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Sarah Olesen

Presentation to IPAA Canberra Evaluation Forum

22 September 2016

The value of data visualisation

research write edit design

A clip from the ABC’s ‘Checkout’ about the perils of pie

charts and the importance of accuracy for integrity:

http://iview.abc.net.au/programs/checkout/LE1502H0

10S00

*Start at 24:44 minutes

into the show

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Confessions of a data addict enthusiast

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Open data

DataEvidence-

based policy

Big data

AccessibilityEvidence-

based policy

The data landscape

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Trifecta: Engaging, clear, accurate

Presenting data in a visual way to draw readers in and

make messages clear.

Data visualisations include:

• graphs

• tables

• maps

• infographics.

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60Outcome 1

Outcome 2

Outcome 3Outcome 4

Outcome 5

2008 2010 2012

It might be visually interesting, but is this the most intuitive way to show change over time?

Be wary that connected lines imply related and continuous measures

2.50

2.60

2.70

2.80

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2008 2009 2010 2011 2012 2013 2014 2015

Incidence of Condition A

0.00

0.50

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2008 2009 2010 2011 2012 2013 2014 2015

Incidence of Condition A

Always start bars from zero

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Why visualise?

1. To make information and findings clear(er)

2. To communicate your message, or ‘data

story’

3. To make data meaningful to readers.

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When to visualise?

Making findings clear (but still accurate), when

you:

• have a lot of data and want to draw out the

key and/or overall findings

– over time

– over levels or clusters

– across measures.

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When to visualise?

Making findings clear (but still accurate), when:

• have a lot of data

• the overall ‘story’ or pattern(s) in the data is

more important than individual data points.

Image from: Marland et al. 2007 (https://commons.wikimedia.org/wiki/File:Global_Carbon_Emissions.svg)

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What to visualise?

Finding your data ‘story’

Ask yourself:

• what pattern(s) is the data showing?

• what message(s) do you want to tell your

audience?*

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What to visualise?

Finding your data ‘story’

Ask yourself:

• who/what has changed? (who/what hasn’t?)

• who/what is different?

• what did you find that you weren’t expecting?

• what do you want readers to do about it?

• how can I incorporate data in the overall story of the publication?

Figure 1: Expenditure by district

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North South East West

Before After

What’s interesting?

Figure 1: Men and women’s responses to satisfaction survey

020406080100

Men

0 20 40 60 80 100

Question 1

Question 2

Question 3

Question 4

Question 5

Question 6

Question 7

Question 8

Women

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Common data patterns to visualise

• Comparing magnitude (one or more groups)

• Change over time

• Part-to-whole

• Distribution (once, or over time)

• Deviation

• Associations

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Match patterns to graph types

research write edit design

https://www.sciencestyle.com.au/

research write edit design

Match patterns to graphs

..and/or other visuals, including:

• tables – when individual data points are important

• maps – when data are spatially located and it helps

to see the spatial relationships between data

• data-driven infographics – to summarise, for text-

based data

Example map: Uptake of screening services for condition x, Victorian local

geographic areas

Image from: http://www.chiefscientist.gov.au/2016/07/science-and-maths-in-australian-secondary-schools-datasheet/

http://www.chiefscientist.gov.au/2016/07/science-and-maths-in-australian-secondary-schools-datasheet/

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Make it meaningful

Choose the right visual for your audience

• Know your data and data relationship

• Know your audience

• Know your medium

- Digital/web or print?- Interactive or static?- Accessible? - Which devices?

Policy-makers, decision-makers and managers, public..?

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Make it meaningful

• Highlight the reader’s location, job, age-group...

• Use interactive data visualisations let readers

personalise their ‘data story’ by focusing on, or

filtering, the data within the visualisation.

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Interactive data visualisation

To interact with this visual go to:https://public.tableau.com/views/LGAScreeningprogramexample/Dashboard1?:embed=y&:display_count=yes&:toolbar=no

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Function over fashion

• Keep it simple to keep it clear.

• Reduce clutter (don’t ‘double up’ on data).

• Legibility (font, size, direction).

• Think about data:ink (Tufte 1983).

• Avoid rainbows.

• Avoid bells and whistles (3D effects, shadows) .

• Consider accessibility.

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research write edit design

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Don’ts

• Don’t use visuals for the sake of visuals.

• Don’t be too complex (eg dual axes).

• Don’t be misleading.

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Dos

• Do be intuitive in your

choice of graph type.

• Do be clear and

accurate with statistics.

• Do be consistent.

• Less is often more, but

more is often better

than one complex

visual.

• Do use scientific

conventions.

• Always reference your

source (and always

license your own data).

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Take away

• Use data visualisations to summarise and show

patterns and key messages.

• Prioritise clarity and accuracy.

• Know your readers – they will expect:

– accurate

– clear comparisons

– clear outcomes (positive or negative)

– bottom-line messages.

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Resources

Good books on data visualisation

• Stephen Few’s ‘Show me the numbers’.

• Edward Tufte’s ‘The visual display of quantitative information’.

• S. Evergreen’s ‘Presenting data effectively’.

Check out the Australian manual of scientific style (particularly

sections on ‘Showing’). Find it here:

https://www.sciencestyle.com.au/

research write edit design

research write edit design

www.biotext.com.au

Sarah Olesen

(02) 6282 2280

sarah.olesen@biotext.com.au

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