Data Science

How do I use the 'Hero's Journey' framework for presenting data science projects?

MA Asked by Matthew Hall · 15-06-2024
0 upvotes 5,654 views 0 comments
The question

I recently read that the Hero's Journey can be applied to data storytelling to make presentations more engaging. Has anyone actually tried this in a corporate environment? Who is the "hero" in this scenario—the data, the customer, or the business? I'd love to hear some practical examples of how to map a technical project to this classic storytelling arc. 

3 answers

0
EL
Answered on 10-07-2024

In data storytelling, the "Hero" should always be your audience or your customer, never the data or the analyst. The "Villain" is the problem or the market challenge they face. Your data analysis acts as the "Mentor" or the "Magical Tool" that helps the hero overcome the challenge. The journey starts with the status quo, encounters a conflict (the data insight showing a problem), and ends with a resolution (the recommended action). This framing makes the audience feel empowered rather than just informed. I’ve used this for a churn reduction project, and it significantly increased stakeholder engagement.

0
RI
Answered on 12-07-2024

This sounds great for a big pitch, but isn't it a bit dramatic for a weekly progress update or a standard sprint review? 

EL 15-07-2024

Richard, you don't have to be overly theatrical. For smaller updates, just use a mini-arc: Challenge, Insight, Action. It’s a scaled-down version that keeps the narrative focus without feeling like a movie script. It’s about maintaining a logical flow where the data serves a purpose, rather than just being a list of tasks completed during the week.

0
NA
Answered on 25-07-2024

The Hero’s Journey works because it mirrors how humans process information. By making the stakeholder the hero, you ensure they are personally invested in the solution you're proposing. 

MA 28-07-2024

I agree, Nancy. It shifts the perspective from "here is what I did" to "here is how you can win," which is a much more persuasive way to present data science results.

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