Useful Tips for Visuals in Data Storytelling

Here's a rundown of practical tips you can easily apply to make your data visual better and your story clearer.

It is a common misconception that impactful data stories should feature complex visualizations created through advanced tools. The truth is that the most effective visuals are straightforward, accessible, and simple to understand.

In this post, we’ve rounded up some practical tips for presenting data with simplicity and clarity, whatever your tool.

1. Steer clear of the “spaghetti” line chart.

Line charts are an excellent choice for visualizing trends over time. But sometimes, too much data can make the chart look like a messy plate of spaghetti, obscuring valuable trends.

For example, in the chart below, it is easy to ignore the overlapping lines at the bottom and simply focus on the top lines that are easier to read:

Alternatively, consider a small multiples chart to visualize the same data more clearly. Still using lines to visualize, the small multiples version below creates a separate view of each series so the reader can now appreciate how the headcount per department is trending. Now we can see the declining trend in the Finance department that was otherwise buried in the original chart.

2. Group or aggregate when it makes sense.

Another way to clean up a busy chart is to consider whether grouping the categories effectively delivers the insight your stakeholders need.

The busy line chart below makes it difficult to see the quarterly employee satisfaction data trends of the nine cities featured.

The updated chart below shows how the same data can be simplified by grouping the nine cities into East Coast and West Coast categories, highlighting the broader trend more clearly. Knowing the context of your data and what might be helpful for your audience can inform whether grouping or aggregating categories provides a more insightful message.

3. Spotlight the gap.

Data storytelling surfaces specific insights that guide decision-makers toward appropriate action. Sometimes, the insight isn’t in the trend created by the lines but in the distance between them.

For example, the chart below shows a call center’s call capacity and the calls received per day. We can see that calls received per day have exceeded capacity since Q1 of 2022.

…but can we stress this point further?

Through visual signals like selective application of color and shading, we can draw the reader’s attention to the gap, emphasizing how long this issue has been happening and how it is worsening. Selecting which visual elements to highlight reveals the insight at a glance.

4. Bars over donuts when comparing values.

While popular charts, like donut charts, might be visually enticing, they can over-complicate the message or water down the main takeaway.

For example, the donut chart below features ten different data points, some of which are close to each other. The circular orientation and arced shape of the slices make it challenging to compare values accurately.

In this example, a bar chart allows for a more efficient and accurate visual comparison of the data. Not only are lengths on a shared base easier to compare visually, but it is also more intuitive for the reader to compare values as separate data points instead of as a part of a whole.

4. Avoid data dumps.

It can be tempting to present all the data points we have on hand to proactively address all the possible questions our audience might ask. But often, this can backfire, leading to confusion and wasted time.

The chart below shows survey results with five categories of responses across a 20-week timeframe. The stacked bar chart captures all the data but makes it hard to follow and see the trends.

When we take the time to understand our audience and the context, we can make better decisions about what to include and leave out in our data story.

The chart below features the same dataset, but this time uses a line chart to demonstrate the combined values for “Satisfied” and “Very Satisfied.” It also maximizes the use of headlines and callouts to contextualize the message further so there is an explicit action the audience might consider.

To learn more about data storytelling and other learning opportunities related to data communication, check out our scheduled workshops or contact us to set up a special class.

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