Subject matter shapes can tell a compelling story. Incorporate contextual clues with shapes and designsĬontext helps us decipher information at first glance instead of sitting and scrutinizing it. It’s helpful to show consistency across values or to highlight contrasts in the data.Ĥ. And how you use colors also has serious effect. If you’re working with temperatures, use red to indicate heat and blue for cold. Use intuitive colors that make sense to the viewer so they process the information faster. Too many colors will create a cacophony, while using a single color or too many shades of one color can cause the data to blend. Use color to highlight and accentuate the information. However, there’s a delicate balance to using color keeping it simple is best. It can convey so much without using words. Tell data stories quickly with clear color cuesĬolor is important. You don’t want your viewer’s to get lost as they follow one point to another quickly and easily. If you’re using multiple graphs, make sure the order is consistent and connections between the data are clear. A simple example: if you are communicating in a language that reads left to right, then orient your visualization to adhere to that convention. To capitalize on these human tendencies, make sure the order or format in which you present data makes sense to viewers whether it’s numeric, alphabetical, or sequential. We naturally seek patterns, and if patterns are random or don’t make sense, it’s very difficult to understand what the visualization conveys. Our eyes are drawn to indicators that tell us important information at a glance. To learn about more visualization formats and see helpful examples, read and bookmark this whitepaper. In fact, every year on Pi Day (March 14), data scientist Kaiser Fung and his colleague find a pie chart and remake it into a more suitable version, as their way of protesting against flagrant overuse. Pie charts are powerful for adding detail to other visualizations, but aren’t as effective on their own.Maps are a no brainer for visualizing location-specific questions or aiding geographical exploration.Histograms and box plots show where your data is clustered and can compare categories.

The result is a simple, straightforward way to visualize changes in one value relative to another.

The line graph connects several distinct data points, presenting them as one continuous evolution.

Bullet charts show progress against a goal by comparing measures and were designed to replace dashboard gauges, meters, and thermometers.

They’re especially effective when you have data that can be split into multiple categories.

Bar charts are effective at comparing categories within a single measure and one of the most common data visualizations.

Sometimes even combining related charts is helpful it can fuel deeper exploration that leads to helpful business insights and answers that driven action. One size does not fit all so carefully consider and choose the right format for your visualization that will best tell the story and answer key questions generated by data-all of it connecting with your main purpose. Choose the right charts and graphs for the job For more tips, read 10 Best Practices for Effective Dashboards. When you’re ready to dive in and create, these tips and techniques will make your visualization intuitive and interesting so it’s digested quicker, easier, and the intended messages come through clearly, leaving viewers with specific, helpful takeaways.

What other questions will my visualization inspire or what conversations may result?.

Before jumping to specifics, think about what you want to accomplish with the visualization, which helps you decide what data to include. They can also shine a light on hidden information and details that you wouldn’t uncover in a spreadsheet, bar chart, or pie graph. If they’re done well, visualizations tell an interesting story. How do I make my data visualization more interesting, dynamic, relevant, and well-received by diverse audiences? For anyone developing a visualization-whether you’re a newcomer or seasoned data analyst-these questions are top-of-mind because you want to make it great.

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Basic data visualization techniques