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Visualisation’s Duality:
Finding Stories and
Showing Stories
Andy Kirk
www.visualisingdata.com

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Design architect/consultant
Trainer

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Author
The real craft behind data
visualisation design is being able
to rationalise choices
What to show | How to show it

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1. Establish the
visualisation’s
purpose and identify
key factors
What is ‘Purpose’?
Client project (brief)
Internal project (brief)
Self-initiated
Trigger
Its reason for existing
How well is it defined?
Intent
The intended tone and function

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How important is accuracy
compared to aesthetics?
Read data vs Feel data
Precision vs Beauty
Pragmatism vs Emotion
Intent: Tone
Who does the work to surface
the insights?
Find or Show
Reader or Designer
Explore or Explain
Intent: Function

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Analytical/Pragmatic
Abstract/Emotive
Exploratory(FindStories)
Explanatory(ShowStories)
Analytical | Exploratory

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Analytical | Explanatory
Emotive | Exploratory

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Emotive | Explanatory
The brief? Open, strict, helpful, unhelpful, clarity
Pressures? Timescales, managerial, financial
Format? Static, interactive, video, tools
Setting? Issued, presented, instant, prolonged
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…
Potential key factors

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2. Acquire and
prepare your data
Acquisition
Examination
Transform for quality
The hidden burden…

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Transform for analysis
Consolidation
Visual Analysis
The hidden cleverness…
Using visualisation techniques to
familiarise, learn about and
discover insights from data
Requires curiosity and
graphical literacy
Visual analysis

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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
Graphical literacy
0
10
20
30
40
50
60
70
80
90
Graphical literacy

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Relationships
– Outliers
– Intersections
– Correlations
– Connections
– Clusters
– Associations
– Gaps
Graphical literacy
Graphical literacy

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3. Establishing
editorial focus by
finding stories
Good content reasoners
and presenters are rare,
designers are not.
Edward Tufte

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What questions do you have
about this data?
What questions do you want
readers to be able to answer
about this data?

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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)

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4. Conceive your
visualisation design
specification
1. Data representation
The 5 layers of a visualisation

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What are we trying to say
with what we are showing?
Which chart?
1. Consistency with purpose
2. Choose the correct visualisation method
3. Effectiveness of visual analysis techniques
4. Consider physical properties of your data
5. Create the appropriate metaphor
Data representation ingredients

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Comparing categories
Assessing hierarchies & part-to-whole relationships

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Showing changes over time
Charting connections and relationships

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Mapping spatial data
2. Colour
The 5 layers of a visualisation

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Colour used well can enhance
and clarify a presentation.
Colour used poorly will
obscure, muddle and confuse.
Maureen Stone
Colour (Hue)
Represent data values
Colour
(Saturation)

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Distinguish between categorical items
Accentuate data

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Exploit visual language
3. Interactivity
The 5 layers of a visualisation

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Immersive interactivity
Details on demand

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Potential for animation
4. Annotation
The 5 layers of a visualisation

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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
Layers of user assistance

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Layers of user insight
5. Arrangement
The 5 layers of a visualisation

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Consider the placement of
every single visible element in a
way that minimises thinking and
maximises interpretation
Size, sequence, position, grouping, orientation…

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5. Construct and
launch your data
visualisation solution

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