The 4Ds in Data Storytelling: Making Art Out of Science
The 4Ds in Data Storytelling: Making Art Out of Science
Data is everywhere. Anyone with some level of training, and nowadays with a bit of help from AI, can generate some scientific insights out of data and build fancy data visualizations. However, interpreting and selling the meanings behind the numbers and graphs is an art. When ChatGPT and generative AI come to the front stage, many concerns regarding being replaced by AI emerge. With clear instructions, AI can help us generate code, and visualization, and even build well-performing models that contain useful insights, but they struggle compelling credible and memorable stories based on those insights. They can do science, but art is the unique skill humans possess, at least for now.
Depending on the audience, these data stories are essential to establish trust in collaborations or influence business decisions. Data scientists' work without storytelling is merely digital fortune-telling. In this article, I want to share a 4D framework to help data scientists crack the data storytelling process and deliver data insights with higher efficiency and impact, and a bonus section with practical suggestions in the end.
Define
The first step of storytelling is to define a story. What exactly is a story? While fiction writers may give a more comprehensive answer, essentially, a story is a narrative that conveys a series of events with background settings, characters, and plots. To make a story interesting, it has to be engaging, intriguing, entertaining, or informative to the readers. The data storytelling process starts with defining a story that will keep your audience interested by establishing relatable settings, compelling characters, and intriguing plots.
The settings
When you find interesting results from data, you need to set a relatable background to your receivers before delivering the findings. The settings of the storytelling establish the context for the following communication. We first need to know what is the medium of this communication. Is it a presentation or a written report? Is it a deep-dive technical session or a high-level result review? This will direct your stories in different directions.
Then, we need to define the what and why in the story. What's the context? Everyone involved in the communication needs to be on the same page. What's the matter? Why are we having this communication?
Additionally, what are the action points of this communication? Assuming everyone agrees with your story, what's the next step?
Setting up the background before laying out your story is crucial. It helps you prepare for the whole communication more structurally and efficiently and helps the audience resonate with your story by aligning them with you on the same page.
The characters
Characters are the souls of stories. A good data story should have both you and the audience embedded in the story. There are two aspects: Who are the audiences, and what's their relationship with you? Unfortunately, there is no panacea for crafting a story that will let all types of audiences resonate with you and your story. Different audiences have varying pain points, which makes it necessary to tailor your message accordingly. Ask yourself these questions when you before you prepare the story:
- Who are the audiences? Are they technically strong or business-driven? Do they have a busy schedule? Do they only care about the high-level overview, or are they detail-driven? How would they benefit from this communication?
- What's their relationship with you? Are they someone new with whom you need to build trust and credibility? Or have you already established a relationship with them? Do you need their collaboration, or are you giving them results? Will you need them to make a decision or take action?
These are the questions that will help you navigate through the preparation. Note there are occasions that you are making incorrect assumptions about your audience. For example, you thought they don't care about details, but actually, they are super interested in your detailed thought process. Thus, it is vital to have prior communications to align everyone's expectations. Or you could also have backup slides or evidence in case follow-up questions take a sub-track.
The plots
The plots are the spine of a story. Consider the structure of a traditional three-act story, a widely utilized framework for organizing and presenting narratives in literature, theater, and film. This structure separates a story into three distinct acts, each with its own unique purpose and sequence of events:
We could use the same framework in telling a data story. In Act 1 of our data story, we can briefly introduce the context and the background to ensure everyone is on the same page. Then, in Act 2, we delve deeper into the data and findings, introducing challenges that your audiences care about or try to solve. Here is where you move to the focus of the communication. It could be identifying a surprising trend or an important finding. Right away, you represent a breakthrough that tackles the challenges, perhaps discovering a hidden pattern that reshapes everyone's perspective. Act 3 is all about resolution, where we present solutions and action points. At the end, take a moment to consider the wider impact of our data story, leaving our audience with memorable takeaways or required actions. The logic and flow behind the communication will define how the audiences receive and react to the messages. A memorable story plots ups and downs and audiences resonate with these stories because they help them tackle a pain point they face.
Display
Now that we have a structure in mind, we need to figure out the flesh building on top of the bones. A data story contains both the narratives and visual support, like slides or graphs. In this section, I will mainly focus on how to choose different data visualizations that match your data story and which tools to use that will help you better convey the messages.
