Data visualization – Overwhelming at first, sometimes disheartening but above all exciting
I’m thrilled to say that I recently started my expedition in data visualization. With a background in health, I’m not a data wizard or techie person but after stumbling into tableau software and having a feel of what I could do with few clicks of a button, I developed a crush for data visualization and storytelling through maps, lines and bar charts.
For a beginner that does not work with data on a daily basis, the process of getting hold of data can itself be overwhelming, as it can sometimes be a little disheartening when you go online and see all those incredible visualizations done by experts and Zen masters using some wild techniques, making you think “Wow, I will never be that good!”
However one should not let that deter the enthusiasm and willingness to progress. After all the community of tableau fans is extensive and with a keen interest for sharing knowledge and exchange technical tips through blogs, forums or even twitter.
Additionally, the reality is that we are surrounded by data which can be communicated through visual objects. Only last week several newspapers in the UK were writing about a tooth fairy survey carried out across the country. Evidently the topic is a light hearted one, with no rigor or scientific precision but nonetheless I thought it would be a fun and good way to gather some data, prepared it and finally create a colourful dashboard with some insights that understandably have to be taken with a pinch of salt – 1000 people were surveyed but no details on how the survey was carried out, which methodology was used or if the sample was representative of the population were disclosed;
Also when researching for data in the USA on that same subject I was surprise that one of the Tooth fairy pools was being presented as a positive economic indicator with the idea that the Tooth Fairy becomes more generous arm in arm with households getting raises and a surging stock market, so the indication was that the economy was improving because the tooth fairy was being more generous this year. On the other hand, another poll declared that the tooth fairy was indeed giving less money to the American kids.
Why the disparity between the two surveys then?
Amongst many possible reasons for such disparity, are the outliers in form of households that pay megabucks for baby teeth, as well as the fact that in both surveys a large portion of respondents admitted that the amount of cash one had on hand had a big influence on how much the tooth fairy would leave under the pillow.
And just like that, I had enough data to ‘play with’. I was able to create a dashboard comparing two countries with surveys with approximately similar size samples and other where I compared the two tooth fairy polls in the USA.
I could still draw some insights from the data, such as: which part of the country was getting more money or where that money was being spent, likewise there was correlation between the tooth fairy gender and the monetary generosity. But most importantly the main insight and the most obvious one was the need to critically analyse my own dataset.
If as me you are starting your journey in this field, just have fun, use the data around you, be patient, spend time behind the mouse, read, see other visualizations and above all be excited about what you can do. The learning curve has just begun.
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