The right technique for data visualization: Part 2 — Standalone Data

By October 4, 2018 September 10th, 2019 Data Visualization, Design Tips, Visual Communication
Right Tool Part 2 Header

If you’ve read part 1 of our series on data-visualization techniques, you already know the best applications for bar graphs, line graphs, and pie charts. But maybe you’re not showing data over time, or by category. Instead, you have a few standalone data points that just aren’t graphable, or aren’t visually effective as graphs. Maybe they’re clustered in a few paragraphs in a report. Maybe you outlined them and then let them collect dust while you figured out how to display the information. These options just might be for you. Welcome to the final installment of our 2-part series.

Quantagrams

It’s a big word, but “quantagram” just means a repeated pictogram or icon used to show quantity. A common example is using multiple characters to show a number of people. You’ve probably seen this technique using the classic male and female icons from bathroom doors.

Quantagrams are great for small numbers (like “12 new restaurants opened on our street”). They also work well for small percentages or proportions where a pie chart could work. An example would be “3 in 4 restaurants [75%] on our street are 5+ years old.”

But quantagrams don’t work for larger numbers. Imagine your stat was “11,214 items sold in 2018.” You don’t have space for 11,214 icons on your design — and if you think you do, we implore you to think again! That’s a massive number to count out one by one. So, it’s tempting to add a key — “1 shopping bag = About 1,000 items” — and just show 11 shopping bags.

However, you’re probably trying to show that this is a big, impressive number. When you reduce it like this, this visualization now has the opposite effect. Eleven shopping bags don’t look or feel like 11,000+, even with a key. It’s really just a long way of discovering that the number “11,214” is powerful on its own.

The same thing happens with ratios. For example, imagine visualizing “8,370 of the 11,214 items sold in 2018 were mugs” using quantagrams. No, thanks!

So if you need a key to explain it, a quantagram isn’t the right choice.

If your stat fits the bill for a quantagram so far, think about what pictogram you would use. Because they’re so simple, pictograms can feel too reductive for serious topics. If that rings true with your data, you may need another way to visualize it.

If your stat is too large or not suited to pictograms, there’s an easy fix in the next section.

Right Tool Part 2 Quantagram-01-01

Typography

We bet you didn’t expect to see a section on typography from the leading visual communication agency. Well, here it is! The truth is, there are limited cases in which typography really is the best solution. To be clear, it should never be used just because you don’t want to create visuals. Don’t go back to the old text-only solutions of the past! Instead, use typography intelligently to achieve a successful and effective piece of content.

If your number is large (greater than 100), isn’t a percentage of a whole or a percentage increase/decrease, and is standalone — not being compared to another number — it’s probably a good candidate for typography. You might’ve thought the section on quantagrams was almost right for you, but realized your number was just too large. That’s an indicator that this the section you need.

Before settling on typography, run your data through each of the descriptions above and in Part 1 of this series. You should eliminate all visual possibilities before using type. That’s because visuals are simply more compelling, and oftentimes more effective. But visuals are only effective insofar as they’re accurate. If you face confusion or inaccuracy by visualizing your number, just go with text.

One way to enhance the typography technique is to combine it with a pictogram (like would be used in Quantagrams, but just a single one), an icon, or an illustration to accompany your text stat. This will help provide the viewer with visual context as to the subject matter of the stat, but let the number speak for itself.

Right Tool Part 2 Typography

Whether Part 1 or Part 2 of this series spoke loudest to your current data — or if they both make sense for your project in different ways — aesthetic is a consideration that spans all forms of data visualization. Beyond simply choosing the right data visualization technique to use, you must pick the right aesthetic to represent your information and reach your audience. A fun neon line graph with modern type might not work for a report geared at investors and C-suite execs, even if a line graph is the right choice for the data. A flat, grayscale pie chart could be the wrong choice for a summer camp pamphlet, even if the pie chart accurately shows attendee data.

So always ensure that form and function are equally considered — a beautiful chart that no one can read is just art.

Looking for more information on this subject? Get a deeper dive into the subjects above, and much more, by checking out Killer Infographics CEO Amy Balliett’s Lynda.com course, Data Visualization: Best Practices.

Lucy Todd

Author Lucy Todd

Lucy Todd is the Chief Process Officer at Killer Visual Strategies. She is a Seattle native and Western Washington University graduate. Her degree in Creative Writing and her customer service background both inform her work daily. A Killer employee since 2011 and executive since 2014, Lucy has researched for, written, and/or project-managed over 4,000 projects for the company, affording her key insight into our processes and projects. This experience is invaluable in allowing her to lead and empower Killer’s content and project management teams to success. Lucy enjoys managing the day-to-day at the office, offering a unique perspective when a team or colleague feels stuck, and learning from her peers and clients each day.

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