7 Essential Components of Effective Data Visualization

In today's data-driven world, the ability to create clear, compelling data visualizations is a crucial skill for all of us, and thus why it is SO important we help our students practice and master this skill. But how can we go about it…without feeling overwhelmed by all of the pieces? Here we explore seven essential components that make data visualizations effective, helping you and your students communicate complex information with impact and clarity.

1. Get Clear on Your Purpose

Every effective data visualization, whether our students are making it or a professional data visualizer is making it, should start with a clear purpose and a deep understanding of what you are after. Ask yourself:

What is the main question you want to ask (when graphing to explore)? OR
What is the main message you want to convey (when graphing to explain)?

Any data visualization is a means to an end (the purpose)...it is not the end in and of itself. Therefore, helping our students identify why they’re making the graph (chart, plot, map, etc.) at the start is CRITICAL to anchor their entire process and set them up for success.

2. Actively Select Your Graph Type

Choosing the right type of data visualization is crucial for putting together an effective data representation whether you are exploring or explaining (confirming) with data. Key things to consider include:

What is the comparison, relationship, pattern, etc. you want to investigate or highlight?
What is the nature of your data (categorical, numerical, etc.)?

Helping students consider graph choice early and often is important for their success in building effective data visualizations intentionally. For a more detailed description on how to help students select the right graph type, check out our blog on The Ultimate Guide to Choosing the Right Graph for Your Data.

3. Check the Accuracy of the Representation

Accuracy is paramount in data visualization. And part of accuracy is checking and rechecking your work…and the more we can normalize that for the students the better. But that can feel overwhelming for many students, so a few specific things to encourage students to check include:

Data points are plotted correctly,

Axis scales are appropriate for the variables they are plotting and are not misleading,

Proportions in charts like pie charts are mathematically correct (if applicable).

A critical eye is essential whenever we are working with data, thus creating opportunities for students to double-check that their graph matches their data.

There are many ways in which our data visualizations can be inaccurate. If you are looking for some examples of how to talk about it for your students, check out 5 Tips to be aware of “How Charts Lie” and other ways to ensure data accuracy in visualizations.

4. Include Clear and Informative Labels

Labeling the various parts of our data visualizations is a big part of making them understandable. This includes things like:

Properly labeled axes with units

A legend if multiple data categories are represented through color, shapes, etc.

Clear titles

A note about titles, they should be different if we are exploring the data through the data visualization than if we are explaining a story from the data. But no matter what you are doing with the data, a title should never be a repeat of what the axis labels are. If you are interested in helping students think about their graph titles check out Let’s Talk Titles for more tips.

5. Effectively Use Color

Color is a very sticky part of visual information. We notice color more quickly than many other aspects of the visual space. Therefore, color can greatly enhance our understanding from a visualization or, if used poorly, can create a lot of confusion. Encourage your students to consider:

Keeping a consistent color scheme with a data visualization

Using colors that contrast with one another to highlight important parts of the data that you want to explore more or highlight to explain

A key part of making decisions about color is that the color is used strategically and intentionally for what you are doing with the data…not the default color scheme a graphing program (e.g., Google Sheets) puts forward or adding color just to add distracting flair to the visual.

Also, remember it is estimated over 300 million people are color-blind, so accessibility for your students and others who may be looking at the data visualizations is something to consider. For example, imagine if the number of adult breeding pairs here was plotted in green, then it would be virtually impossible for someone with red-green color-blindness to differentiate between chicks and adult breeding pairs.

As a note, we LOVE https://colorbrewer2.org/ as a great resource to find color-blind friendly color options.

6. Embrace Simplicity and Clarity

The best data visualizations are often the simplest. So the earlier we can teach students, and the more frequently we can remind them, to avoid “chart junk” (e.g., 3D effects, unnecessary icons) the better! To avoid cluttering a graph (chart, plot, map, etc.) with unnecessary elements, remind students that:

Every part of the visualization should serve a purpose to make sense of the data,

Keeping white space in a graph can actually enhance the readability of it,

3D effects, extra colors, rotating axes, etc. are all things that are fun to play with but actually make it much HARDER to make sense of the data.

7. Lean into Narrative and Insights

In order to support our students to move beyond "just making a graph" (because we have asked them to) into thinking about what story they can tell from the data is crucial to helping connect their purpose to their output. Each data visualization can make a story visible. But sometimes this can feel awkward for students, so help them think about:

What does the data mean to them, and what would they want someone else to learn/takeaway from the data?

Why is it important to learn/takeaway that from the data?

What conclusions can be drawn from the data? Or what actions can be taken now after learning this from the data?

Check out our short Data Bite on On carving out time for students to explore data and tell stories from what they find for more ideas on helping students connect data exploration with telling stories from data.

Conclusion

Hitting these seven essential components each time our students make a data visualization will significantly improve their effectiveness…and teach them critical aspects of what goes into making sense of data. Remember, practice and feedback are key to improvement. Encourage your students to critique and refine their visualizations regularly.

Ready to transform your students' data visualization skills? Download our Graph Type Matrix Resource and start upleveling their graphing today!