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THE SCIENCE OF DATA VISUALIZATION It’s a cognitive truth: our visual system is enormously influential in

drawing up a collection of graphs, charts, and dashboards. Data

our brains. As an information learning and processing mechanism,

visualization is a creative process, and we can learn to enrich it

our brains are our best tool in decoding and making sense of

by leveraging years of research on how to design for cognition

information presented visually. The best data visualizations are

and perception. Think of successful data visualization from a visual

designed to properly take advantage of “pre-attentive features”

science perspective, and consider the careful balance of the art

– a limited set of visual properties that are detected very rapidly

of visual design and curation alongside the observations and

(typically 200 to 250 milliseconds) and accurately by our visual

insights of data science. The most meaningful data visualizations

system, and are not constrained by display size.

will be the ones which correctly present complex information in a way that is visually meaningful, memorable, and actionable.

Through pattern recognition, color recognition, and counting, data visualization exploits elements of our brain’s intrinsic

Data visualization should work to establish visual dialogue –

horsepower to help us better see and understand data. With self-

to leverage our cognitive visual hardwiring and the power of

service and user-oriented data visualization tools, we can leverage

perception to have a “conversation” with the data to glean new

our natural hardwiring to layer visual intuition on top of cognitive

information in salient, memorable, and lasting ways. The data

understanding to interact with, learn from, and reach new insights

visualization is the tangible byproduct of when art and science

from our data. We can also enter a visual dialogue with our data

come together to facilitate a visual discussion of data.

to find new answers and ask new questions. KEY COGNITIVE ELEMENTS IN VISUALIZATION This first brief in a four-part series will take a high-level look at: •

A distilled introduction to the science of data visualization

•

Key cognitive ingredients to have a visual dialogue with data

•

How to curate meaning in the data through visual cues

DATA VISUALIZATION IS A SCIENCE

Any work of art relies on core visual principles and elements. Three key building blocks of visual analysis are pattern recognition, color use, and counting. They are interrelated and can be integrated to create meaning visually from data. Patterns and Organization The way we perceive patterns is one of our most interesting

The brain is a remarkable organism. Even with recent advances in

cognitive functions. Patterns – the repetition of shapes, forms, or

cognitive science and perceptual psychology, our understanding

textures – are a way of presenting information to help our brains

of the brain is still somewhat primitive. There are many questions

discriminate what is important from what is not. There are patterns

on how our brains work that we still have yet to answer – and

around us every day that we may not even recognize – for example,

even more questions that we have yet to figure out even how to

the way television show credits list actors in a series (generally the

ask. However, after much research we do know that the brain is

top star first and the second last, making the first and final data

a highly visual mechanism. We know that areas like Wernicke’s

points in the pattern the most significant). Patterns are how our

Area and Broca’s Area are designed for the comprehension and

brains save time decoding visual information: by grouping similar

processing of language, and we know that the Visual Cortex – the

objects and separating them. The Gestalt principles of design

bi-hemispherical part of the brain responsible for processing visual

emphasize simplicity in shape, color, and proximity and look for

information – lights up when presented with colors and shapes.

continuation, closure, and figure-ground principles. The German

Being able to see and understand data requires more than simply

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When we look at any data visualization, one of the first things that

Color Use

the brain does is look for patterns. We discriminate background

Colors and shapes play a large part in patterns. Color (or the lack

from foreground to establish visual boundaries. Then we look to

of) differentiates and defines lines, shapes, forms, and space.

see what data points are connected and how (otherwise known as perceptual organization) – whether it is through categorical cues

However, the use of color in design is very subjective, and color

like dots, lines, or clusters, or through other ordinal visual cues

theory is a science on its own. Colorists study how colors affect

like color, shapes, and lines. Typically, there are five core ways to

different people, individually or in a group, and how these affects

apply pattern recognition:

can change across genders, cultures, those with color blindness, and so on. There are also many color nuances, including overuse,

Proximity – Objects that are grouped together or located close

misuse, simultaneous and successive color contrast, distinctions

to each other tend to be perceived as natural groups that share

between how to use different color hues versus levels of

an underlying logic. Clustered bar graphs and scatter plots utilize

saturations, and so on. For this discussion, let’s focus simply on

this principle.

when and how to use color in visualization to achieve unity.

Similarity – This principle extends proximity to also include items

In The Functional Art, Cairo writes, “The best way to disorient

that are identical (or close). This gives the brain two different

your readers is to fill your graphic with objects colored in pure

levels of grouping: by the shared, common nature of objects, as

accent tones.” This is because pure colors – those vibrant “hues

well as how close they are. Geospatial and other types of location

of summer” that have no white, black, or gray to distort their

graphics utilize this principle.

vibrancy – are uncommon in nature, so they should be limited to highlight important elements of graphics. Subdued hues

Continuity – It is easier to perceive the shape of an object as part

– like gray, light blue, and green – are the best candidates for

of a whole when it is visualized as smooth and rounded – in curves

everything else. Most colorists recommend limiting the number

– rather than angular and sharp. Arc diagrams, treemaps, and

of colors (and fonts and other typography) to no more than two

other radial layouts use this principle.

or three to create a sense of unity in a visual composition. Unity is created when patterns, colors, and shapes are in balance.

Closure – Viewers are better able to identify groups through the establishment of crisp, clear boundaries that help isolate items

When thinking about color use in your data visualization, focus on

and minimize the opportunity for error (even if items are of the

how you are applying your color efforts as visual targets:

same size, shape, or color). This effect would be applied, for example, in a clustered bar chart to add additional organization

Perceptual Pop-Out is the use of color as a visual beacon or target

to the pattern by alternating shading the area behind groups of

to pre-attentively detect items of importance within visualization.

bars to establish boundaries.

The shape, size, or color of the item here is less important than its ability to “pop out” of a display. Consider a visit to the eye doctor,

Patterns – These help to establish clear visual organization,

when your vision is tested by the ability to spot a flash of color in

composition, and layout. Once we can see patterns in information,

a sea of darkness.

we can next layer visual intuition on top of cognitive understanding to come to new conclusions. This is where color and counting

Conjunction Target is the inefficient combination of color and

come in.

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conjunction targets mix color and shape. That distracts and

most of us, numerosity gives us the ability to visually “count”

causes visual interference, making visual analysis and other

somewhere between two and ten items. We can further enhance

cognitive processes slower and more difficult. Thus, it is prudent

numerosity with visual elements like color. As an exercise, glance

to maximize cues like perceptual pop-out, while not inadvertently

quickly at Figure 1 above. How many orange sixes do you see? If

interrupting the pre-attentive process with conjunction targets.

you “see” 7, you are correct. Most types of data visualization will include a numerosity effect, however those that take advantage

As illustration, the figure

of data reduction processes will be most beneficial for numerosity.

below is inspired by a picture

Consider scatter plots, histograms, and other clustering

made by visualization guru

visualizations.

Stephen Few. It is much harder to see the number

CONCLUSION

6 in sequences without the benefit of shading. Likewise,

The downside to being able to visually create meaning so quickly

we can take advantage

and efficiently is that our brains can betray us and leave us with a

of perceptual pop-out by

wrong idea – visual bias. Data visualizations are intended to clearly

adding an additional color

and effectively communicate the correct information and insight.

element into the picture. This example shows how elements of

Thus, we should pay close attention to recognizing key cognitive

pattern recognition, color, and shape can be used together while

elements in visualization, and how these should be used together

avoiding the clutter of conjunction targets.

to craft a meaningful representation of data. This Research Brief has highlighted some key cognitive elements affected by data

Counting and Numerosity

visualization. The next Brief in this series will review the visual

Color and counting work in tandem, as do counting and patterns.

elements of formal analysis that are building blocks to visual

Relatively new research shows that the brain has an ordered

discovery.

mapping, or topographical map, for number sense, similar to what we have for visual sense and other pre-attentive features.

This brief was originally published by the International Institute of Analytics (IIA) in July 2015

There are two relevant counting conversations within the scope of data visualization. First is how data visualizations try to reduce counting by clustering or similar approaches designed to replace similar data objects with an alternative, smaller data representation. Histograms take this approach. Visual spacing on linear scales versus logarithmic scales is another example. Second is numerosity – an almost instantaneous numerical intuition pattern that allows us to “see” an amount (number) without actually counting it. This varies among individuals (people with extreme numerosity abilities are known as “savants”). Numerosity itself is not an indicator of mathematical ability. For

Radiant Advisors is a leading strategic research and advisory firm that delivers innovative, cutting-edge research and thought-leadership to transform today’s organizations into tomorrow’s data-driven industry leaders. To learn more, visit www.radiantadvisors.com

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VISUAL DESIGN BUILDING BLOCKS When you first look at a data visualization, you may not realize just how much careful thought and effort has gone into crafting it. ata visualizations are multi-faceted, not just because of their ability to represent multiple types and layers of data in a meaningful way, but because to craft the visualization itself requires attention to every detail

from color choice, to the methods in which the

important pieces of information are connected into patterns, to the layout and typefaces used throughout the graphic. In the previous brief, focused on features

The

cience of

ata

Figure 1 – This figure shows the added patterning influence of a

isualization,

color, patterns, and counting

that exploit

our brains’ intrinsic pre-attentive horsepower and form the basis of a visual dialogue with data. In this brief, we’ll take a deeper look into how to establish visual meaning of data through the intentional selection of specific elements. These elements textures, shapes, and typography

line as a visual cue.

lines,

are some of the visual cues

that influence how our eyes move around a visual to separate important areas from unimportant areas. These visual cues guide us to organize the information to facilitate meaning through visual discovery. LI The most basic building block of visual analysis, lines have several purposes in data visualization. They are used to create complex shapes discussed in the following section), to lead us visually through or to) different areas of the visualization, or as a way to layer texture on a visual surface.

Lines can be used as labels, directional cues, or they can also be used as a way to create texture in data visualization. Texture is one of the more subtle design elements to include in data visualization, but worth a mention due to its relation to lines and shapes and our previous discussions on color). Texture is defined as the surface characteristics or, the feel) of a material that can be experienced through the sense of touch or the illusion of touch). It can be used to accent an area so that it becomes more dominant than another, or for the selective perception of different categories.

olors, shapes, and textures

can be combined to have further levels of selection. Finally, textures of increasing size can represent an order relation. Because it is usually accompanied with real adjectives like rough, smooth, or hard, texture may seem intrinsically three-dimensional real). It can also be two-dimensional.

onsider how some graphs take

advantage of angles like points or circles).

Lines are especially potent tools to reinforce patterns or order.

In data visualization, lines are one of the best ways to represent

This is because they offer powerful cues for our brains to perceive

texture. For example, when used in tandem with color, lines can

whether objects are intended to grouped or linked together.

create texture through what is called color weaving to produce a

onsider a diverse group of different colored shapes. With its

tapestry of woven colors to simultaneously represent information

pre-attentive capabilities, the brain will automatically group

about multiple co-located color encoded distributions. For

them by shape and color. Adding a line to connect a subset of

example, color weaving is similar to the texturing algorithms

common shapes will add more connectedness, and produce a

and techniques used to visualize multiple layers in topographical

more powerful pattern see Figure 1).

maps, weather maps, and or other climatology visualizations.

Pre-attentive features are defined as a limited set of visual properties that are detected very rapidly and accurately by our visual system, and are not constrained by display size 1

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HAP

make sure you are using color priority in choosing a shape if you want to use circles to emphasize areas of opportunity for sales

In his article The Tall Louis ullivan 1

ffice Building Artistically

onsidered,

agents on a map, use green circles instead of red or orange).

6) made a statement that has forever affected

econd, be aware of color contrast and luminance between

how we approach the premise of shapes and forms. He said All

shapes. The higher the luminance contrast, the easier is it to see

things in nature have a shape, that is to say, a form, an outward

the edge between one shape and other. If the contrast is too low,

semblance, that tells us what they are, that distinguishes themselves

it can be difficult to distinguish between similar shapes

from another.

even distinguish them at all.

ver the years, this form follows function refrain

or to

has been taken both literally and completely misinterpreted it has also been described as coarse essentialism by data visualization

Like many visual cues, there is often no

design gurus like Alberto

encoding visualization properly through the use of shapes and

airo). Today, in visual design, it is a

one right way

powerful mantra primarily applied to the relationship between

forms.

the forms of design elements, i.e., shapes and lines) to the

more a consideration for what is easier for the viewer. As an

any times it becomes less a question of correct and

informational function it is intended to serve.

example, both scatterplots and bar charts can be used to represent absolute variables. Both of these also support the description of

As forms, shapes are one of the ways that our brains understand

the use of shapes, forms, and colors to aid visual meaning of data.

patterns. This is a time saving technique: we will immediately group similar objects and separate them from those that look

T P G APH

different. o, shapes and forms should be used to achieve what the visual is attempting to communicate about the data. Again,

Generally speaking, when we think of typography within the

refer to Figure 1 above. This visual shows, at a glance, the impact

context of a data visualization, we think in terms of two choices

that shapes can have when looking at information.

of type categories: serif versus sans serif. While the origin of the word serif is unknown, a common definition for it has come

hapes are formed with lines that are combined to form squares,

to be feet. Thus, serif typefaces

ew oman or

triangles, circles, and so on. They can be organic irregular shapes

Baskerville, are those with

found in nature circles, etc.)

shapes with strong

the word sans comes from the French without. Thus, sans

lines and angles like those used in mathematics). Likewise, shapes

serif typefaces are those without feet. erif fonts are usually

can be two-dimensional

considered to be more traditional, formal typefaces, while sans

or geometric

) or three-dimensional

). These

feet.

like Times

Building on the previous,

shapes expand typical two-dimensional shapes to include length,

serif typefaces tend to have a more contemporary, modern feel.

width, and depth

While these rules of thumb exist, there are no absolutes of when

they are things like balls, cylinders, boxes,

and pyramids. In data visualizations like pictograms, infographic

or when not

to use a serif versus a sans serif typeface.

forms and shapes can be expanded significantly through the issue of icons and other symbolic elements as extensions of traditional

In fact, in his book Data Points: Visualization That Means

shapes.

Something,

onsider, for example, the use of shapes of people in lieu

of dots or other shapes.

athan au pointedly notes that while there has been

much discourse on the best typeface, there has yet to be any true consensus. This goes to further emphasize that typeface selection

Further, like so many other elements of design, color has a hand

is highly variable and depends much on personal preference. As

in shape selection. This is particularly relevant in two ways. First,

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First, be conscious of the amount and types of The most pointed advice one can be given on typography is

typefaces and fonts used in a single visualization

this: use typeface and fonts with a purpose. It is easy to dismiss the importance of these selections, possibly because we are so

Second, different typefaces and fonts have

conditioned to read text that we have become accustomed to focusing on the content of the words and not what they look like

different connotations and perceptions

visually. However, the visual appearance of words can and does) have just as much effect on how a document is received as the

And, while there is no absolute rule on typeface

content itself. Fonts can create mood and atmosphere they can

selection, general rules of thumb apply

give visual clues about the order in which a document should be read, and which sections are more important than others. Fonts can even be used to control how long it takes someone to read a

That said, there are a couple of important points to keep in mind

document. Like colors, typefaces are typically chosen in corporate

when making a typeface or font selection. First, typography, like

style guides and other branding design decisions. It is valuable to

another other visual element, is no stranger to bias. There are some

understand why typography is as important in data visualization as

typefaces

any other design element.

for example, omic ans

that have been reduced to

a sort-of comic strip application and are not taken seriously. like Baskerville and Palatino

thers

conjure up nostalgia imagery due

P TTI G IT T G TH

to their historical use in vintage graphics. ome typefaces have been custom-created for use in advertising

like those fonts used

ou might have noticed a theme in many of these discussions on

and are pigeon-

visual cues. They all seem to tie back to one or more pre-attentive

holed into their use in genre-related opportunities which is not

features discussed in the first brief. It is easy to see how visual

necessarily a bad thing, but one to be aware of).

any typefaces

elements like lines, textures, shapes, colors, and typography

are also said to have personality. Like omic ans, others may come

stimulate cognitive pre-attentive features in our brains that are

with a more light-hearted or conservative personality.

atching

so critical in visual analysis. Hence, these are the building blocks

type personality with the tone of the message in the visualization

of visual discovery, intended by design to be layered upon each

is certainly not an exact science.

other and used in mix-and-match fashion to make the most of the

in tar Wars or Back to the Future, for example

visual capacity of data visualization. A good technique to see if you’re choosing appropriate fonts is to use a font that seems completely opposite of what you’re trying

This brief was originally published by the International Institute of

to convey.

Analytics (IIA) in August 2015

eeing how

wrong

a typeface can look will help

you make a more appropriate selection.

hristoph Papenfuss’

blog, Performance Ideas, shares a visualization that is a perfect example of how fonts should not be used in data visualization in a

11 spending report from Papenfuss’ hometown in Germany.

The visualization has a long list of errors in design.

pecific to

typography choices, it uses text that is so dense it is rendered almost completely unreadable. www.performance-ideas.com

ou can see graphic at http: 1

poor-visualizations .)

adiant Advisors is a leading strategic research and advisory firm that delivers innovative, cutting-edge research and thought-leadership to transform today’s organizations into tomorrow’s data-driven industry leaders. To learn more, visit www.radiantadvisors.com © 2015 Radiant Advisors. All Rights Reserved.

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DESIGNING FOR VISUAL EXPERIENCE The first two briefs in this series The cience of ata isualization

The Great Less is

ore

ebate

and isual esign Building Blocks) have attempted to disentangle the compounding factors that affect visual cognition, including

There is ongoing debate in the visualization community regarding

perceptual pop out, pattern recognition, numerosity, etc.

the role that a visualization type plays in data understanding and

These previous briefs have effectively set the foundation

memorability. And, like all great debates, there are convincing

for understanding the science of data visualization and key

arguments on both sides of the topic.

visual design building blocks that make data visualizations so immediately meaningful by leveraging our brains’ pre-attentive horsepower. This brief,

The conventional view argues that visualizations should be devoid of chart junk and curated with as few design elements as possible, and that simplifying a visualization increases

esigning for

isual

xperience, moves beyond the

premise of achieving efficacy in balancing art and science, to understanding how to create a visual experience for complex information through the lens of data visualization. We focus on the value of data visualization as a form of communication and storytelling, and we emphasize achieving balance within a triangle of design constraints that amplify the viewer experience of visualization and capitalize on visual memorability and learning.

memorability and information saliency without the distractions that lead to potential misinformation and conclusions. upported by many data visualization gurus, this position is reinforced by many psychology lab studies. However, there is a body of research that argues against lightweight design: chart junk might actually improve retention and force a user to expend more cognitive effort to be able to understand and learn from the visual, thereby increasing both knowledge and understanding of the underlying data.

WE REMEMBER VISUALLY ecently, attributes-based visual recognition has received a lot of attention with in-depth studies occurring in both academia and industry.

any of these studies have provided clear data and

learning opportunities. We’ve long known that we can remember upwards of 1 ,

images at one time, and that we can recall

these images accurately at an

percent recollection rate. This

speaks volumes to a person’s visual capacity and the power of pictures as recollection engines in the brain. We also know now that visualizations that blend information with influential features such as color, density, and content themes such as recognizable icons) significantly and reliably increase learning, memorability, and recall. Further, seeing and interacting with an image in combination with traditional written and verbal instruction has been consistently associated with higher levels of retention and understanding of salient ideas.

Figure 1

isual representations and examples of) the Less is

ore approach

The original visualizations can be found at: ooblogs and The preadsheet Page

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Less is

ore

echanical Turk) and tested the influence of features such as color, density, and content theme) on participants’ memorability.

A recent collaborative research project by computer scientists from Harvard and cognitive scientists from

IT1 explored

The study confirmed previous research findings that faces

cognitive memorability of visualizations. The researchers began

and human-centric scenes are more memorable than others.

with hopes of uncovering empirical evidence to support the

pecifically, people and human-scale objects contributed most

theory that while each of our sets of memories is unique, we have

positively to the memorability of images. The results of this study

the same algorithm embedded in our minds to convert visual

also confirmed that certain design principles make visualizations

communication to memory, and thereby learn and retain learning.

inherently more memorable than others, irrespective of a view’s individual context and biases.

sing a publicly available data set, the researchers augmented an object and scene annotations to include spatial, content, and

However, along with validations this study also revealed some

aesthetic image proprieties). ome of their research findings were

contrary findings. For example, visualizations with low data-to-

surprising. For example, one highlight was that unusualness and

ink ratios and high visual densities more chart junk) were actually

aesthetic beauty are not associated with high memorability, but

more memorable than minimalist data visualizations. Likewise,

instead negatively correlated with memorability. This challenged

unique visualizations that left a lasting impression were more

the popular assumption that beautiful images beauty being a

memorable than traditional, common graphs

function of aesthetics) are more valuable in memory currency.

graphs which are considered part of the data visualization canon.

bar charts or line

Another interesting find was that a visualization is instantly and overwhelmingly more memorable if it includes a human-

THE VISUAL EXPERIENCE BEGINS WITH A PICTURE

recognizable element, such as a photograph, person, cartoon, or logo. These types of elements essentially provide our memory

The above arguments aren’t to say that bad data visualizations

with a visual cue to build a story around, linking back to our

those that fail to take into account design considerations or

most primitive form of visual communication: symbolism, or the

those that incorrectly graph information

are okay.

f course,

practice of representing things by symbols. This finding creates

they’re not. But, that said, the research does reinforce the

a compelling case for the use of icons in visualizations such as

power of pictures as a key part of how we communicate, learn,

infographics, which often rely on symbols to communicate large

and remember.

or complex data in straightforward ways.

is more than one way to communicate effectively through data

oreover, it does support the idea that there

visualization. ore is

ore Through our intrinsic hard wiring to communicate, learn, and

n the other side, a study2 on what makes a visualization

remember visually, we develop the capacity for visual dialogue.

memorable began with researchers building a broad, static

This visual dialogue is paramount to the experience of visualization.

visualization taxonomy of the large variety of data visualizations

onceived by

in use today. The researchers collected nearly 6,

visual

onnecticut’s art department, visual dialogue is the exchange

representations of data from various publications and used a

that occurs between the artist, his work, and its consumer. It builds

wide range of attributes to categorize these images.

upon the basis of visual memorability and literacy.

ext, the

athan

nobler, former chair of the

niversity of

researchers exposed the images to participants via Amazon 1

Isola, P., iao, ., Torralba, A., liva, A. What makes an image memorable? I omputer ision and Pattern ecognition P ), 11. Pages 1 -1 .

onference on

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ognitive psychologists describe how the human mind, in its

our incredible ability to remember huge quantities of images with

attempt to understand and remember, assembles bits and

a highly accurate recollection rate is the fact that we remember

pieces of experiences and information into a story. To aid in this

them better than words. And we remember the images long after

story-building process, we automatically conjure images

we have forgotten the words that go with them. While words and

cues

visual

associated with the story. This is the core of visual data

storytelling, but it speaks inherently to the power of pictures as a visual experience.

ata visualizations highlight the connections

definitions vary from language to language, visualization human communication mechanism

as a

is universal.

between visualizations, design, and elements of science, and they allow audiences to explore and develop a connection through personal insight and experience.

THE PICTURE SUPERIORITY EFFECT The concept of The Power of Pictures is not new it has been described as an argument that’s both old and perennial. As an example, consider this quote from a turn of the century text on nglish composition: In some respects

words cannot compare

Figure

isual representation and example of) the Picture uperiority ffect

in effectiveness with pictures. The mere outlines in a Greek vase painting will give you a more immediate appreciation of the grace and beautify of the human form than the pages of descriptive writing. A silhouette in black paper will enable you to recognize a stranger more quickly than the most elaborate description in the world. The Picture

In a previous brief in this series, we noted that data visualization is a highly curated hybrid of forces and elements: when you first look at a data visualization you may not realize just how much

uperiority

ffect P

) recognizes that concepts

learned by viewing pictures are more easily and more frequently recalled than those learned purely by textual or other word-form equivalents including audio or other information that is learned by hearing).

TRIANGLE OF FORCES

evelopmental molecular biologist

quantified the P

ohn

edina

in the following way: when we read text, three

days later we only remember 1 percent of the information. et, text combined with a relevant image is more likely to be recalled at a much higher rate

6 percent in three days.

careful thought and effort went into designing it. In this brief, we’ve discussed two research views on when a data visualization accurately conveys a message and when it reaches the tipping point of too much. Frequently, the intent of visual design is to clearly and effectively communicate a single message. The best data visualizations are those where nothing stands between the visual’s message and its audience. While research is ongoing and the debate continues, the simple truth is that when visual cues are used correctly, they

Whether art, memories, or illustrations for educational purposes,

can bring data to life and give it more context, meaning, and

people are drawn to pictures. This is because of the experience:

resonance. ata visualizations should be focused on the message

pictures conjure emotions, memories, and insights. They

in the data, and visual enhancements like hue, saturation, size,

stimulate new thinking. In this context, even more important than

and color) should be used for emphasis rather than explanation.

Borkin, ., o, A., Bylinski, ., Isola, P., unkavalli, ., liva, A., Pfister, H. I F I 1 : What akes a isualization emorable?. Atlanta, GA: I . 2

1 ). Proceedings from I

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very graphic is shaped by a triangle of constraints: the tools and

processes that make it, the materials from which it is made, and its purpose or utility. This idea of design constraints as a triangle

emorability is an intrinsic feature of visual information and

of forces came from acob Bronowski, a mathematician, whose

is reproducible across a diverse spectrum of visualization

discussions on context and visualization had a large impact. In

mechanics. isuals that blend information with influential features

The hape of Things Bronowski wrote: The object to be made

are significantly more memorable. We have high visual memory

is held in a triangle of forces

If the designer has any freedom, it

is within this triangle of forces or constraints.

capacity and can recall thousands of images for a long time.

ore

importantly, concepts learned by viewing pictures are more easily and more frequently recalled than those learned purely by textual

However, Bronoswki’s triangle of forces is not fixed. ach of its sides

or other word-form equivalents.

can move, and as they do, the other sides in tandem so that they move along with it. ach move of one side puts strain on the other

ertain design principles make visualizations consistently more

two. Thus, it is important to not only recognize the parameters of

memorable than others, irrespective of a viewer’s individual context

the triangle of forces, but to strive for balance within it.

and biases. isuals that blend information with influential features are able to create a visual experience for the viewer and, therefore, are significantly more memorable. eeing and interacting with an image also has been consistently associated with higher levels of retention and understanding of salient ideas. Poorly designed data visualizations that misrepresent data may very well be memorable but for all the wrong reasons, making them possibly dangerous.

Figure

isual representation of the Triangle of Forces Theory

This brief was originally published by the International Institute of Analytics (IIA) in September 2015

I N S I G H T

S E R I E S

DESIGNING FOR INFLUENCE Previous briefs in this visual design series have stepped through

guess what we think the information presented is intended to

the science, artistic curation, and various cognitive processes

mean (or could mean) and then look for additional data points

enlisted in building a data visualization. In each of these briefs,

and visual cues to refine and confirm that.

we’ve covered assorted design considerations for developing data visualization for improved visual discovery and analytics.

This process of visual querying supports the notion that

However, so far we approached this primarily through a lens

perception is indeed active as opposed to passive. Figure 1,

of functionality and principles for use. In addition, there is a

which is borrowed from

substantial contribution that the individual consumer of the

Mastering the Information Age1, illustrates this process by

visualization brings to the table in how data visualizations are used

indicating ten steps or aspects of the active nature of processing

and understood, whether it’s an analyst user, business leader,

visual information.

is aster’s

ix, Pohl, and

llis’ book,

customer, and so on. However, although this fact is acknowledged and a wealth of long-standing literature covers the technical aspects of both design and visualization, the collaborative, social, and organizational aspects are less studied. In this fourth and final brief, we tackle some of the key elements of designing for influence by exploring the importance of viewer perception and how this can be leveraged through certain applications of techniques used to influence the user. This is the human element of visualization, not the scientific, artistic, or cognitive, and it plays a critical role in communicating through data visualizations. In particular, interactivity and emotion (storytelling) are two ways we can leverage data visualization to influence our audience.

Figure 1

From

ix, Pohl, and llis, this visual illustrates a simplified view of the

broad visual analytics process

TH P W

F I W

PTI ecent research in the

Though data is core to the analytical process, the human is

psychology of perception indicates that visual perception is

fundamentally at the heart of visual analytics. Therefore, the user

an exploratory and active process wherein we do not see a

is in a position to affect multiple aspects of the visual process,

sequence of static images, but a continuous flow of changing

including visual perception, human interaction, and problem

scenes and imagery. Thus, we have to actively

solving analysis and activities driven by data visualization.

Perception is not a passive experience.

look around

to get a more comprehensive image of the information being

While there are multiple cognitive and perceptual processes

presented. To support this, the use of data visualization is often

that will affect user biases and responses to data stimuli, we

aptly characterized as an active and exploratory process with the

must remember that visualized data is largely used by people.

goal to yield sometimes-complex insights.

uring the course of

Further, these people may or may not have received formal data

this process, data analysts and other users routinely generate

analysis education or possess detailed knowledge of visual design

hypotheses, and then test them against the data that is visualized.

principles. It is people who use a visual to make decisions and

This visual analysis process is akin to the scientific method: we

take action.

http: www.vismaster.eu news mastering-the-information-age

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Also, while reactions will to some degree vary from person to

by researchers i et al2:

person, the important takeaway is that people are different, and

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we have to be aware of these variances and accommodate them. Instead of focusing this final brief on more of the wide context of

precursor to another operation •

core elements for successful visual design, we narrow the focus on the human aspects between a user and a data visualization

ITI G I T

A TI

econfigure: spatially rearrange the data e.g., sort, rotate, change attributes assigned to axis)

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like interacting with visual stimuli or inviting emotion with visual data storytelling.

xplore: activities that include movements such as zooming, panning, resampling, etc.

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in terms of interaction and analytical processes and emphases. With this in mind, we can better grasp the influence of actions

elect: identify and select items of interests, possibly as a

ncode: alter visual appearance e.g., change view or adjust attributes like color, size, and or shape)

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Abstract laborate: show more or less detail drill down or up)

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Filter: select show data matching specific conditions or criteria

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onnect: highlight data related items e.g., brushing)

In data discovery, we often describe the discovery process as iterative and agile. Further, we assume that analysts working

A word of caution here: the work of an analyst is influenced by a

with data require the ability to move quickly and fluidly through

host of cognitive biases, and many of these are set into motion

information and tools to extract, explore, and uncover new

by the way information is “fed” into the perceptual cognitive

meanings and analytical models

processes

insights

generally referred to as

enlisted

through

data

visualization.

Interactive

in previously unexplored information, or even in new

visualization designs may influence biases and vice versa), and

configurations of blended data. Another important element,

thus we need to aware of how interactive visualizations affect

especially for the use of data visualizations as a vehicle to visual

these and work to minimize them. For example, consider the

discovery, is interactivity

the ability for users to interact directly

difference that a data visualization printed on paper may yield

with the data and the accompanying visualization to deepen

insomuch as the users’ ability to visually move through the data by

analytics insight.

drilling down or expanding certain elements, and how much more valuable this process would natively be in a digital environment.

Interactivity supports the type of visual thinking that drives visual discovery. Without interactivity, visual discovery falls short of its

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A WITH

intended purpose and its analytical functionality is constrained

Award-winning writer and director

by the limitations of static imagery. However, with the right

most powerful way to motivate people to action is by “uniting

interactivity, data visualization becomes a natural extension of

an idea with an emotion. The best way to do that is by telling

the users’ thought process. Interactivity, then, is the element that

a compelling story that weaves in a lot of information and also

allows users to play with data: to manipulate information into

arouses the listener’s emotion and energy.

patterns, theorize about meanings, project interpretations, explore

good story fulfill s a profound human need to grasp the patterns

possibilities

of living

all while balancing the fixed content, context, and

relationships of the data with creativity and imagination.

obert

c ee said that the

c ee argues that a

not merely as an intellectual exercise, but within a very

personal, emotional experience. A good story turns a one-sided narrative into a conversation

it influences an audience to action.

Interactive capabilities in data visualization should support and facilitate the users’ intentions when visually exploring data sets.

isual data stories should always put data at the forefront

onsider the following list of interactive category types suggested

of storytelling. However, data stories differ from traditional

i, . ., ah ang, ., tasko, . T., acko, . A. ). Toward a deeper understanding of the role of interaction in information visualization. Visualization and Computer Graphics, IEEE Transactions on, 13 6), 1 -1 1. 2

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storytelling that typically chains together a series of causally related events to progress through a beginning, middle, and end with the

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utliers

a powerful way to show something outside the

realm of normal

goal of delivering a message. While data stories can be similarly

As a best practice, visual data stories are most effective when

linearly visualized, they can diverge.

ore important, data stories

they have constrained interaction at various checkpoints within

can also be interactive to invite discovery, solicit new questions,

the narrative, allowing the user to explore the data without

and or offer alternative explanations.

veering too far away.

isual cues for storytelling include things

like annotations to point out specific information using color to What makes visual data storytelling different from other forms of

associate items of importance without having to tell them or even

storytelling is the complexity of the content being communicated

visual highlighting (e.g., color, size, boldness) to connect elements.

within the constraints of effective data visualization. It is essentially information

compression

that

condenses

information

ome data visualization tools are being enhanced with storytelling

into

capabilities, too, and vendors in the space are supporting the use

manageable pieces by focusing on what’s most important, and

of this engagement and influencing technique as part of their

pretending it is bound entirely within the visualization(s) used

tool’s platform. Tableau, for example, has its story points

to illustrate the message. To that end, graphical techniques and

storytelling snapshots

lik its

ellowfinBI its storyboard.

interactivity can enforce various levels of structure and narrative flow. For example, consider an early reader picture book, which illustrates salient points with a visual

sometimes interactive

Interactivity and storytelling both affect how the user experiences

emphasize key points of learning. And, though static visualizations

a data visualization and are primary means of influencing audience

have long been used to support storytelling, today an emerging

response to information presented.

class of visualizations that combine narratives with interactive data and graphics is taking more of the spotlight in conveying

With interactivity, a user is invited to get hands-on with the data,

visual narratives.

and explore the information with

visual thinking

that drives

visual discovery. Thus, the data visualization becomes an extension For the purposes of storytelling, the basic plots of visual data

of the user’s thought process and enables them to “play” with

stories can be articulated as the following:

data and discover their own insights. Likewise, storytelling is an

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appropriate way to unite an idea with emotion in data visualization.

hange over time

see a visual history as told through a

simple metric or trend •

rill down

start big, and get more and more granular to

find meaning •

oom out

The best data stories are influential: they are interactive to invite discovery, engaging to solicit new questions, and thoughtprovoking by offering alternative explanations to convey

reverse the particular, from the individual to a

compelling visual narratives.

larger group •

ontrast

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pread

the this or that help people see the light and the dark, or reach of

data (disbursement) •

Intersections things that cross over, or go from less than to “more than” (progression)

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Factors

things that work together to build up to a higher-

level effect