art meets insight: eye catching visualizations for better analytics

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ART MEETS INSIGHTEye Catching Visualization For Better Analytics

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ABOUT

Paul Shapiro

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WHAT IS DATA VISUALIZATION?

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“The use of computer-supported, interactive, visual representations of abstract data to amplify cognition.”Source: Readings in Information Visualization: Using Vision to Think by By Stuart K. Card, Jock D. Mackinlay, & Ben Shneiderman

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IN OTHER WORDS…

USING VISION TO THINK

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THIS IS THE DATA VISUALIZATION WE’RE TALKING ABOUT…

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WE’RE NOT TALKING ABOUT THIS…

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NUMBERS!

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WHY SHOULD YOU CARE?

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MARKETERS ARE INUNDATED WITH DATA

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PROPERLY LEVERAGING DATA VISUALIZATION WILL…

1. Enable you to better convey information to your audience and get your point across fast and effectively.

2. Uncover insights that would otherwise go unnoticed.

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VISUAL PERCEPTION IS POWERFUL!

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Sources: http://bit.ly/2ayqCpV and http://bit.ly/2bhefeY

Most people read words at

about 120 words per minute

(or 8.16 bits per second).

The brain receives 8.96mbof data from the eye every

second.

http://bit.ly/2ayqCpV

http://bit.ly/2bhefeY

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PICTURE SUPERIORITY EFFECT

“We are incredible at remembering pictures. Hear a piece of information, and three days later you'll remember 10% of it. Add a picture and you'll remember 65%.”

Source: http://www.brainrules.net/vision and https://en.wikipedia.org/wiki/Picture_superiority_effect

http://www.brainrules.net/vision

https://en.wikipedia.org/wiki/Picture_superiority_effect

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60-65% OF THE GENERAL

POPULATION ARE

“VISUAL LEARNERS”

Source: https://en.wikipedia.org/wiki/Visual_thinking#CITEREFDeza2009

https://en.wikipedia.org/wiki/Visual_thinking#CITEREFDeza2009

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“The first and main goal of any graphic and visualization is to be a tool for your eyes and brain to perceive what lies beyond their natural reach”Source: The Functional Art by Alberto Cairo

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Main Website Microsite

Male Female Male Female

542 694 491 613

634 829 764 551

Age Groups

20-24

25-29

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2 TYPES OF DATA VISUALIZATION

DISCOVERY STORYTELLING

• We don’t yet know what we’re looking for yet in our data. Data visualization helps us detect patterns.

• Optimized for pattern discovery. Typically shows more data.

• We already have a story to tell and want to tell it more effectively.

• More refined, more designed, less extraneous data presented.

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OFTENTIMES, DISCOVERY TURNS

INTO STORYTELLING

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HOW TO GO ABOUT VISUALIZING YOUR DATA

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STEP #1Do you already know the story you’re trying to tell with your data?

NOLook at your data using

“Shneiderman’s Mantra”

DISCOVERY

YESDetermine what you would

like to show and map to an

appropriate chart type.

STORYTELLING

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LOOKING AT YOUR DATA WITH SCHNEIDERMAN’S MANTRA

1.Overview

2.Zoom and Filter

3.Details on Demand

LET’S APPLY TO SEARCH TRENDS DATA PERTAINING

TO SOME DIFFERENT SPORTS

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OVERVIEW

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ZOOM

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FILTER

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DETAILS ON DEMAND

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STEP #1Do you already know the story you’re trying to tell with your data?

NOLook at your data using

“Shneiderman’s Mantra”

DISCOVERY

YESDetermine what you would

like to show and map to an

appropriate chart type.

STORYTELLING

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TYPES OF DATA

Comparison

Compare magnitudes

Relationship

Show correlations, outliers, and

clusters

Distribution

How values are

distributed along an axis

Composition

How parts of a whole

related to each other

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MAP TO AN APPROPRIATE CHART TYPE

http://pshapi.ro/chartbytype

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STEP #2Make first attempt at creating a graph (most people stop at this step)

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STEP #3If graph seems suboptimal, try a different kind.

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STEP #4Refine the better graph using data visualization principles:

Preattentive attributes | Cleveland & McGill’s Research | Other rules-of-thumb

More of this later

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STEP #5Make it beautiful or send to a designer

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DATA VISUALIZATION PRINCIPLES

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Source: https://www.perceptualedge.com/articles/ie/visual_perception.pdf

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92742847284724824856200977747

74747100104779749748480252211

12110956698424669629810921053

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http://www.creativebloq.com/design/science-behind-data-visualisation-8135496 (derived from Information Dashboard Design by Stephen Few)

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THE CLEVELAND & MCGILL HIERARCHY

More Accurate

1. Position along a common scale

2. Position along nonaligned scales

3. Length/Direction/Angle

4. Area

5. Volume/Curvature

6. Shading/Color saturation

Less Accurate

Source: https://www.cs.ubc.ca/~tmm/courses/cpsc533c-04-spr/readings/cleveland.pdf

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Source: The Functional Art by Alberto Cairo

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DATA VISUALIZATION RULES OF THUMB

AND TIPS!

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“MAXIMIZE DATA-INK RATIO”-Edward Tufte

Source: http://www.tbray.org/ongoing/data-ink/di1

BetterBad

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Sources: http://www.exceluser.com/blog/1152/oh-no-chart-junk-from-the-wall-street-journal.html

http://ergotmc.gtri.gatech.edu/dgt/Design_Guidelines/hndchb35.htm

“MINIMIZE CHART JUNK”-Edward Tufte

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8% OF MEN (0.5% OF WOMEN) ARE COLOR BLIND

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AVOID RED + GREEN

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USE COLOR-BLIND FRIENDLY PALETTES!

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CHECK USING CHROMATIC VISION SIMULATORhttp://asada.tukusi.ne.jp/webCVS/

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DON’T USE PATTERN FILLS

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AVOID LEGENDS IF POSSIBLE

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BETTER!

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USE HORIZONTAL BAR CHART WHEN…

• Your category labels are

too long

• Your showing a notable

ranking relationship in your

data

Source: https://bi.luc.edu/ibi_help/index.jsp?topic=%2Fcom.ibi.help.ia%2Fsource%2Ftopic42.htm

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WHEN DATA IS INCOMPLETE, DON’T DO THIS…

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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

Traffic

Traffic

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DO THIS!

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Traffic

Traffic

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SHOW UPWARD SLOPING GRAPHS WHEN POSSIBLEResearch from Gattis and Holyoak, demonstrated that upward sloping graphs are

perceived as better.

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Date Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Rank

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Rank

✓X

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DO YOUR SCALES RIGHT

• When using a bar charts, begin the

scale at zero, and end a little above

the highest value.

• With every type of graph other than

a bar charts, begin the scale a little

below the lowest value and a little above the highest value.

• Begin and end the scale with round

number, and make the interval

round numbers as well.

Source: Now you see it: Simple Visualization Techniques for Quantitative Analysis by Stephen Few

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WHEN MAKING COMPARISONS…

Try adding a “reference line” to make those comparisons clearer.

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PICTOGRAPHS MAY HELP YOU REMEMBER DATA BETTER

Says research from Haroz, Kosara and Franconeri

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TRY TRELLISING DATA INSTEAD OF RESORTING TO 3D GRAPHS

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A LOT OF WEB ANALYTICS DATA IS TIME SERIES DATA…

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USE LINE GRAPHS FOR ANALYZING PATTERNS AND EXCEPTIONS

Seasonal

Decrease

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USE BAR CHARTS FOR EMPHASIZING & COMPARING INDIVIDUAL VALUES

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YOU COULD DO SOMETHING LIKE THIS…

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ALTHOUGH, YOU’RE BETTER OFF SHOWING THE DIFFERENCE

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YOU CAN USE RADAR GRAPHS AS AN OPTION FOR COMPARING CYCLES…

12-1am1-2am

2-3am

3-4am

4-5am

5-6am

6-7am

7-8am

8-9am

9-10am

10-11am

11am-12pm12-1pm

1-2pm

2-3pm

3-4pm

4-5pm

5-6pm

6-7pm

7-8pm

8-9pm

9-10pm

10-11pm

11-12pm

Average Visits by Hour

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BUT THIS CAN ALSO EASILY BE A LINE GRAPH

Average Visits by Hour

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USE HEATMAPS FOR ANALYZING HIGH-VOLUME CYCLICAL PATTERNS AND EXCEPTIONS

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USE BOX PLOTS FOR ANALYZING DISTRIBUTION CHANGES

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USEFUL VISUALIZATION FOR SEARCH

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NETWORK GRAPHSite Architecture

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VENN DIAGRAMOverlapping Organic Keywords

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TREEMAPCompetitive Landscape

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WORD CLOUDSocial-Keyword Research

http://searchwilderness.com/semantic-keyword-research/

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BUBBLE CHART

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HTTP://DATAVIZCATALOGUE.COM

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CONCLUSION

Follow the Discover to

Storytelling Process

Make use of

preattentive attributes

Utilize the Cleveland

and McGill hierarchy

Follow best rules-of-

thumb/best practices

Familiarize yourself with the different charts

and graphs available for use

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TL;DR

There’s no quick and easy way to make an ideal data

visualization, but with an understanding of some of the

basic data visualization principles we can make them a

whole lot better.

art meets insight: eye catching visualizations for better analytics

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