Art Meets Insight: Eye Catching Visualization For Better Analytics By Paul Shapiro

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ART MEETS INSIGHT EyeCatchingVisualizationForBetterAnalytics

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ABOUT Paul Shapiro

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WHAT IS DATA VISUALIZATION?

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“Theuseofcomputer-supported,interactive,visualrepresentationsofabstractdatatoamplifycognition.”Source:ReadingsinInformationVisualization:UsingVisiontoThinkbyByStuartK.Card,JockD.Mackinlay,&BenShneiderman

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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.  Enableyoutobetterconveyinformationtoyouraudienceandgetyourpointacrossfastandeffectively.

2.  Uncoverinsightsthatwouldotherwisegounnoticed.

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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 8.96mb of data from the eye every second.

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PICTURE SUPERIORITY EFFECT

“Weareincredibleatrememberingpictures.Hearapieceofinformation,andthreedayslateryou'llremember10%ofit.Addapictureandyou'llremember65%.”

Source: http://www.brainrules.net/vision and 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

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“Thefirstandmaingoalofanygraphicandvisualizationistobeatoolforyoureyesandbraintoperceivewhatliesbeyondtheirnaturalreach”Source: The Functional Art by Alberto Cairo

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MainWebsite Microsite

Male Female Male Female

542 694 491 613

634 829 764 551

AgeGroups

20-24

25-29

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2 TYPES OF DATA VISUALIZATION

DISCOVERY STORYTELLING

•  Wedon’tyetknowwhatwe’relookingforyetinourdata.Datavisualizationhelpsusdetectpatterns.

•  Optimizedforpatterndiscovery.Typicallyshowsmoredata.

•  Wealreadyhaveastorytotellandwanttotellitmoreeffectively.

•  Morerefined,moredesigned,lessextraneousdatapresented.

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

NOLook at your data using “Shneiderman’s Mantra”

DISCOVERY

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

NOLook at your data using “Shneiderman’s Mantra”

DISCOVERY

YES Determine what you would like to show and map to an appropriate chart type.

STORYTELLING

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TYPES OF DATA

Comparison

Comparemagnitudes

Relationship

Showcorrelations,outliers,and

clusters

Distribution

Howvaluesare

distributedalonganaxis

Composition

Howpartsofawholerelatedtoeachother

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MAP TO AN APPROPRIATE CHART TYPE

http://pshapi.ro/chartbytype

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

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

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

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

Moreofthislater

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STEP #5 Make 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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927428472847248248562009777477474710010477974974848025221112110956698424669629810921053

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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 DATA” -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 SIMULATOR http://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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DO THIS!

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

perceived as better.

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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 GRAPH Site Architecture

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VENN DIAGRAM Overlapping Organic Keywords

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TREEMAP Competitive Landscape

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WORD CLOUD Social-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 Visualization For Better Analytics By Paul Shapiro

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Transcript of Art Meets Insight: Eye Catching Visualization For Better Analytics By Paul Shapiro