The old adage of “a picture is worth a thousand words” could not be more apt, or more evident, than when it comes to data visualisation. And with research proving that the human brain can process images that the eye sees for as little as 13 milliseconds, data storytellers are playing an increasingly critical role in bringing across key insights, visually, as effectively as possible.
Data storytelling is a methodology for communicating information, tailored to a specific audience, with a compelling narrative. It merges data science, visualisations, and the concept of a narrative to provide one of the most effective ways of sharing business information and driving outcomes.
When it comes to data, the reality is that data preparation on its own holds little value beyond those working closely with it. To unlock all its potential, a company requires a data storyteller that understands not only the data itself (science) but can pull it together visually (visualisation) to tell an important story (narrative) and help guide organisational strategy and decisions.
Data evolution
Anyone who follows my blog will know that the data visualisation topic is one I am rather passionate about. And so, when I came across this insightful article on the evolution of a data analyst to a data storyteller in three steps, I immediately wanted to share the insights and my take.
The author of this industry piece goes on to explain how to improve data visualisations using a simple procedure in Matplotlib. And while you can read the mechanics of this to do this for yourself, there are a few fascinating insights to come from the process involved.
The critical point is that visualisations are the key mechanisms to translate complex data outcomes into understandable business stories. For its part, visualisation becomes the fundamental tool required to enable data-related storytelling. According to the article, if a data analyst or scientist cannot visualise the results, then they do not know the results. The author writes that it takes a passionate and skilled data scientist to transform basic visualisations (which just about anyone can create) into a story that managers and customers will understand and get excited about.
This all comes down to three things noted in the article – adding information; reducing information; and emphasising information. Much of this revolves around improving the signal to noise ratio. This describes the amount of valuable information compared to the amount of unnecessary information. The article goes on to explain how to highlight all the important information and remove everything that does not add any real value.
At its core, this is what storytelling does. Nobody wants to read a novel that is poorly written or has weak characters and plot holes. Similarly, data storytellers must create compelling narratives where the ‘readers’ become passionate about what they are seeing.
Insights beyond
In his 1983 book, ‘The Visual Display of Quantitative Data’, Edward Tufte introduced the concept of Data-Ink ratio. Tufte refers to data-ink as the non-erasable ink used for the presentation of data. If data-ink would be removed from the image, the graphic would lose the content. Non-data-ink is accordingly the ink that does not transport the information, but it is used for scales, labels, and edges. The Data-Ink ratio is the proportion of Ink that is used to present actual data compared to the total amount of ink (or pixels) used in the entire display. In the book, he explains how to get more data (story) onto the graph and less graphic distractions.
Furthermore, Stephen Few has also written extensively on visual business intelligence, or rather, data visualisation as we know and love it today. I have in the past attended one of his excellent courses and have worked my way through some of his material – which is fantastic. In his most recent blog, he addresses the data storytelling attempt by some to compare the effects of the COVID-19 pandemic around the world. While it provides a fascinating read on how to make the data tell a more insightful story, the examples he creates highlights that it is not always about making things more complicated but adopting a simpler approach to provide more valid and useful ways to represent data.
Data visualisation and storytelling are fast becoming critical components for any business who wants to get a better understanding of the data at its disposal. In fact, I believe that this will be a vital resource for any organisation to create differentiation in this digitally driven world.
