Data visualization design is both an art and a science, which makes it difficult for beginners to grasp. It is, nevertheless, a necessary ability if you wish to master data storytelling and make a powerful effect through content.
Even if you’re not falsifying data, if you’re not presenting it in the best possible way, you’re doing your reader a disservice. Fortunately, there are a number of basic things you can take to guarantee that your data stories have the desired impact.
So, if you’re ready to step up your data visualization design game, we’ve put together a list of our top ideas to help you address common data design mistakes and improve your existing data visualizations, one chart at a time. This list is also organized by category in case you need a quick reference. We hope they’ll be of assistance in your quest to data science jobs.
Data visualization is not a new concept. It has also been around for a long time. Maps are the earliest and most obvious example. Then, in the nineteenth century, the pie chart was invented. After a few decades, Charles Joseph and Minard employed graphs to map Napoleon Bonaparte’s Russian war of 1812, using several metrics such as many armies, temperature, and distance. Other notable figures in the history of data visualization include Oresme. One of the finest minds of the Middle Ages, credited with the invention of the bar chart in the 14th century and the first use of the area chart by Playfair. Let’s start with the Best Practices for Data Visualization.
To begin, there are a few general considerations to keep in mind. Keep in mind that every data visualization design decision you make should benefit your reader, not you. (Sorry, but this isn’t the place to flaunt your amazing line-art talents.) To make the most of your data, follow these guidelines.
There may be more than one way to appropriately visualize the data. Consider what you’re attempting to achieve, the message you’re sending, who you’re trying to reach, and so on in this scenario.
No, this does not indicate that half of your data points are discarded. But be wary of chart clutter, additional copy, needless images, drop shadows, and decoration, among other things. The beauty of data visualization is that it allows the designer to perform hard work in terms of enhancing and communicating the story. Allow it to do its work. (However, avoid using 3D charts.) They can alter the perception of the visualization, as previously indicated.)
Take a step back when you’ve finished your visualization and analyze what basic features could be added, adjusted, or eliminated to make the data easier to understand for the reader. You could want to add a trend line to a line chart, or you might notice your pie chart has too many slices (use 6 max). These small changes add up to a big difference.
Although a line chart does not have to begin at zero, it should be included if it provides more context for comparison. If very few variations in data are significant (for example, in stock market data), you can truncate the scale to highlight these differences.
5. Always opt for the most Effective Visualisation Method
Visual consistency is important so that the reader can compare and contrast at a glance. This could imply using stacked bar charts, grouped bar charts, or line graphs. Whatever you choose, don’t make the reader work too hard by asking them to compare too many things.
You may have two great stacked bar charts that are supposed to allow your reader to compare points, but if they’re too far away, you’ve already lost.
7. Don’t Over Explain
If a fact is already mentioned in the prose, it doesn’t need to be repeated in the subhead, callout, or chart header.
It’s not necessary to be witty, eloquent, or punny. Any descriptive language above the chart should be succinct and relevant to the graphic below. Remember to concentrate on the quickest route to understanding.
Callouts aren’t there just to take up space. They should be used on purpose to draw attention to important information or to provide more context.
10. Distracting Fonts and Elements should be avoided
You may need to accentuate a point at times. If that’s the case, simply use bold or italic text to accentuate a point—and don’t use both at once.
Data visualisation aids in the management of vast amounts of data, which is shown in the form of useful charts and graphs. To accomplish so, we’ll need the best visual tools on the market, including as Power BI, Tableau, and others. Always try to choose the instrument that is most appropriate for your needs. The product should allow for maximum interactivity, be visually appealing, and allow users to combine data sources and share dashboards with other viewers. Always strive to develop a sleek and tidy dashboard that assists visitors in better comprehending facts and making decisions. Last but not least, data visualization can help you save a lot of time in your organization. For more information, you can read the Data Science Questions and Answers Guide.