1. We need insight, not interpretation.
In a recent Fast Company article, What Killed the Infographic, writer Mark Wilson argues, “A dirty little secret about data visualization is that it doesn’t always provide as much insight into large datasets as you might hope.”
It’s not that data visualizations are useless, on the contrary, they can provide extraordinary insight locked in spreadsheets and databases. However the challenge is when they are used as the sole means of understanding data, particularly for the everyday decision-maker who may lack the skills or time to interpret and explain these often complex displays.
What if, alongside that visualization, you had an accompanying narrative to explain the story behind the graphic or chart– a few sentences highlighting what is most interesting and important? By integrating Advanced Natural Language Generation (Advanced NLG) into your existing viz platform, you have the ability to gain immediate insight from your data through intelligent, accompanying stories, without the need to interpret and explain the results. You can leave that task to the machine.
2. We need answers, not more questions.
Unfortunately searching for answers in our big data reality often translates to opening up Pandora’s Box. We may find the nugget of insight we were looking for, alongside of multiple additional questions we never even thought to ask. This is often the case with the output from visualization tools. We unearth patterns and anomalies not initially seen when analyzing a spreadsheet but not answers to why these patterns and anomalies exist.
Wilson quotes multiple data visualization experts in his article to articulate the current challenges with assuming visualizations will provide the answer.
‘To me, I use the human genome project as a mental model here. The idea was once we mapped the genome, we’d unlock the doors to all of these things, we’d cure diseases, find new medicines,’ the data artist Jer Thorp says. ‘It was going to change things. But mapping the genome taught us how little we know. I think what we need to understand with big data is that the same thing can happen. Big data doesn’t only lead to answers it also leads to questions.’ In other words, the very medium of data-rich infographics might not be right for general consumers.
For these general consumers, what if instead of prompting more questions, they could receive the answers they search for in easy-to-understand language? Whereas visualizations can lead to future questioning or operate as black boxes which don’t expand on reasoning, dynamic and comprehensive narratives can deliver relevant explanations based on the intended audience’s needs.
3. We need an umbrella, not a weather radar.
Wilson concludes his argument with a simple but powerful metaphor. “Even Nicholas Felton, who gained cult status in the data visualization world for mapping every minute detail of his life, questions the usefulness of many visualizations… Instead of data visualization, Felton imagines a future built upon pure insight. No one needs to see a weather radar, he contends, when all you really want to know is whether or not you need an umbrella.”
A future built on pure insight? Evidence-based stories make that future a reality.
To learn more about data storytelling through the power of narratives view our POV ‘Storytelling is the Last Mile in Big Data & Analytics’.