How great data make great stories

Great stories need data, and most data need storytelling to be understood. It sure sounds like a match made in heaven. But how does it work?

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Data is boring

But turning my data analysis into storytelling is not for me — it’s for you, the recipient. Because data is boring ( — and beautiful if you’re anything like me, but most people aren’t). So, I sometimes lose the people who’ve got a hard time putting the data I display into context. And if I want to make sure people listen to what I have to say, I need to move it into a narrative or framework that will help them understand what I’m trying to say.

Here’s how I’m trying to go about it:

Step 1 — Find you why

I write down my own “why”. Sometimes this is: “I believe this client is better than it’s competitors”. Or, “I believe I can learn something interesting by looking at data from racist Facebook groups”. Most often, it’s more in line with “I need to figure out how we’re doing so that I can make the necessary adjustments.”

Step 2 — Collect your data

I collect my data points in a very unstructured form. A lot of my data analysis is exploratory, and I believe that’s fine. Most of the time, I have a topic I’m exploring, but I don’t want to already have a fixed storyline in mind when I start this process. I just collect what I find, often as bullet points and with visualisations from Tableau in a Dropbox folder.

Step 3 — Organise your data

I organise my data points in a straight line. The findings that support or are close to each other should be presented either together or following each other. I also want to find a natural way to introduce the story and a natural way to end it. I often start with broader strokes, move further into detail later, and move back out again to summarise.

Step 4 — Map out a storyline

Starting to get a storyline in place, I translate my findings into a narrative. Here I’m making sure to use “everyday language” setting the context to the real world.

Step 5 — Start to think about visualisations

I decide what visualisations that are needed to communicate my argument well. Sometimes it’s with a chart, but more often, it’s not. Most people I work with are not data literate, and if I show them a data visualisation, they tend to get very stressed.

  • II. Categories should be simple to understand and make sense to anyone
  • III. The colours used should make sense and be coherent. Not in a “pink for women, blue for men” type of way, but keep your colours throughout your story and make sure they are easy to separate and remember. Remember that a lot of people are colour blind, so green and red is a bad combo.
  • IV. Make sure the chart is possible to decode in the format you will present it. Make sure text is big enough to read for instance in a video or on a screen

Step 6 — Make your story come to life

When I have a storyline in its raw form, I try to find ways to make it come to life. This is, by far, the hardest step for me. How can I invite the audience to feel, or to use their imagination so that they are well primed for the argument I’m going to make? What examples would make people care?

Step 7 — Connect back to where you started

End by connecting back to where you started. Either by tying it to the initial story or by summarising it all neatly. Zoom out and put your argument into a broader context.

Step 8 — Test!

Test it on both your audience and on people who know the underlying data. Data analysis and storytelling is not an either-or relationship, they need to coexist. If your storytelling is making your data analysis incorrect, (for instance, by using metaphors that don’t capture your argument well), you need to go back and change it. So you’ll need to double-check that the human-friendly version is perfectly aligned with the data analysis you started out with.

Written by

Digital strategist and behavioural scientist. Fascinated by humans online. Currently consulting for @hm. Previously at @spotify.

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