data visualisation: art or science?

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Channel growth

Data vs Visualisation

Where do you start?

Data challenge: quality

Data challenge: understanding

Data challenge: outliers

Thinking about visualisation

Question. Question. Question.

› Male gender bias in graduate admissions.

› At department level: most departments had a small but statistically significant bias in favor of women

› Situation:∙ Women were applying to competitive departments

with low rates of admission ∙ Men tended to apply to less-competitive departments

with high rates of admission

Beware aggregate data

Source: Science, Bickel et al (1975)More info: https://www.boundless.com/statistics/statistics-in-practice/observational-studies/sex-bias-in-graduate-admissions/

4,321 applicants35% admitted

8,442 applicants44% admited

Beware the y-axis bias

March April May June July3.1363.1383.14

3.1423.1443.1463.1483.15

3.1523.154

March April May June July0

0.5

1

1.5

2

2.5

3

3.5

Going beyond the bar chart

Source: Nate Agrin and Nick Rabinowitz

Going beyond the bar chart

Timelines and geo

Source: http://hint.fm/wind/

Visualisation OverloadVisualisation overload

Remember the different channelsA step too far…

What we speak aboutbecomes the housewe live in- Hafiz

› Visualisation is both art and science.› The visualisation should inform not just be pretty› Lots of potential insight – start small with the most

important first› Don’t forget to understand and clean the data › Look at your outliers for potential new opportunities› Understand what you are trying to takeaway from the data

∙ Let that guide your choice of visualisation› Shy away from dashboard overload!› Don’t forget more often than not in marketing, your data

involves people

Key takeaways

Thank you