Page 93 - The EDIT | Q1 2017

Thoughtleader
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One striking observation of this approach (and
other data storytelling frameworks) is that data
is never the start point. If you’re an analyst and
you find yourself jumping straight into the dataset without some forethought on what it is you are trying to achieve, without asking questions, without thinking about the audience and what ultimately the audience wants to achieve or see from the data — then you should probably reassess the way in which you are working.
One other aspect of data storytelling that is noticeably absent in this essay is that of visualisation. I have left this out deliberately as visualisation is not data storytelling — whilst it’s true that visualisation is a component of story-telling, it should be seen
as complementary. And more emphasis should be placed on the data and the story, as opposed to employing super slick visual aides.
Final thoughts
One thing that is noticeably consistent when exploring the concept of data storytelling — it’s about balance. There is a need to stimulate your audience both intellectually (data) and emotionally (with a story) — use data and tell your story through a humanised narrative. A good acid test is to read “just” the headers in your slides (of course the slide content is important to support the point), but by laying out your slides where you can only read the headers — your story should be apparent from
just the slide headlines. If you find a slide where the header doesn’t seem to flow with your story or seems out of place — it probably is.
At the end of the day, it’s not the data itself that will make us stand out. It is all about what we do with the data that helps us Find A Better Way.
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THE EDIT ISSUE 05 | Q1 2017