Analytics and Data Stories: From Fantasy to Non-Fiction

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by Kimberly Nevala

During our recent session on data storytelling a question was posed: Can storytelling be applied at the first stage of the analytic process? Specifically, when a working hypothesis is initially formed and before hard data is well in hand.

The answer is yes. Storytelling can and should be applied throughout the analytic process. Applied properly, story can first put meat on the bones of a hypothesis. Then provide context for data interpretation. And, ultimately, deliver the evidence in a manner that promotes belief and action.

The key to successfully leveraging story throughout the analytic process is simple. Recognizing that your story will evolve. Progressing, as it does, through different genres – from fantasy to nonfiction.

Let’s start at the beginning. The kernel of any hypothesis is a story. Typically cast as a mystery or drama. What is happening or will happen next? Why? Who done it? How? But at this juncture and until proven otherwise your story is fiction. Perhaps well-intuited fiction. But fiction nonetheless. Label it accordingly.

Equally imperative? Not becoming overly invested in your thesis. If the very thought of following Nancy Duarte’s advice and “murdering your darlings [ideas]” is abhorrent, a quick timeout may be in order. Because the primary goal at this point in the process is exactly that. To attempt, with equal vigor, to both prove and disprove your hypothesis.

As analytic discovery and modeling progresses, data will emerge. As it does, a new perspective on your story may be revealed. Or not. (If the data came first, your data storytelling journey may start here.) At this juncture your story begins the transition from fantasy to non-fiction.

What’s important here? Being unafraid to start over. The data may not support your initial hypothesis. If so, the objective is not to use story to justify the difference. Yes, a good story can be used to subvert the facts. But that is certainly not the goal. The right story may be an explanation of why the fantasy was just that. Contrasted with the reality exposed by the facts.

Either way, the key at this stage is simple: don’t get overly dramatic. Regardless of whether your hypothesis was right or wrong. Whether or not the data is contrary to prevailing opinions or practices. Data storytelling – at least in the corporate environment – isn’t about divine tragedy or comedy. Rather, the goal of data storytelling is to promote evidence-based decision making. Not to undermine it. The most important emotion created by story is empathy. Enabling an audience to view the facts from what may be a new and, in many cases, slightly uncomfortable perspective.

It follows then that the narrator’s role is to ensure the punch line doesn’t eclipse or subvert the message. Consider the following example of a “story that took too well” shared by an erstwhile colleague. Hoping to raise awareness of their increasingly complicated operating environment, his team created some arresting visuals depicting the messy mass. Then took to the road. The visual and, apparently, the narrative accompanying it were well received. But not in the intended manner. Within the business, a new rallying cry became “does this make the environment simpler?” Which was not, in fact, the intended message. Leading, as you might imagine, to some unintended results.

The moral of the story? A well-crafted story sticks. Reiterating the need for deliberation when crafting a narrative. Or an arresting visualization for that matter. Need help walking the walk? Check out these tips from my colleague and veteran storyteller Bree Baich.

To stretch this story to the metaphorical limit, retrospective BI reports and dashboards read like a memoir. They are grounded in historical fact. But are often subject to interpretation colored by personal memory and perspective. Yes, application of annotation and integrated narratives will mitigate this effect in the future. But, like any other story discussed here, such interpretation cannot (and should not) be eliminated entirely.

Used appropriately, story can be a critical part of your analytic toolkit. But data storytelling is not a silver bullet. Nor is it easy.

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