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Why data visualization matters and how to do it well

Updated: Jan 13, 2021

3 Best practices for easy-to-read data.

A quick Google search will tell you that there are 2.5 quintillion bytes of data created every single day. And, we’ll tell you right now that when you start using words like quintillion, that’s just silly. The human brain is wondrous and endlessly fascinating, but it just isn’t equipped to process a quintillion of anything. Luckily, data visualization offers a quick, convenient way for our brains to absorb a lot of information at once and not only understand it – but also take action. But, what is data visualization exactly and how do you make sure you’re doing it right? Read on.

Insert obligatory idiom: a picture is worth a thousand words.

Duh. Right? But, it really is hard to have a conversation about data visualization – charts, graphs, any visual representation of data – without using that old adage. Remember those human brains we were just talking about? Some other stats that are readily available on the old Google include:

  • Human brains process images 60,000 times faster than words

  • Ninety percent of the info the brain transmits is visual

  • Our recall is around four times as good when we have visuals versus text

This is why data visualization is a critical tool for helping our brains understand the data we collect. A graphic summary of complicated stuff plays to our brains’ strengths and aids in business intelligence. Operations managers, purchasing directors, CEOs – people in high-impact decision-making role use data visualizations to:

  • Identify trends and patterns

  • Map frequencies for key events

  • Determine correlations between data sets

  • Analyze risk

  • Examine demographics

  • And more

Kind of hard to do all of that quickly when you’re working from tens of thousands of lines of data in an Excel sheet (cue the tension headache, right?). Of course, as with any “picture,” there can be a big range in terms of how well the artist nailed it. When the artist is a software engineer or UX designer and the picture is data, here are a few things to keep in mind:

1. Visualization type

When you’re developing (or selecting) your data visualization tool, think about the kind of data you collect and the reasons people need to understand it. Certain charts and graphs are better suited for certain purposes. For example, line and bar graphs are great for showing changes over time. Histograms are better for frequency, scatter plots and standard deviations help you find correlations between data points, and heat maps give you a quick hit on risk assessments.

2. Color choice

Color theory is an extensive yet subjective aspect of visual design. Your color palette does a lot more than just make your charts and graphs more interesting. When considering colors, you need to think about things like:

  • Preexisting color associations – We have certain color guidelines we understand culturally, such as green means go, red means stop, etc.

  • Color psychology – Our brains have emotional reactions to colors; e.g. blue can signify trust, orange can inspire creativity, red can show anger, etc.

  • Brand colors – In some cases, keeping your colors in line with your brand’s color palette is a priority.

All of these aspects and more (such as saturation and luminance) can affect the way viewers experience color choice.

3. Usability

Once you have the right visualization types and the most effective color choices, you’ll want to spend a little time thinking through how users will actually interact with your data. You want function over form in this case – the whole point of creating data visualizations is to simplify the complex, right? So if it’s not easy, it’s not working. A few basic ways to improve usability include:

  • Consistent, recognizable metrics – Use metrics the viewer cares about and display results in a standardized format.

  • Show scale, avoid distortion – You want your charts and graphs to accurately reflect variances. For example, in a pie chart, your segments should visually match percentages.

  • Clear labels and abbreviations – Minimize the amount of text needed, but not at the cost of causing ambiguity.

  • Interactivity when possible – Are your charts clickable? Can viewers dig deeper into data by clicking on aspects of the visualizations themselves?

Establishing the right style and format for effective data visualizations is a process for sure, but it’s one that pays off big-time when done right. Anyone who has ever shaken a fist and uttered a “Man alive!” at an Excel sheet knows this to be true. If you need help thinking through the best way to display your data, we’re happy to lend a hand.


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