This week I discussed storytelling with one of my Huspress newsletter subscribers. Data storytelling in particular.
Data storytelling is the ability to tell a story with data and to personalize the data seen according to the audience. For example, a Marketing Director doesn’t have the same reporting needs as an operational manager responsible for digital campaigns.
Data storytelling is the ability to effectively communicate insights from a dataset using narratives and visualizations. It can be used to put data insights into context for and inspire action from your audience.
Both look to agree while Toucan Toco's definition insists on the personalization to the audience while HBS emphasizes context. I might be less of a romantic but at the end of the day I guess what really matters is the actual decision making.
That being said, in order to set a decision making process in motion, storytelling is mandatory. Humans are wired to hear and work based on stories (not talking about Social Media here...).
What makes a good (data) story?
Before anything else, when asked to work on a new topic or question, analysts should themselves start by asking questions. You can rephrase what has been asked and make sure that you have all the context. I suggest you also ask for expected potential outcomes, recipients, and deliverables. I wrote a blog post last year to help frame a data question.
Patterns have been used by storytellers for centuries. As a contemporary example, Disney is VERY good at using patterns to tell their stories. (Check the The Tried-and-True Formula for Real Disney Magic blog post). Not only does it bring methodology, but it also helps your stakeholders make brain connections. For Data Analysts, it means you should always format your delivery document with a similar structure.
In Husprey notebooks, we pushed this even further by forcing "Business Goals" and "Takeaways" sections to always be located at the same place in notebooks.
Also, very early in your work, you should use your company KPIs to highlight and define the problem you're working on. Stakeholders will then be able to relate, compare with previous analyses and discover new patterns.
Slide decks are proven to be a great tool to share a story. Illustrations and visualizations help give cadence and impact to your story. The short amount of text available while designing slides forces the writer to think about the messages and the links between one slide to another.
When you want to make rational business decisions, written documents are proven to be an excellent support. Amazon has used documents since its very beginning to plan and organize meetings, and move the business forward. We can see more companies joining this new approach, moving away from slide decks to actual documents and notebooks with both context, brain juice and data.
As you write a new story, you need to make sure your sources and facts (and dimensions 🥁) are straight. Data pipeline quality is critical here (obviously?) but you'll also need to outline the scope of your work: what is in and with is out of the scope of your analysis. The source of the data and the various steps you took are also very important to ensure transparency. You might initially feel this is not a fully-fledge prerequisite for a good story? Think again, have you ever believed a journalist without sources? As a Data Analyst, your job, is to help stakeholders make the best decisions. As a Data Analyst you're convinced data is crucial in doing so.
Keep in mind that storytelling will drastically boost the reception of any of your recommendations by stakeholders.
Grow your storytelling skills. Tell more stories but don't forget the data.