CatalystDigital.com ART MEETS INSIGHT Eye Catching Visualization For Better Analytics
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CatalystDigital.com
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.
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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
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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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“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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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…
0
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7000
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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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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan
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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10
15
20
25
Date Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Rank
0
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Date Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
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.
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THANK YOU!
SEE YOU AT THE NEXT #SMX
617-663-1247 | CatalystDigital.com© 2016 Catalyst | All Rights Reserved
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