data visualisation: art or science?
72hrs of you tube video 
571 new websites 
100m new emails 
277,000 tweets 
.. created every minute
Channel growth
Data vs Visualisation
Where do you start?
Data challenge: quality
Data challenge: understanding
Data challenge: outliers
Thinking about visualisation
Question. Question. Question.
Beware aggregate data 
› Male gender bias in graduate admissions. 
4,321 applicants 
35% admitted 
8,442 applicants 
44% a...
Beware the y-axis bias 
3.154 
3.152 
3.15 
3.148 
3.146 
3.144 
3.142 
3.14 
3.138 
3.136 
March April May June July 
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/
VViissuuaalliissaattiioonnOovveerrllooaadd
Remember the different channels 
A step too far…
What we 
speak about 
becomes 
the house 
we live in 
- Hafiz
Key takeaways 
› Visualisation is both art and science. 
› The visualisation should inform not just be pretty 
› Lots of p...
Thank you
Visualisation: science or art?
Visualisation: science or art?
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Visualisation: science or art?

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How should you approach data visualisation today? Is there a standardised process or approach or is it still art?

Includes tips as to how to approach starting out with visualisation.

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  • We are going to look at data visualisation and how you should approach it. How much of it is still art and how much of it is science?

    Before we do lets look at the landscape
  • It is pretty clear from the slide – there is a huge amount of content being generated every minute, let alone every day.

    But there is also a secondary message to this slide, a subtle reminder..

    at the sizes involved it is often difficult to visualise well..

    sometimes words work just as well if not better.
  • So given I am talking about visualisation for marketers, you don’t need me to tell you that there is the increasing number of channels being adopted.

    People (except us marketers of course!) do not think about channels, and yet at the same time there is now an expectation that everything is joined up.

    So its important to be able to see across channels. With the deluge of data coming in though, being able to do this quickly and easy is critical.

    Recent research by IBM found that more than 70% of CMOs are put off from doing anything with their data.
  • There are multiple ways to visualise data – and some of the tools available today make it easier.

    The point of visualization is to make large amounts of data approachable, this is the science bit so we can apply our own pattern detection - i.e. our brain - to draw insights from it. This needs some science and some art to get right.

    Large amounts of tables generally do not lead to insight, so representing it visually helps.

    To do so, we need to access all of the potential relationships of the data elements. That is often difficult if not impossible so understanding what you do have is critical.

    For example, we often get hung up on channel attribution, but all we might have is view data, click data, time of campaign. That’s enough to visualise the campaign and see how each channel is interacting. But it is not everything, was there something happening in the real world that had an impact? Maybe the weather, maybe the end of the world cup? Of course, you can get too hung up on what you don’t have, even with all this data at your finger tips. You just have to get on with it and understand what you lack.

    So with all that said, context is the source of insight. It is the ART. The data alone can only provide the patterns.
  • Start slowly.. Understand what you know and what might work at each of the touchpoints with your customers or prospects?

    Collect the data one channel at a time and then layer in new data one at a time, and see how the picture changes. Look at it from different views. Once you have some insight, test it and look for any impact.

    Whether that is a 1-1 message via social or a triggered message via email or mobile apps.
  • Now that sounds incredibly easy – and you would be correct.

    More often than not, this is what happens though (usually without the test), and once the insight is found, it is plonked straight into a dashboard never to be looked at again.

    So if you havent got the hint, this is really what you have been looking at.
  • There is a whole bunch of work that needs to be done with your data before you go playing with it. Otherwise you end up thinking you have real insight, and then when you test it, it doesn’t actually work.

    You’re left with egg on your face.

    So what went wrong here?
  • Is it entered manually?
    What are you using the data for?
    Can you clean the data? Infer meaning from the data so that it is cleaned.
    Value jeopardized if the data is not accurate or timely.

    This process takes longer than expected – but it is critical.
  • How certain are you that the data actually is what it says it is?

    if the data comes from social media content, you need to know who the user is in a general sense – such as a customer using a particular set of products – and understand what it is you’re trying to visualize out of the data. Without some sort of context, visualization tools are likely to be of less value to the user.

    One example we have experienced is when trying to understand a person’s location. We had a scenario where when we analysed location using IP data, their location looked as if they were mostly in North America, when in fact their location was in the Middle East. People were using their office VPN to connect to the internet and so their supposed location was wrong. You have to understand how are you interpreting your data.

    Of course, sometimes you don’t understand the results, and so the only option is to test the results and dig deeper. Ask your colleagues. Could something external to you be having an impact?
  • Use data responsibly.

    Be clear with your customers as to how you intend to use it.

    Example of FB and OKCupid.
  • Visualisation: science or art?

    1. 1. data visualisation: art or science?
    2. 2. 72hrs of you tube video 571 new websites 100m new emails 277,000 tweets .. created every minute
    3. 3. Channel growth
    4. 4. Data vs Visualisation
    5. 5. Where do you start?
    6. 6. Data challenge: quality
    7. 7. Data challenge: understanding
    8. 8. Data challenge: outliers
    9. 9. Thinking about visualisation
    10. 10. Question. Question. Question.
    11. 11. Beware aggregate data › Male gender bias in graduate admissions. 4,321 applicants 35% admitted 8,442 applicants 44% admited › 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 Source: Science, Bickel et al (1975) More info: https://www.boundless.com/statistics/statistics-in-practice/observational-studies/sex-bias-in-graduate-admissions/
    12. 12. Beware the y-axis bias 3.154 3.152 3.15 3.148 3.146 3.144 3.142 3.14 3.138 3.136 March April May June July 3.5 3 2.5 2 1.5 1 0.5 0 March April May June July
    13. 13. Going beyond the bar chart Source: Nate Agrin and Nick Rabinowitz
    14. 14. Going beyond the bar chart
    15. 15. Timelines and geo Source: http://hint.fm/wind/
    16. 16. VViissuuaalliissaattiioonnOovveerrllooaadd
    17. 17. Remember the different channels A step too far…
    18. 18. What we speak about becomes the house we live in - Hafiz
    19. 19. Key takeaways › 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
    20. 20. Thank you

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