Who doesn’t like a good story? I recently read an article by Paul Zak for the Harvard Business Review titled “Why Your Brain Loves Good Storytelling.” In this article, Paul describes how data-driven stories are able to cause audiences to release larger doses of a neurochemical called oxytocin. According to the academic, this chemical “is produced when others trust us or show us signs of kindness, which motivates cooperation with other people.”
The force of narrating lies in its capacity to change individuals’ way of behaving or information. Consequently, this practice has great potential to extract the messages and lessons that our data contains. Increasingly, data stories are being used to convey to the public the most important messages raised by the rigorous efforts of data collection and exploration. For this reason, an important question arises: once I have analyzed the data and identified the key conclusions that we can draw from it, how can I use storytelling to present these ideas? There is no doubt that storytelling is an excellent resource, but you always have to be clear about your message and decide whether to represent it in a video, infographic, illustration, or any other format depending on what you want to convey.
The following steps will guide you through the process of developing a data story.
Define a call to action
This is probably one of the most challenging steps but also the most necessary. What is the message you want to convey? What do you want your readers (or listeners) to do or share after hearing your story? We use data stories to transform something abstract or complex into something people can easily understand, contextualize, and remember. Remember that “anecdotes trump data.”
Identifying a call to action allows us to focus on the design and narrative of our story, which will increase the likelihood that our audience will share the story, commit to taking action, and/or make desired changes.
Choose your audience
Pixar, the well-known animation studio, states that rule 2 of the 22 rules for storytelling is knowing who the audience for our story is going to be and what their interests are: “You have to take into account what the audience is interested in. public, not what you have fun doing as a writer. These two things can be very different.” Consider who is going to be interested in the message that your data transmits and to whom you direct the call to action. Writing the story for them.
Envision that the crowd you have picked had the valuable chance to dig further into the information you have gathered; what questions might they raise? What decisions will they face, and what information do they need to make the most appropriate one? Be sure to include your audience’s point of view in the story you develop.
It’s important that your audience can review the data if they have any questions or don’t entirely agree with your story. During the collection, processing, and analysis of the data, you probably had to make many decisions about what to select and how to record everything; one way or another, you interpreted some variables in a specific way. Remembering all the parameters and decisions you make can be overwhelming, so make sure you document your entire workflow and share the most relevant information and caveats with your audience.
Simplify as much as you can
When presenting your data, always keep your audience in mind. You shouldn’t mention terms like standard deviation or confidence interval if your audience doesn’t know much about statistics. In this regard, Jeremy Taylor has developed a series of steps that can help you in this process. For example, he recommends starting by showing descriptive statistics, synthesizing the data ahead of time to focus on the analysis itself, or even acknowledging that the study did not yield meaningful data. If you want to go deeper into how to present statistical data, you can also read the United Nations manual “How to make data understandable.”
Use visual aids to complement the story.
Visual aids enhance the story you are telling, but the choice of what and how to protect them is of great importance. Meg Cannistra, at Ceros blog, suggests that choices should be based on the type of data relationship you want to present. Deciding how you are going to tell your story can be very complicated. You generally need to remember what the target of your message is. To make this task easier, here you can consult an article about different types of visual aids and how to choose the most appropriate one according to the message you want to convey. Also, in the article “Narrative Visualization: Telling Stories with Data,” Stanford researchers analyze author-driven versus reader-driven narratives; Do you want to communicate a clear message, or do you prefer to encourage public interaction with the data?
Tell a heartfelt story.
In general, human beings tend to make the mistake of seeing what we want to believe. If, when interpreting the data, your hypothesis has not been made clear, sometimes you can “find” things in the data that are not valid but that support the point of view that you want to test. So make sure you tell a candid story that is consistent with the data, regardless of the message you want to convey or the decisions you want to influence. For reference, in this article, Steve Cooper presents a series of guidelines that can help you when interpreting your data. For example, remembering what the context is, collecting “points of interest,” looking for deviations, etc.
Apart from that, if you are interested to know about Eight common examples of natural language processing then visit our Tech category.