Jose Berengueres - Introduction to Data Visualization & Storytelling: A Guide For The Data Scientist

It was May 2018 when I received an email from Kaggle.com , a data science community where people all over the world compete in data modelling challenges. Every year since 2017, Kaggle has surveyed its users and this year they decided to organize a storytelling competition and offer cash prizes for the best survey visualization. I was curious to see what people had submitted so far and sifted through the entries. I was impressed at how much time and effort had been put into analyzing the data. Some charts required laborious data wrangling, others crafty SQL inner joins, and some Python sorcery. And yet, the charts were not doing justice to the compelling findings and the stories that could be told. No doubt, some were creative, extensive in length, exhaustive in the exploration. However, there were also many unimpressive charts. Why was so much IQ not producing more enticing visualizations?

80% of the data scientists graduating today will do so without having received any formal education on storytelling , and 60% of them place data visualization at the bottom of their priority list of skills to have. In addition, the fact that most data science is taught at STEM schools, not Art schools, does not help either. (Really, STEM schools should aspire to be STEAM schools, to include Art). In order to improve the quality of data visualization, there must be a change in how data scientists are trained along with a mind shift regarding the imperative of good story telling. However, to make effective visualizations, art sensibility is not all that is needed.

A second cause, is that the gallery of default chart styles of Microsoft Excel is unlikely to match what is required to tell the story .

A third cause, is that to produce a meaningful visualization we need to know why we are visualizing. To get to the why, its important to explore the story that needs to be told and be able to make it relevant.

To succeed at transforming data into a compelling and relevant story, it helps to connect the data to a context, metaphor or mental framework (frameworks from Economics, Art, and Sociology are particularly useful). In order to make these connections, a cross-functional education is necessary. Unfortunately, this is not the norm resulting in loads of very interesting data failing to become useful knowledge. To address this, in the following pages you will find a set of principles by example that I wish I had learned in grad school.

Happy visualizations!

Jose Berengueres

Stockholm, Oct 25th 2019