In Conversation

During the early days of the COVID-19 pandemic, it quickly became clear (with every graph and chart and slide tweeted and retweeted) that data storytelling was on the precipice of a major moment. Data now has a daily impact on practically every area of our lives, and our public health experts are working hard to emphasize the need for action using data. I’ve always known this ability to communicate a message through data can be powerful and extremely important (I mean, it’s my life’s work for a reason!), but for others, this intense need to rely on and understand information has been a radical shift.

With this in mind, I began a series of free webinars to help companies pivot to the changing times. The workshops quickly filled with people from all different industries who wanted to learn more about connecting with their audiences to drive change.

Interestingly, at the end of one of the webinars, a participant stayed behind (and by that, I mean she didn’t leave the Zoom meeting when the others did) and what she said both saddened me and confirmed what I already knew to be true. She was a qualitative analyst interested in doing good through research but was frustrated because she felt much of her work was manipulating data to tell stories executives wanted to hear.

I’ve been there before. So many of my colleagues have, too. (I can practically see your heads nodding as you read this!) But what this participant realized during the webinar is among the essential parts of data storytelling: It is intended to reveal, not conceal, the truth from those who need to hear it most. For data storytelling to be effective, it must convey the truth, even when the truth is difficult to hear. The intent of data storytelling is to connect decision makers with the facts they need to make well-informed, solid, forward-moving decisions. Without the truth, the story is spin. Manipulation. Which might as well be code for cement, because no one’s going anywhere.

Opinions are not facts

I have been in so many boardrooms (and now virtual workshops) where objections surface when research results are laid bare. People have been in their roles for a long time and think they’ve seen it all; they know their businesses inside and out and have a hard time trusting findings counter to their beliefs.

But this resistance comes from not giving the data its due – from placing a premium on opinions over facts. And that is where that cement mixer starts churning.

Effective, audience-centric storytelling is about recognizing opinions not as criteria for success, but as a means to connect with those on the receiving end to ultimately craft a narrative that reaches them on their level. It means understanding where the audience is coming from first, before you head into the data. The findings from the data drive the direction of your story; the expectations of your audience drive how you connect with them to bring them along on that journey.

Tell the story that needs to be told

The data storyteller’s job is not to tell the story the audience in front of them wants to hear—it is to tell the story they need to hear. (Think about all of the updated pandemic information we see every day; sometimes the graphics and data points gloss over the cold, hard facts, while other times, the truth is given to us so starkly, it stays with us days later. In the data storytelling world, the latter is much more successful.)

Before the story is the analysis, where we prove or disprove theories and discover opportunities. Our interpretation of the data will be driven by our own acumen (meaning what we know about the business) and our knowledge of what the analysis is meant to reveal (this is our technical skill). As storytellers, our audiences rely on us to help translate that process and give them confidence in the decisions that need to be made.

It should be your job to stay true to that data. In data-centric organizations, there is a premium placed on the truth. It’s when you try to manipulate the data to pull out evidence to support a preferred outcome that the story comes first and the research comes second. This is not data storytelling. You’re just stirring cement.

But what about inherent bias?

“But Laura, your peers always say there is inherent bias in storytelling!”

True. Storytelling is a human process, and as humans we always have a bias—or more accurately, a point of view. While data scientists are trained to approach information with a null hypothesis, that’s not how most think about the information. We all approach with something in mind. Data-driven folks are more open-minded to interpretation, but we all bring our own point of view to the table.

That said, our point of view is informed by the goals of the business – what does this data tell us about how successful we are in reaching our goals? Interpretation is always up for debate. But for interpretation to be truly valid, you must have a firm grasp on the business and the analytics. It must—and I cannot stress this enough—not simply be about fulfilling a pre-determined conclusion.

Once you help your audience to get beyond their closely held theories, the business opportunities will more easily reveal themselves. And that’s one foot out of the concrete.