James Baldwin. Harper Lee. Franz Kafka. Susan Sontag. Arianna Huffington. The list could go on and on. You read their novels and stories and are instantly sucked in. You want to know more about the protagonist, you start to abhor the antagonist, you change your mind about a pressing modern-day issue, and hours later, you wonder where your day has gone. How do they do it? Well, there are thousands of classes that aim to answer that very question, and millions of students, writers, and journalists willing to take them.  

But many of those classes, in my experience, don’t look towards the future. How do you channel those prolific writing styles when data is involved? How do you use Kafka’s wondrous metaphors to talk about a 2,000-person data set, and how do you use Susan Sontag’s love of visual imagery to talk about human behavior? Can it be done, or is data-driven writing too far removed from other writing styles?

Why Do I Ask?

Simply put – because someone needs to. Writers write, that’s a given, but writing has changed now that data is expected to not only be included in various forms of writing, but front and center. Just think about how infamous data-driven journalism has become, even though the term has only been around since 2009. This pivot in writing has presented us as authors, journalists, and marketers with infinite possibilities, and just in time.

When you include data in a story, it’s perceived to be more valuable, trustworthy and authoritative. In an era of rampant illegitimate news stories, and when the majority of adults don’t trust any news source to be unbiased, this becomes even more important. The role of the writer in various capacities is changing.

The Data Journalism Handbook (which is a real thing), says that “Data…can reveal ‘a story’s shape’ (Sarah Cohen), or provides us with a ‘new camera’ (David McCandless). Using data the job of journalists shifts its main focus from being the first ones to report to being the ones telling us what a certain development might actually mean. The range of topics can be far and wide… in today’s global economy there are invisible connections between these products, other people and you… The language of this network is data: little points of information that are often not relevant in a single instance, but massively important when viewed from the right angle.”

All of this is a somewhat convoluted way of saying that data-driven writing, throughout many genres, is important and here to stay. So, now we can get to the interesting part.

What Will These Data-Driven Stories Look Like?

Given how new the idea of writing stories revolving around data is, there’s no right or wrong way to do it. It’s an exciting time for experimentation. Writers have the opportunity to blend different ways of writing with the growing emphasis on data. Data-driven writing could even incorporate Shakespearian language if it wanted to:

Oh n value, oh n value, where fore art thou n value?
Is this an inadequate sample size which I see before me, the handle toward my data set?
Get thee to a startup
Shall I compare thee to T= .33?

I’m just not sure it clicks – but worth a try, see? Experimentation. Failed experimentation.

To provide real context, take a look at this recent CivicScience graph (mentioned earlier) illustrating trust in the news:

Most people don't trust any news source to deliver unbiased news, but data-driven journalism and writing might change that. You have all this information at your fingertips, but how do you narrate it? Do your classic lessons from journalism and writing classes still apply? Do you include a setting? Do you have to provide an answer or solution? Are there characters? Do these numbers show a protagonist – an antagonist? Do you include a traditional interview even though these data points are basically 2,000 interviews? What do you compare these answers to? And the classic writing dilemma: Is this even worth writing about? I could go on and on.

These questions are sometimes overlooked because the process of data-driven writing includes spending immense amounts of time searching for and analyzing data, but not always as much time forming that information into a narrative. That’s probably because – yeah – it is really hard to place data into a story that’s engaging but not superfluous, informing but not monotonous, and data-driven while also appealing to the millions of people who aren’t (yet) data-literate. The life of a data-driven writer (which again, might soon be all of us) is tough stuff!

This brings us to…

Today’s Data-Driven Writing Paradox

It often seems that there are those who can write, and those who can understand and analyze data, and fewer who can do both. To me, that’s a problem. Given how vital data has become in our everyday lives, that gap cannot continue.

So, although slightly dramatic, here lies the crossroads between writing and data. Where will they merge, and how can the data-literate and data-illiterate writer best create data-driven stories?

Though I profess to have no concrete answers, I think it’s important to realize the inherent creativity in both data and writing, and to remind all writers that data-driven stories do not negate or erase the creativity inherent in writing, as I hope I’ve illustrated. And again, I hope this gives you permission to relax and experiment while writing. There’s no right or wrong here! 

Interested in learning more about data? Check out our recent interview with Ross McGowan, leading data scientist!