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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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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.
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THANK YOU!
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