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Effective data visualization makes data accessible, turning it from a collection of words and numbers into an easily-understood display. Transforming data into effective data visualization can be a challenging prospect, but with a clear strategy in mind, you can make the most of your data.
Image via Flickr by Sebastian Sikora
Data always tells a story. It’s your job to recognize what that story is, and tell it. Look for the relationships and patterns in your data and use them to drive your data visualization. When you tell a story, your data visualization becomes more than simply a blend of information and graphic elements. It’ll be a cohesive piece that is, like the best novels, easy to understand and engage with.
Good data storytelling starts with the big picture, then drills down on the details. It puts these details in context so that viewers can accurately interpret them and their significance. It also highlights the important factors driving the data so viewers can better understand the bigger picture.
There’s an old saying that you should never let the truth get in the way of a good story and an effective data analyst would surely disagree! While your data should tell a story, it should always be an accurate one. This might seem an obvious point, but data visualization expert Dona M. Wong insists “Many charts have sophisticated and intelligent underlying information, but their presentation fails to convey the intended message.”
This might be because omitting or tweaking some data told the creator’s story better or because the creator was inexperienced at making data visualizations. For example, an inexperienced person may start the vertical axis above zero and present just the tips of a bar chart, not realizing this artificially exaggerates differences in data and presents a false image.
French writer Antoine de Saint-Exupery once said, “Perfection is achieved not when there is nothing more to add, but when there is nothing left to take away.” He might not have been a data visualization expert, but his appreciation of minimalism suggests he would have a great aptitude for it.
Clutter distracts us. The brain can only perform a limited amount of tasks at any time. Looking at clutter, then focusing on it or even ignoring it, takes attention away from the things that matter. Therefore, a cluttered data visualization cannot communicate as effectively as one with only the necessary details.
Think critically about all of the elements you’re including in your data visualization and their roles. Anything that doesn’t have a purpose should be removed. Look out for common clutter culprits, like excessive color, unnecessary graphic elements like borders and grid lines, three-dimensional effects, and multiple decimal places. These details can be distracting and get in the way of effective data visualization.
While an effective data visualization is free of clutter, it won’t skimp on important details. Titles and labels make data visualization clear, minimizing the risks of confusion and misinterpretation. Remember to label charts and their axes. Include brief introductions and summaries where appropriate to tell your story. Never neglect units of measurement.
Data visualizations typically ask readers to compare and contrast data. Think carefully about how you’re presenting your data to achieve these goals.
Aligning numbers to the right allows for the easiest comparison. Figures aligned to the center or left are much harder to compare, as their units, tens, hundreds, and other columns aren’t in the same positions. When using decimal numbers, make sure you display a consistent number of decimal points to keep the values aligned. Thousands separators, like commas and spaces, can make large numbers much easier to read.
Bar charts and line graphs can be excellent visual tools because they’re easily understood. They allow viewers to compare and contrast data much more quickly than they could if the data was written in numerical form.
Pie charts are another common way to represent data visually, but they’re often less effective. Colored wedges look attractive, but our brains can’t accurately compare the size of angles. If the wedges are similar in size, viewers can incorrectly judge one is larger than the other. Even if the wedges are dramatically different, our eyes can’t gauge by how much.
Colors are powerful tools for effective data visualization, but they can confuse if you don’t use them in the right way. Colors have established connotations that are readily understood by all readers. For example, green symbolizes the environment, black connotes premium products and luxury, and red signifies danger. Using these colors prominently in data visualization concerning these topics helps convey the themes at a glance.
Colors can also confuse readers if they’re not used as expected. We’d expect to see positive numbers in green text and negative numbers in red. Using these colors in the reverse will confuse the message, as our associations with the hues are so deeply entrenched.
Your colors should contrast enough to easily differentiate between hues, but not so much that they jar the eye. If colors stand out too much, the data around them may seem more important than it is. Don’t use any more than six colors on a single data visualization. Too many colors can confuse the eye and make conveying the message more difficult.
Not all data is equal. The most effective data visualization creators recognize which data is the most important for telling their stories. They then use design elements to show the data hierarchy.
The most important data might be positioned near the top or center of a data visualization. Large dimensions and bright, bold colors can also give data greater importance. Conversely, less important data might be placed in a less prominent position. It might also take up less room on the data visualization and be rendered in paler, more subdued tones.
Don’t be daunted by data. Follow these steps and you can turn those dry statistics into effective data visualization that engages and informs your readers.