1.
Visualisation Workflow:
Finding Stories and
Telling Stories
Andy Kirk

2.
Hebden Bridge

3.
Data Visualisation Blogger

4.
Architect | Consultant

5.
Trainer
tion
duct ion to DataVisualisa
Training Courses Intro
le
Current Public Schedu
Visualisation
The Growth of Data
le of 2012:
e through to the midd
publi c traini ng cours
mean s for These are the scheduled
ded us with ubiquitous Arts, Copenhagen | £250
COP2
in technology have provi e once data h Academy of Fine
Exponential advances amounts of data. Wher Thu 8 Mar | Royal Danis Copenhagen | £250 COP1
mobi lising incredible mers have h Academy of Fine Arts,
creati ng, recording and Our attitudes as consu Fri 9 Mar | Royal Danis London | £235 LON3
captured in abundance. for visual insight House, University of
was scarce, now it is openn ess and yearn Thu 26 Apr | Senat e York City | £250 NY C1
nd transparency and ournalism, CUNY, New
also evolved: we dema Fri 1 May | Grad Schoo
1 l of J
n DC | £250 WDC1
to aid our understand
ing. ation Center, Washingto £250 BAL1
for the Mon 1 May | Found
4
widespread capabilities Wed 1 May go | £250 CHI1
s to fantastic tools and iques requi red
6
Center Conference, Chica
Yet, whilst we have acces knowledge and techn Fri 1 Jun | University
5
analysis of data, the Toron to | £250 TOR1
storage, handl ing and Mon 1 J | Venue TBC,
8 un £235 BRS 1
instin ct
ach based on intuit ion, Fri 29 J un Edinburgh | £235 EDI1
e world, a design appro Hotel , University of
a cluttered, competitiv Fri 6 J | Salisury Green
ul 1
Amst erdam | £250 AMS
data visualisation comes
in. Fri 1 Jul | Venue TBC,
3
overload. This is where
l A 1 discount
0%
comm unications that appea Train ing page on
and innovation, designing
unleashing creati vity regist er to atten d an
event .
way our eyes and brain
s process om where you can also
exploiting the www.visualisingdata.c
aimed at under standing and
recen t times
h in popularity over
lisation and its growt sizes and
The interest in data visua isations of all shapes,
story. As a result , organ ster now to reserve a
Places are limited so regi
has been a remar kable value.
ation of its poten tial
waking up to the realis
domain are now training workshop.
place on your preferred
tent
Training Course Con Visit the www.visualising
data.com, select the
with a comprehensive, d location.
The objective of the
traini ng is to provi de delegates
Training page and click on your preferre
excitement
event s buzzi ng with
tion. You will leave the have acqui red,
impact and ampli fy cogni ical capabilities you
knowledge and pract rtunit ies
about the foundation n challenges and oppo
on future data visualisatio
inspir ing you to take
Further Information
in the courses will include: environment
main topics cover ed Class size a supportive learn ing
The size is 20 to facilit ate
of data visualisation The maxim um class
and modern context
Historical background n visual system en all attendees.
of design and the huma group discussion betwe
Foundation principles and select ion
The essen tials of chart design
and resources Refreshments
tial visualisation tools centr al locati ons.
Exploration of the essen process ed. All event s will be held in city
n methodology and lunch will not be includ
The visualisation desig n
ing to visualisation desig
Applying critical think itioners
ice exam ples and pract Laptops
Showcase of best pract s
Visualisation project case studie
lisation challenges g the day’s activi ties.
re your own data visua across the group durin
Opportunit ies to explo have a some devices
Times
? end of the
Who Should Attend
time allocated at the
g from 9:00 and extra
regist ration comm encin r discussion s.
nsibil ity for, or is intere
sted in quest ions or hold furthe
for anyone who has respo comm unicating data. session to pick up any
The courses are suited and
for visually exploring
best pract ice approaches
.
Visualising Data Ltd
body who
lex datasets, or some
st with large and comp t be an
You might be an analy gement repor t. You migh
the occasional mana visualisation
just wants to enhance Ltd, a UK based data
er of Visualising Data ber of this
crowd. You might be
a Andy Kirk is the found has been an active mem
g to stand out from the traini ng service. He
to adver tising and are lookin ner witho ut progr amm ing skills. design consultancy and
design traini ng or a desig sector.
progr amm er with no eering or the publi c lisingdata.com.
cine, the media, engin popular blog www.visua
You might be in medi
we’ve all
Data is everywhere and
is no typical delegate. is most
The point is that there Anyone and everyone
with it, so let’s do it right.
got to do something
to atten d!
welcome and encouraged

6.
Trainer

7.
Speaker

8.
Author

9.
Career
Lancaster University | 1995 to 1999
Degree in Operational Research + Year in Industry
Co-operative Insurance Society (CIS) | 1999 to 2001
Business Analyst
West Yorkshire Police | 2001 to 2007
Performance Analyst > Information Manager
University of Leeds | 2007 to 2012
Information Manager
University of Leeds | 2007 to 2009
Masters Degree (Research) in Data Visualisation
Visualising Data Ltd. | 2010 to date
Freelance Jack of All Trades / Visualisation Mercenary

10.
Visualisation Workflow:
Finding Stories and
Telling Stories

11.
“The aggregation of marginal gains”
Dave Brailsford

12.
1. Establish the
visualisation‟s purpose
and identify key factors
2. Acquire, prepare and 3. Establish editorial focus
explore your data with your subject matter
4. Conceive your
visualisation design
5. Construct your data
visualisation solution

13.
1. Establish the
visualisation‟s
purpose and identify
key factors

14.
What is „Purpose‟?
Trigger Intent
Its reason for existing The intended function and tone
How well is it defined?
Client project (brief)
Internal project (brief)
Self-initiated

15.
Intent: Function
Who does the work?
Designer driven or Reader driven

16.
Intent: Tone
Scienc
Art
e
http://www.hybridtweaks.com/wp-content/uploads/2012/07/valuev-holyfield.jpg

17.
Intent: Tone
Getting [visualisation] right is
much more a science than an
art,
which we can only achieve by
studying human perception.
Stephen Few
http://www.interaction-design.org/encyclopedia/data_visualization_for_human_perception.html

18.
Intent: Tone
I have this fear that we aren’t
feeling enough.
Chris Jordan, TED Talk
http://www.youtube.com/watch?v=f09lQ8Q1iKE&feature=youtu.be&t=5m11s

19.
Intent: Function + Tone
Exploratory (Find Stories) Analytical/Pragmatic
Explanatory (Tell Stories)
Abstract/Emotive

20.
Analytical
+
Exploratory

21.
512 Paths to the White House | New York Times
http://www.nytimes.com/interactive/2012/11/02/us/politics/paths-to-the-white-house.html

22.
Analytical
+
Explanatory

23.
Why Is Her Paycheck Smaller? | New York Times
http://www.nytimes.com/interactive/2009/03/01/business/20090301_WageGap.html
http://www.barackobama.com/jobsrecord

24.
Emotive/Abstract
+
Exploratory

25.
OECD Better Life Index | Moritz Stefaner
http://oecdbetterlifeindex.org/countries/united-kingdom/

26.
Emotive/Abstract
+
Explanatory

27.
What A Hundred Million Calls To 311 Reveal About New York | Pitch Interactive
http://www.wired.com/magazine/2010/11/ff_311_new_york/

28.
Potential Key Factors
The aim? Open, strict, helpful, unhelpful, clarity
Pressures? Timescales, managerial, financial
Format? Static, interactive, video, tools
Setting? Issued report, presented
Technical? Software, hardware, infrastructure
Audience size?
One, group, organisation, outside
Audience type? Domain, captive, general
Resolution? Headlines, detail
Frequency? One-off, regular
Rules? Structure, layout, style, colour
People? Individual, team, the 8 hats…

29.
2. Acquire and
prepare your data

30.
The Hidden Burden
The Hidden Cleverness

31.
80% perspiration,
10% great idea, 10% output
Simon Rogers
The Guardian, „Facts Are Sacred: The Power of Data‟

32.
3. Establishing
editorial focus by
finding stories

33.
Good content reasoners
and presenters are rare,
designers are not.
Edward Tufte
http://adage.com/article/adagestat/edward-tufte-adagestat-q-a/230884/

34.
Finding Stories

35.
Finding Stories is…
Using visualisation techniques to
familiarise, learn about and
discover insights from data

36.
Graphical Literacy

37.
Visual Analysis to Find Stories
Comparisons
– Categorical comparison and proportions
– Ranking: big, small, medium
– Measurements/values: absolutes
– Range and distribution
– Context: Targets, forecasts, averages
– Hierarchical relationships

38.
Visual Analysis to Find Stories:
Comparisons

39.
Visual Analysis to Find Stories
Trends and patterns (or lack of)
– Up and down vs. flat?
– Linear vs. exponential
– Steady vs. fluctuating
– Seasonal vs. random
– Rate of change vs. steepness

40.
Visual Analysis to Find Stories: Trends
https://pbs.twimg.com/media/A8aptCHCAAAWyqx.png:large

41.
Visual Analysis to Find Stories
Relationships
– Outliers
– Intersections
– Correlations
– Connections
– Clusters
– Associations
– Gaps

42.
Visual Analysis to Find Stories:
Relationships

43.
4. Conceive your
visualisation design

44.
Telling [or Framing]
Stories

45.
Telling Stories is…
Identifying and caring for the
reader – taking responsibility to
maximise their potential insight

46.
http://image.yaymicro.com/rz_1210x1210/0/5d9/pile-of-bricks-5d9ac1.jpg

47.
http://yourcolorcoach.files.wordpress.com/2010/11/img_7704.jpg

48.
http://degaryan.blogspot.com/2011/03/introduction.html

49.
The Visualisation Anatomy

50.
Data representation

51.
Showing what we are
trying to say
http://www.storytellingwithdata.com/2012/05/creating-visual-story-questions-to-ask.html

52.
The Ebb and Flow of Movie Box Office Takings | New York Times
http://www.nytimes.com/interactive/2008/02/23/movies/20080223_REVENUE_GRAPHIC.html

53.
We rejected them because they
didn’t do a good job of
answering some of the most
interesting questions... Different
forms do better jobs at
answering different questions.
Amanda Cox (on NYT Stream Graph)
http://www.portfolio.com/views/blogs/odd-numbers/2008/02/26/q-amp-a-anatomy-of-a-graphic

54.
Comparing categories

55.
Assessing hierarchies & part-to-whole relationships

56.
Showing changes over time

57.
Charting connections and relationships

58.
Mapping geo-spatial data

59.
Colour and background

60.
Colour used well can enhance
and clarify a presentation.
Colour used poorly will
obscure, muddle and confuse.
Maureen Stone
http://www.perceptualedge.com/articles/b-eye/choosing_colors.pdf

61.
Confusion…
http://go.bloomberg.com/multimedia/measuring-the-u-s-melting-pot/

62.
OMG
http://www-958.ibm.com/software/data/cognos/manyeyes/visualizations/schools-in-manchester-1821

63.
To represent data values
Colour (Hue)
Colour (Saturation)
http://www.theusrus.de/blog/the-good-the-bad-22012/

64.
To distinguish between categorical items
http://oecdbetterlifeindex.org/countries/united-kingdom/

65.
To help distinguish foreground and background
http://www.flickr.com/photos/walkingsf/6276642489/sizes/l/in/photostream/

66.
To create signals/accents
From “Information Dashboard Design” and http://centerview.corda.com/corda/dashboards/examples/sales/main.dashxm l

67.
Annotation

68.
The annotation layer is the
most important thing we do...
otherwise it’s a case of
here it is, you go figure it out.
Amanda Cox, Graphics Editor, New York Times
http://eyeofestival.com/speaker/amanda-cox/

69.
TEDTalks “Myths about the developing world“ (2006) | Hans Rosling
http://youtu.be/hVimVzgtD6w?t=1m1s

70.
The Growth of Newspapers Across the US | Stanford
http://www.stanford.edu/group/ruralwest/cgi-bin/drupal/visualizations/us_newspapers

71.
The Growth of Newspapers Across the US | Stanford
http://www.stanford.edu/group/ruralwest/cgi-bin/drupal/visualizations/us_newspapers

72.
Arrangement

73.
Consider the placement of every
single visible element in a way
that minimises thinking and
maximises interpretation

74.
Deliberate design
http://www.perceptualedge.com/blog/wp-content/uploads/2012/10/dashboard-competition-winner.png

75.
„Narrative Visualization: Telling Stories with
Data‟, Edward Segel and Jeff Heer
http://vis.stanford.edu/papers/narrative

76.
1. Magazine Style
Dot point map of cholera deaths | Jon Snow
http://www.casa.ucl.ac.uk/martin/msc_gis/map_making_myth_making.pdf

77.
2. Annotated Chart
Why Is Her Paycheck Smaller? | New York Times
http://www.nytimes.com/interactive/2009/03/01/business/20090301_WageGap.html

78.
3. Partitioned Poster
Steroids or not, the pursuit is on | New York Times
http://vis.stanford.edu/images/figures/case-bonds.png

79.
4. Flow Chart
Graphic of Napoleon's March (1869) | Charles Joseph Minard
http://www.edwardtufte.com/tufte/posters

80.
5. Comic Strip
Drought‟s footprint | New York Times
http://www.nytimes.com/interactive/2012/07/20/us/drought-footprint.html

81.
6. Slide Show
Rise of the Megacities | The Guardian
http://www.guardian.co.uk/global-development/interactive/2012/oct/04/rise-of-megacities-interactive

82.
7. Video/Animation
Visualizing how a population grows to 7 billion | NPR
http://www.npr.org/2011/10/31/141816460/visualizing-how-a-population-grows-to-7-billion

83.
Interactivity

84.
Interactive Features and Functions
Variable adjustment – selection
highlighting/brushing, filtering, excluding, sorting
View adjustment – pan, zoom, scale, rotate,
transpose, arrange, tabs
Annotation – hovering/annotate, drop lines
Animation – play, pause, reset, chapter
navigation, grab the slider, show new
data/changed data

85.
Wind map | Fernanda Viegas and Martin Wattenberg
http://hint.fm/wind/

86.
http://www.normzarr.com/2010/05/22/midipad-ipad-iphone-music-app-wireless-touchscreen-software-controller-for-ableton-live-logic-
cubase-nuendo/

87.
5. Construct and
evaluate your data
visualisation solution

88.
http://www.visualisingdata.com/index.php/resources/

89.
Sample Project

90.
Visualizing the London 2012 Olympic
Games we will see the best of the best compete for pride,
This summer,
glory, and, of course, medals at the Olympics. From kilograms
lifted in weightlifting to the number of individual countries
competing to the number of medals won by competing nations - the Olympics
provides a barrage of numbers that are ripe for designers to analyze and
visualize.
We challenge you to use data and design to visualize the Olympics, helping us
understand and enjoy as we watch. For instance, you could create a piece that
contextualizes each country‟s medal count with information about their
population, GDP, and athletic training resources. Or you could illuminate the
results of a particular event or the impact of hosting the London 2012 Olympics
Games on the UK's economy.
“We‟re looking for any data-driven project that
brings new insight, context, or comparison to our

91.
Find stories…

92.
Find stories…

93.
Establish Narrative/Data Questions
Repeat for all relevant sports and events:
• Comparison between patterns for different medals?
• What % improvement in time has there been?
• Which events have improved the most and the least?
• Comparison between progress of men and women?
- Is one gender improving more than the other?
- Any evidence of women getting closer to men?

94.
Tell (or frame) stories…

95.
Tell (or frame) stories…
Data representation
- line chart, dot plots, small
multiples, tables
Colour and background
- gold, silver, bronze, blue, orange
Animation and interaction
- Data/view manipulation
Arrangement
- intro, selections, chart, filters,
extra stats
The annotation layer
- context, annotated detail, stats

96.
Construction

97.
Evaluation
Understanding (10 Points):
How effectively does the visualization communicate?
How well does it help you make sense of this issue?
Originality (5 Points):
Are the approach and design innovative?
Style (5 Points):
Is the visualization aesthetically compelling?

98.
Evaluation
Andy Kirk's “The Pursuit of Faster” also
earned an honorable mention for its
level of data analysis that was
unmatched by most, if not all, of the
challenge entries. Its focused and
thorough narrative further distinguished
the project from the other interactive
entries.

99.
Learning the Craft

100.
http://images.wikia.com/marvel_dc/images/9/93/Adventures_of_Superman_424.jpg | http://www.adobenido.com/blog/wp-content/uploads/2012/01/wonder_woman.jpg

101.
http://collider.com/wp-content/uploads/WarGames-Sheedy-and-Broderick-on-computer.jpg

102.
Multi-disciplinary: Art & Science

103.
The 8 Hats of Data Visualisation
Project
Initiator Journalist Communicator
Manager
Cognitive Design Computer Data
Science Science Science

104.
Craft
Practice, practice, practice – experience is the key
Seek potential projects – paid, curiosity, contests
Learn about yourself – take notes, self critique
Technical skills – push yourself out of comfort zone
Evaluate others – silently or provide reviews
Publish yourself – encourage and digest peer
critique

105.
Theory
Online content – immerse yourself in the community
Books – so many invaluable references and inspirations
Academia – papers, journals
Conferences – within the field and around it
Training/education – look for good training provider…

106.
www.visualisingdata.com
andy@visualisingdata.com
@visualisingdata