Think about the worst data visualizations you have ever seen. Perhaps they were confusing, ugly, garish, or misleading. A visual can fail for lots of reasons but one of the most common factors which crops up time and time again is a weak narrative, or a lack of narrative altogether.
How does this happen? And why is ignoring narrative such a danger for visualizers? Read on to find out.
How Does Narrative Get Lost?
As data visualizers, we tend to adopt a considered approach to the data we use. What’s more, it is unlikely we would ever deploy a piece of visualization without a purpose or objective in mind. So, if we are so tuned into the aims and drivers of our DV work, why does the all important narrative sometimes fall by the wayside?
Babson College professor and digital strategy expert Tom Davenport has identified four factors which often cause narrative to become lost or obscured during the creation of a visual.
Firstly, Professor Davenport argues that many data visualizers approach the discipline from a purely mathematical or data-centric standpoint. This is understandable, as these are professionals who are used to working with and interpreting large data structures. A sharp, analytical mind is required to transform this data into a workable visualization. However, visualizers must be able to temper this approach with what Davenport describes as a ‘literary’ bent; a commitment to finding a relatable narrative within the stacks of numbers.
Secondly, Davenport states in his article that education is still geared towards the analytical and statistical side of things, with little weight given to narrative building and data storytelling.
Davenport argues that a shift in this ratio is required if we are to produce the next generation of sharp visualizers.The third area identified in Davenport’s article is the inherent prejudice that many data scientists harbor towards narrative building. “Capable quantitative analysts may justifiably argue that many people can tell good stories, but relatively few can run a logistical regression model with heteroskedasticity corrections,” he writes, explaining that many data experts consider narrative building to be an unworthy use of their talents and a waste of their skills.
Finally, Davenport highlights time as a factor. Building an effective narrative is an incredibly time-consuming process, and will require several sessions of writing and re-drafting, much in the same manner as a writer crafting a short story will have to go over his or her work again and again until it is perfect. Davenport argues that visualizers understand that this makes their work far more effective, but…
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