![]() Remember: Focus on the quickest path to comprehension.ġ0) Use callouts wisely. Keep any descriptive text above the chart brief and directly related to the chart underneath. There’s no need to get clever, verbose, or pun-tastic. If the copy already mentions a fact, the subhead, callout, and chart header don’t have to reiterate it.ĩ) Keep chart and graph headers simple and to the point. That said, in many data visualizations, infographics, and e-books, we see data visualization and copy working against each other instead of together.Ĩ) Don’t over explain. Copyĭata is about numbers, certainly, but it is generally used in conjunction with copy to help provide context for the point at hand. Exciting! But what’s more exciting? Showing that you’ve actually had a 100% sales increase since Q1. Maybe you had a 30% sales increase in Q4. You may have two nice stacked bar charts that are meant to let your reader compare points, but if they’re placed too far apart to “get” the comparison, you’ve already lost.ħ) Tell the whole story. Whatever you choose, don’t overwhelm by making the reader work to compare too many things.Ħ) Watch your placement. This might mean you use stacked bar charts, a grouped bar chart, or a line chart. You want visual consistency so that the reader can compare at a glance. If relatively small fluctuations in data are meaningful (e.g., in stock market data), you may truncate the scale to showcase these variances.ĥ) Always choose the most efficient visualization. Although a line chart does not have to start at a zero baseline, it should be included if it gives more context for comparison. (Ever tried to compare 32 different pie charts? Yeah, didn’t think so.)Ĥ) Include a zero baseline if possible. But just putting two charts side by side doesn’t necessarily accomplish that. Comparisonĭata visualization makes comparison a lot easier, letting you actually “see” how two different data sets stack up to each other. These subtle tweaks make a huge difference. You might add a trend line to a line chart, or you might realize you have too many slices in your pie chart (use 6 max). Once you have your visualization created, take a step back and consider what simple elements might be added, tweaked, or removed to make the data easier for the reader to understand. As previously mentioned, they can skew perception of the visualization.)ģ) Design for comprehension. The great thing about data visualization is that design can help do the heavy lifting to enhance and communicate the story. But be mindful of things like chart junk, extra copy, unnecessary illustrations, drop shadows, ornamentations, etc. No, that doesn’t mean you kill half your data points. In this case, consider what you’re trying to achieve, the message you’re communicating, who you’re trying to reach, etc.Ģ) Remove anything that doesn’t support the story. There may be more than one way to visualize the data accurately. (Sorry, but it’s not about showing off your sweet line-art skills.) Follow these tips to do your data justice.ġ) Choose the chart that tells the story. Remember that every data visualization design choice you make should enhance your reader’s experience-not yours. To start, let’s cover a few general things to keep in mind. We even arranged this list by category in case you need a quick reference. So, if you’re ready kick your data visualization design game up a notch, we’ve compiled our team’s best tips to help you fix common data design mistakes and enhance your existing data visualizations, one chart at a time. Luckily, there are many simple things you can do to ensure your data stories make the impact they should. In these cases, your credibility may be on the line, and nobody wants that.Įven if you’re not misrepresenting data, if you aren’t presenting it in its most optimized form, you’re doing a disservice to your reader. Subpar data design comes in many forms-a confusing visualization, mislabeled data, 3D charts that skew perception, etc. Not only are you not doing it well you might actually be hurting your brand. Unfortunately, a lot of people think that slapping a few charts together means you’re doing data visualization design well. To see the power of data visualization at work, watch this quick video. ![]() By “seeing” the data, it is easier for your brain to intake, synthesize, and retain the information presented. Your brain is prewired to process visual content much quicker than text, which is why data design is so effective. But if you want to master data storytelling-and make a strong impact through content-it’s a crucial skill. ![]() Data visualization design is both an art and a science, which is why it can be challenging for noobs to master. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |