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 are all too often unwilling to put in such a time commitment when it comes to constructing a compelling thread of narrative.

Perhaps the key takeaway from a very interesting article by Professor Davenport is that a change in mindset is required. It is vital for us not to see narrative building as the black sheep of the data family, the lazy second cousin who still hasn’t left his parents’ home, but instead for us to look upon storytelling as an integral component of the data journey.

Recognizable Cultural Signifiers

The term ‘pareidolia’ might not be instantly familiar to us but the phenomenon it describes certainly is. Pareidolia refers to our propensity, as humans, to recognize faces and other meaningful symbols in otherwise random patterns. This somewhat unnerving psychological concept has been blamed for everything from the famous Belmez Faces (although a straight-up hoax is more likely in this case) to depictions of Jesus turning up in grilled cheese sandwiches.

But pareidolia is not merely a coincidence. It is rooted in psychology and the way in which humans learn to recognize the faces of their parents and other family members from an early age. From this we can make a simple connection; the human brain is hardwired to reach meaningful conclusions when presented with images, even if these conclusions are not necessarily valid.

So what does this mean for data visualization? Well, if you neglect to clearly define your narrative when creating a visualization or if you simply deploy imagery for its aesthetic appeal rather than for any meaningful purpose, your users will interpret it subconsciously, drawing their own conclusions.

Intended meaning can be clouded and confused even further when we factor in culturally-based signifiers. Using different pieces of imagery in our data visualizations can produce different effects. For example, in western cultures, a dark red color may imply passion, danger and warning, while in the Far East, red signifies good luck. As the appeal of visualization becomes increasingly globalized, this is something which needs to be taken into account.

Shape psychology is another element which must be considered. This area of psychology is thought to be almost universal and is related more to the fundamental ways in which humans engage with the physicality of the structures around them. For example, soft curves and circles have a benign and calming effect on an audience, while sharp lines and angles are associated with harsher concepts.

Of course, we should give data vis consumers the respect that is due to them. Your audience is likely to be intelligent and will have the necessary cognitive tools to decode the visualization and the narrative you present to them. However, if your narrative is unclear, the cultural and psychological signifiers will come into play and will drown your message beneath layer upon layer of semantic distortion.

Don’t Turn Away from the Story

Even the most hardened advocate of data and deep insight, even the most committed follower of numbers and statistics, needs to be able to take a step back and to embrace the power of narrative.

It is important to remember the ethos which drives all data visualization; the idea of communicating difficult concepts and revelatory insight on a mass scale. If your users could grasp the concept that you set out to explain simply by looking at a table of figures, they would. Instead, they come from a wide range of different specialities and so need an expert to guide them through the data. This is data visualization. The narrative represents the thread which draws your users through, towards a conclusion.

Enabling personal interpretations of the figures at hand and always remaining true to the raw data; these are both solid tenets on which the discipline of data visualization is built. However, without narrative, data visualization cannot achieve its rhetorical aims and will ultimately fail.

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