to reduce points. Power BI also supports zoom and pan to focus on areas. Custom visuals or R scripts can handle big data better.

Result

Scatter plots remain responsive and readable even with large data.

Knowing how to manage data size prevents performance issues and keeps visuals useful.

Under the Hood

Scatter plots map each data record’s two numeric values to coordinates on a two-dimensional plane. Power BI uses these coordinates to place dots on the canvas. Additional fields like size and color modify the visual properties of each dot. The rendering engine optimizes drawing and interaction, including zoom and tooltips.

Why designed this way?

Scatter plots were designed to visually reveal relationships between two variables because numbers alone are hard to interpret. Power BI’s implementation adds interactivity and extra dimensions (size, color) to enrich analysis. This design balances simplicity and depth, making complex data accessible.

Power BI Scatter Plot Internals:

[Data Source]
     ↓
[Data Model] → [Measures for X, Y, Size, Color]
     ↓
[Scatter Plot Visual Engine]
     ↓
[Render Points on Canvas]
     ↓
[User Interaction: Zoom, Tooltip, Play Axis]

Myth Busters - 4 Common Misconceptions

Quick: do you think a scatter plot always shows cause and effect between variables? Commit to yes or no.

Common Belief: Scatter plots prove one variable causes changes in the other.

Reality: Scatter plots only show correlation or patterns, not causation. Other analysis is needed to prove cause and effect.

Why it matters: Mistaking correlation for causation can lead to wrong business decisions and wasted resources.

Quick: do you think adding more variables as size and color always makes scatter plots clearer? Commit to yes or no.

Common Belief: More variables in size and color always improve understanding.

Reality: Too many variables can clutter the plot and confuse viewers, hiding insights instead of revealing them.

Why it matters: Overloading visuals reduces clarity and can mislead decision makers.

Quick: do you think outliers in scatter plots are always data errors? Commit to yes or no.

Common Belief: Outliers are always mistakes or bad data points.

Reality: Outliers can be valid and important signals, like new trends or special cases.

Why it matters: Ignoring outliers risks missing critical insights or opportunities.

Quick: do you think scatter plots perform well with millions of points without any preparation? Commit to yes or no.

Common Belief: Scatter plots handle very large datasets smoothly by default.

Reality: Large datasets can slow down scatter plots and make them unreadable without aggregation or filtering.

Why it matters: Not managing data size leads to slow reports and poor user experience.

Expert Zone

1

Power BI’s scatter plot uses a layered rendering approach to optimize performance and interactivity.

2

The Play Axis feature internally creates snapshots of data states over time to animate smoothly.

3

Size scaling in scatter plots is often non-linear to better represent differences without overwhelming small values.

When NOT to use

Avoid scatter plots when you have only one variable or categorical data without numeric measures. Use bar charts or pie charts instead. For very large datasets, consider heatmaps or aggregated visuals to prevent clutter and performance issues.

Production Patterns

Professionals use scatter plots to detect correlations, segment customers, and monitor KPIs over time. They combine scatter plots with slicers and filters to explore data interactively. Animated scatter plots are popular for presentations to show trends dynamically.

Connections

Correlation Coefficient

Scatter plots visually show relationships that correlation coefficients measure numerically.

Understanding scatter plots helps grasp what correlation numbers mean and when they might be misleading.

Data Clustering

Scatter plots reveal natural groupings in data that clustering algorithms formalize.

Seeing clusters visually prepares you to apply and interpret clustering techniques in machine learning.

Astronomy Star Maps

Both plot points in two dimensions to reveal patterns and relationships among many objects.

Recognizing this connection shows how visualization principles apply across science and business.

Common Pitfalls

#1 Plotting categorical data on scatter plot axes.

Wrong approach:

X-axis: Product Category (Text)
Y-axis: Sales Amount

Correct approach:

X-axis: Sales Amount
Y-axis: Profit Margin
Use categorical data only in Details or Legend fields.

Root cause:

Misunderstanding that scatter plots require numeric axes for meaningful positioning.

#2 Using raw data with too many points causing clutter.

Wrong approach:

Plotting millions of individual sales transactions directly without aggregation.

Correct approach:

Aggregate sales by region or time period before plotting to reduce points.

Root cause:

Not considering visual clarity and performance limits of scatter plots.

#3 Assuming all outliers are errors and removing them blindly.

Wrong approach:

Filtering out all points far from the main cluster without investigation.

Correct approach:

Investigate outliers to understand if they represent important insights before removing.

Root cause:

Lack of understanding of the significance of outliers in data analysis.

Key Takeaways

Scatter plots show relationships between two numeric variables by plotting points on X and Y axes.

Adding size and color to points lets you visualize more data dimensions in one chart.

Interpreting clusters, trends, and outliers in scatter plots reveals valuable business insights.

Power BI’s Play Axis feature animates scatter plots over time to show data changes dynamically.

Managing data size and clarity is crucial for effective and performant scatter plots.