Scatter Plots: Uncovering Hidden Patterns in Your L&D Data

What is a scatter plot in L&D data visualization?

A scatter plot, also known as a scatter diagram or scatterplot, is a type of data visualization that displays values for two variables as points on a two-dimensional graph. In the context of learning and development, scatter plots can reveal relationships between various aspects of your training programs and their outcomes.

Scatter plots are useful when you want to:

• Identify correlations between two variables.
• Pinpoint trends or patterns in your data.
• Detect outliers that may require further investigation.
• Visualize the distribution of data points across two dimensions.

The power of scatter plots in L&D

Scatter plots uncover insights in your L&D data. They help you visualize and analyze relationships between two variables, such as the time employees spend on training and their performance improvements or how engagement levels correlate with learning outcomes.

By plotting individual data points on a two-dimensional graph, scatter plots can help you identify trends, correlations, and outliers that could be crucial to optimizing your learning programs.

Scattered data, focused insights

Consider using a scatter plot when you’re curious about how one aspect of your training program might influence another or when you want to see if there’s a relationship between the metrics you’re tracking.

For example, you might use a scatter diagram to visualize the relationship between:

• Hours spent in training and performance improvement
• Course completion rates and learner satisfaction scores
• Pre-training assessment scores and post-training performance

When to look beyond scatter plots

While scatter plots are great tools, they’re not always the best choice. Consider these alternatives for specific scenarios:

• Line graphs: Better for showing trends over time
• Bar charts: Ideal for comparing categorical data
• Heat maps: Useful for displaying relationships between multiple variables simultaneously

Anatomy of a scatter diagram

So, what are the key features of a scatter plot? Picture a graph with two axes: the horizontal X-axis and the vertical Y-axis. Each axis represents a different variable you want to explore. For instance, you might have “Training Hours” on the X-axis and “Performance Score” on the Y-axis.

Your data points represent individual entries in your dataset, showing where each item falls in relation to both variables. The pattern these points form can tell you a story about the relationship between your variables.

We often add trend lines to help navigate data. These lines help us see the overall pattern or relationship between our variables. They can be straight lines showing a linear relationship or curves indicating more complex interactions.

Using quadrants to enhance scatter plot analysis

Gain even more insights from your scatter plots by dividing them into quadrants. This technique can help you categorize data points and identify patterns that might not be immediately apparent.

Here’s how to interpret quadrants in a scatter plot:

Quadrant A (Upper Right): High Revenue, High Cost

• This quadrant represents activities with both high production costs and high revenues.
• These could be considered high-stakes projects that require significant investment but also yield substantial returns.

Quadrant B (Upper Left): Low Revenue, High Cost

• This area shows activities with high production costs but lower revenues.
• These might be projects that are currently underperforming or require optimization to improve their ROI.

Quadrant C (Lower Right): High Revenue, Low Cost

• This quadrant is sparsely populated, indicating fewer activities that achieve high revenue with low production costs.
• The activities in this area could be considered the most efficient or profitable.

Quadrant D (Lower Left): Low Revenue, Low Cost

• This quadrant shows a cluster of activities with both lower production costs and lower revenues.
• These might be smaller scale projects or those in early stages of development.

By analyzing the distribution of data points across these quadrants, you can gain insights into the effectiveness of your training programs and identify areas for improvement.

For instance, if most of your data points fall in Quadrants A and D, it suggests a strong correlation between program engagement and performance. However, if points are evenly distributed across all quadrants, you might need to reassess the relevance of your training content to performance outcomes.

Exploring trends, patterns, and outliers with scatter plots

When analyzing your scatter plot data, start by identifying the overall pattern distribution. Does the data show a positive correlation (upward trend), negative correlation (downward trend), or no correlation (random distribution)?

Key patterns to look for:

• Strong positive correlation: As one variable increases, the other tends to increase
• Strong negative correlation: As one variable increases, the other tends to decrease
• Weak or no correlation: No clear pattern between the variables
• Clusters: Groups of data points that might indicate distinct learner segments
• Outliers: Data points that fall far from the central cluster, potentially indicating exceptional cases or data errors

Common mistakes to avoid with scatter plot analysis

• Assuming correlation implies causation: Just because two variables are related doesn’t mean one causes the other.
• Overlooking outliers: Unusual data points can provide valuable insights or indicate data collection issues.
• Ignoring the scale: Changing the scale of your axes can dramatically alter the perceived relationship between variables.
• Overinterpreting weak correlations: Not every pattern is meaningful; consider statistical significance.
• Neglecting context: Always interpret your scatter plots within the broader context of your L&D programs and organizational goals.

Interpreting scatter plot data in L&D contexts

Let’s explore some L&D-specific examples. Take a look at the following scatter plot that shows the relationship between sales product knowledge program engagement and actual sales performance.

By analyzing the distribution of data points across the quadrants, you can:

• Determine the effectiveness of your training programs.
• Identify areas where additional support is needed.
• Tailor interventions to boost program engagement and sales outcomes.

Or perhaps you’re curious about the correlation between learner engagement and course completion rates—a scatter plot can shed light on this connection.

Best practices for crafting effective scatter plots

Creating a scatter plot is like crafting a visual story for your audience. Ensure your story is clear and impactful by keeping these best practices in mind:

• Start by choosing appropriate scales for your axes—generally, they should start at zero unless there’s a compelling reason to do otherwise. This provides context and prevents misinterpretation of the data.
• Use colors and shapes to distinguish between groups or highlight essential data points.
• Include clear labels and titles to guide your audience through your data narrative.
• And don’t forget about trend lines—these add valuable context, but use them thoughtfully to avoid overinterpreting the data.

Scatter plots in action: How Caterpillar optimizes training videos

Caterpillar created an anomaly detection dashboard in Watershed to analyze the effectiveness of their video content. This dashboard features scatter reports designed to highlight popular videos based on viewer engagement and watch frequency.

The scatter plots visualize the relationship between unique users watching a video and the number of times a quarter of a video is watched. This approach enabled Caterpillar to identify outlier videos that are watched multiple times per person, indicating high engagement and value.

By analyzing these reports monthly, they can track changes in video popularity over time, similar to tracking chart positions of pop songs.

As a result, Caterpillar can focus on their most successful content for deeper analysis and insights—such as, such as optimal video length, content types that maintain viewer interest, targeted audience engagement, and the impact of social interactions on video popularity.

This approach allows Caterpillar to continually refine their learning content strategy, ensuring their training materials are both engaging and effective in supporting their global workforce.

Up Next: Measuring L&D success with heatmaps

By mastering scatter plots, you can dive deeper into your L&D data. You’ll uncover insights that can drive real improvements in your learning programs and, ultimately, your organization’s performance.

Stay tuned for our next post in this series, where we’ll explore how heatmaps can help you visualize complex L&D datasets and identify areas of opportunity at a glance. The world of data visualization is vast and exciting, and we’re just getting started!