© 2012 Visualising Data Ltd 1

Visualisation’s Duality:

Finding Stories and

Showing Stories

Andy Kirk

www.visualisingdata.com

© 2012 Visualising Data Ltd 2

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

TriggerIts reason for existing

How well is it defined?

IntentThe 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 (Find Stories)

Explanatory (S

how Storie

s)

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