The pitfalls of data storytelling: why you can’t always rely on a narrative in analytics

Storytelling is fast becoming one of the hottest terms in the world of data analytics. Getting insights from data is no longer a case of looking at one static picture or graph and basing your business decisions on that information. Instead, people want to know the stories behind the data that’s presented to them – why that’s the case and how it came to be so.

And yet, with our data telling us stories, we’re suddenly placed in a position where these stories can take on different narratives and narrative forms. With any storytelling, the narrative has a habit of telling the most compelling story and this is no different with data – we’re looking for insights and want to find those that will help us take action, but how much can we rely on this narrative?

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Outside of the workplace, newspapers, books, television programmes, photographs and films all play a big part in our lives and all are trying to tell us different stories. They provide us with different versions of what has happened and when.

Sometimes these stories are based in fact and sometimes they’re fiction, but all are told through certain viewpoints and narratives. It might be that that narrative is told singularly, from one person’s viewpoint, or perhaps we receive multiple viewpoints on a story instead.

In many cases, we find ourselves trustingly believing the narrative that is being presented to us – even if it’s a singular opinion from one character or viewpoint. But how reliable should we find the narratives we’re given? Should we take all narratives at face value?

Throughout history, there have been a number of different unreliable narratives – some are more obvious to question, take for instance famous literary characters such as Holden Caulfield from J.D. Salinger’s Catcher in the Rye, who narrates to us even after claiming he’s ‘the most terrific liar you ever saw in your life’. Or in the recent Hollywood blockbuster, Gone Girl, where the two main characters tell completely opposing stories, so you know at least one of them is false.

But, just because narratives might seem convincing, it’s always important to question them. Take historians for instance, still to this day and after a burial more than 500 years after his death, they’re questioning outdated, hindsight-filled accounts of King Richard III and the murders of the Princes in the Tower.

After all, historians have been trained to never consume a narrative unquestioningly – and neither should we.

In some situations, an unreliable narrative could be on purpose because the narrator has their own agenda and reasons for presenting information unreliably – telling us the facts they’ve decided they want us to know, but conveniently omitting things they’d rather we didn’t.

On the other hand, it could simply be because our brains aren’t wired to store every single piece of information to give a comprehensive narrative.

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Take the Ebbinghaus curve of forgetting for instance, sometimes we, quite simply, over time, start to tell different narratives to ourselves, gradually forgetting pieces of information we subconsciously consider irrelevant and subsequently amend what happened in our brain.

It might seem to us like the narrative we’re telling is exactly the way events occurred, but we could unintentionally be missing out important information.

And yet, the untold story often, to the audience, could be really important and exactly what we want to know. What if we’re reading about a murder case and the narrator is the murderer themselves, trying to cover their backs? Or what if, because fifty years have passed because of the event, the narrator has forgotten someone saying something that could be incriminating?

In any situation, the narrator may not know they’re being unreliable, but rather, they’re just telling you as much as they can from their point of view, working from within their frame of reference.

So why is all of this relevant in the world of data and business analytics? Well, we’re constantly being provided with visualisations in the workplace that are supposed to inform us how a situation went, whether that’s the sales figures for the past quarter or the levels of staff absence over the past couple of months.

Sometimes these visualisations are beautiful, sometimes they are bar charts and graphs, but ultimately, they may be unreliable narratives – they’re one-dimensional, non-interactive, and only tell one side of the story.

More is needed to garner an insight into what you’re seeing is really the whole truth or not. After all, any narrative has an implied reader and that might not necessarily be the person looking at the information.

Take PowerPoint for instance. It tells you a story, yes, but it’s dead, static. The story needs to be part of the flow of analysis, within the same environment. We all need to think critically when presented with a data story or series of visualisations.

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It’s only through doing so that we ask questions that then lead to a story we can agree on, but the cycle then goes on.

Data analysis needs to be about engagement and participation and I’d urge any business looking at a data analysis project to bear this in mind. All stories need to be unpicked, people need to debate, and they need to be questioned on reliability!

People might not be trying to trick you with their narrative, but you could still end up looking like a fool if you blindly assume what they’re telling you is completely correct.

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Ben Rossi

Ben was Vitesse Media's editorial director, leading content creation and editorial strategy across all Vitesse products, including its market-leading B2B and consumer magazines, websites, research and...

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