Typography in data visualization and why does it matter

Published on May 20, 2021
Liviu Moroianu
Data & Research Manager

The writing is on the wall. The days are numbered for poorly designed data visuals. Nowadays, data presentations are expected to be at the editorial quality level. This means that text needs to be seamlessly integrated into the data visualization and work on multiple levels of functionality. You may easily use text formatting to signal importance, structure, or performance in data. Text effects can be creatively used to portray quantity, quality, length, and intensity. There are pitfalls as well, as some typefaces (profanely used fonts) have a strong personality or limited functionality and should be used judiciously. So, what is typography in data visualization and why is it important?

Typography in data visualization wears many hats

1. Data Functionality (more than data labels)

Basically, typography is creatively putting together letterforms to facilitate understanding. You need to enrich data visuals and graphs and imbue structure, hierarchy, or emotion in them. Plain old data labels or textboxes just won't cut it. There are several functionality levels a text operates on when being included in a data visualization. You need to use text as a standalone symbolic language to create patterns, hierarchies, showcase impact, and trigger emotions.

2. Data Accessibility (more than just mentions on data)

Your audience should be able to scan a data visual in instants and be able to easily digest all the information presented. To ensure legibility is primordial. All else is nice to have. Besides clarity and cleanliness, accessibility is all about how easily does your choice of format gets the message across. It is really that simple. Never sacrifice functionality for esthetics. Always account for color blindness, device/ medium optimization, and other impairments. One controversial example is the use of Comic Sans typeface that is said to improve legibility for people with dyslexia. To this respect, Vincent Connare the designer of Comic Sans still defends his work.

3. Data Visual (more than neatly written text)

Typography can be used as a standalone visual. Integrate legends in the visual. Use labels and iconography to make your visual legible and intuitive without making your audience having to look in two places or make associations. One great example of such practice is in Scott Sorli's data visual, titled Common Sense Revolution. This visual showcases how budget cuts on social service correlate with increases in the number of homeless deaths on the streets of Toronto between 1985 and 2006. The graph below uses the actual names of homeless people that died on the streets as a visual, quantitative measure.

Title: Common Sense Revolution

Author: Scott Sorli

Source: http://homelesshub.ca/gallery/common-sense-revolution

4. Data Meaning (more than text formatting)

Choice of typeface, color, size, shape, boldness, embossing is all part of conveying meaning through text. You may find yourself looking at data and asking yourself "Is this bad?", "Is this good?". The formatting of a text is like the voice of the text. You can establish an emotional tone by using warm colors or cold colors. Also, the brighter the text, the louder the voice. Highly saturated texts (e.g. black or red) signal importance and stand out while light texts (e.g. grey or bleached/ transparent red) are neutral or muted. Neutral texts are there for additional clarification or to signal that the spotlight is on something else. Dimming one text highlights another one and this is a commonly used tactic to showcase meaning.

5. Data Context (more than text content)

The context in data through text can be attained on multiple levels. One level is allegiance. This answers questions like clustering "us" versus "them", differences between categories, or any attempt of classification. There is such a thing as "data apparatus text" as well. This is the text used for graph scales, axes, sources of data, representativity mentions, time intervals, and anything else that can provide a sense of context on where the data comes from and what it relates to. The way in which you write this text should make it secondary to the main message and should be more on a "for your information" basis.

6. Data Structure (more than titles and headings)

Title, headings, subheadings, and body. Sounds pretty standard, right? You will be amazed how many people get this wrong in their visuals. Although we should not fall into the trap of hyperorthodoxy and exceptions to these rules are highly encouraged on highly creative but clean and streamline data visuals, the structure is a must. The structure provides cleanliness and cleanliness eliminates confusion. Don't make your audience work.

7. A data designer's way to stand out of the crowd

For an information designer, material success and access to opportunities depend entirely on the quality and uniqueness of work. Standing out from the crowd and delivering highly creative quality work will ensure that instead of you finding opportunities, opportunities are more likely to find you.

BONUS: Definitions (beyond word salad)

Typography is an umbrella term that includes both an art and technique of arranging type into written language that is legible, readable, and attractive. It includes the use and creation of letters, numbers, and symbols.

A typeface is a family of fonts and features the design of lettering including variations in size, weight (e.g. bold), slope (e.g. italic), width (e.g. condensed), etc. Each variation of the typeface is a font.

In modern usage, a font is used interchangeably with "typeface", however is good to know that a standard typeface (or 'font family') is composed of a number of fonts.

Type is used generally to refer to individual letters, numbers, and symbols assembled into pages for printing or displaying.

Lettering is the art. The of drawing letters, instead of writing them. Lettering is considered an art form and it is more in the realm of illustration than writing.

Now you know!

This is Liviu. In short, the insights guy.

Research & Data Visualization professional with background in management, marketing and advertising. Market Research & Advertising MA with trainer certification. Coordinated insightful research, reports, presentations and white papers with smart data analysis and editorial quality insights. Experienced data projects and data teams manager overseeing all stages from proposal to delivery.

Check out guidetoinsights.com for more!




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