We hear a couple of buzzwords when we talk about presenting data, data visualization, and data storytelling. There are some key differences, as data visualization can be part of data storytelling, but storytelling is an art form all on its own.
Business leaders speak in metrics and data. But translating those loads of recruiting data (time-to-hire, offer acceptance rates, cost-per-applicant, etc.) into stories makes it more meaningful and more actionable to your internal clients. It also allows you to craft the story of your business culture when you are hiring.
What is data storytelling, and how do you tell a story with data?
The very definition of a story is a narrative with a beginning, middle, and ending. Usually, there is a theme, and it involves both action and conflict. It is the action and conflict that make the story interesting. Without it, your narrative will often fall flat.
In data stories, this action involves interesting data that either illustrates a challenge or a solution to that challenge. The conflict is the potential pain points that the data addresses. For example, with recruiting data, perhaps offer acceptance rates are low, but if a competitor offers a higher salary or more benefits, and their offer acceptance rate is higher, we see both a conflict and a potential solution.
In other words, there is a story there, one with a potential journey from where the data shows we are now to where we want to go. It need not be as harrowing as the journey from the Shire to Mordor, but there are steps along the way. The path can be illuminated by additional data.
This leads us to our first step in telling a data story: determining the story we want (or need) to tell.
First, start by determining your story. Are you giving an overall view of the health of the recruiting process? Are you focused on specific data (like the rate of offer acceptance example above)? Are you celebrating a victory, proposing a change, or simply offering an ongoing progress report?
The purpose of your data story will help you know how to present the data you have. This brings up an important point: telling data stories is NOT cherry-picking data. You need to present the whole picture even if your story will focus on one area of data.
Also, make sure you present balanced, clear, and honest data to illustrate what is actually happening and, if appropriate, what can be done to alter and improve current outcomes.
Any author will tell you that knowing your audience is vital to your story resonating with them especially when sharing a data story. Think of these questions:
Remember, a data story is like any other story: it is designed to inspire both feelings and action. You want your listener or reader to be excited and motivated by the data, and then to take appropriate steps based on that inspiration.
Data that does not inspire is just data visualization with a narrative, and that doesn’t serve you or your internal clients well.
Remember how we said your story needs a beginning, middle, and ending? Usually, with a data story, this is how it might look like:
Of course, there are other data stories you can tell as well, but usually, it is about comparison, correlation, or correction. So where does data visualization come in?
Visualization serves as a part of your story to illustrate your point with graphs and charts showing your data. The most important thing to remember is that this supports your story but it is not the story itself.
Keep things consistent: colors consistent with your brand and the presentation. Keep things neat and organized, not cluttered, and be sure the visualization shows a clear picture of what you are trying to illustrate.
The final piece of a data story is a resolution. You need to wrap everything up in a neat package. Did you ever get angry at the end of a story or a movie where the ending was just–vague? The same is true for data stories. They need to provide your reader or listener with a clear resolution of the data into an actionable plan or a clear summary.
Don’t leave anything hanging. You’re not writing the Hunger Games series; there isn’t a need for a cliffhanger. This story needs to come with a satisfactory ending and a resolution or path forward.
Data stories are just like any others. They need action, conflict, and a beginning, middle, and end. And the story needs to have a point or a theme. Know your story. Know your audience. Build a strong narrative and tell it well, and your data will provide more meaning and result in better outcomes over the long run.
And that is what every storyteller wants, whether their story is told in words or in data.