Mastering the art of Data Storytelling Details: Crafting a Compelling Story with Data
The big picture of storytelling is nailing down the five key elements of a story: characters, setting, plot, conflict, and resolution. However, if you've ever heard someone tell a story, you know it takes more than those five elements to make it interesting. The devil is in the details. Skilled storytellers embellish their stories with plenty of details that feed the imagination and stimulate the senses. They make you feel as though you're watching the action unfold before your eyes.
In a similar fashion, your data science team should include plenty of details in every story it tells to flesh it out and make it more memorable. Details are like little mental sticky notes that help the audience remember the characters, setting, plot, conflict, and resolution. In addition, the details provide supporting evidence to the larger observations and claims being presented by the team.
Shots and Needles?
An organization I once worked was struggling to get enough people to participate in its medical studies. The data science team was called in to figure out why. The team conducted some research and discovered that some people are afraid of needles, others are afraid of having their blood drawn, and a cross-section are afraid of both. This cross-section represented a lot of people.
The data science team asked some good questions and made some interesting discoveries. One such discovery was that people who participated in and had a positive experience with a medical study that did not involve needles or blood draws were more inclined to participate in future studies that die involve needles or blood draws.
The research lead (a nurse) had a great idea on how to tell that story with impact. She would start with a case study, changing the participant's name and a few details to protect the patient's anonymity. Her story went something like this:
When I was a nurse I could always tell who was afraid of needles. They always crossed their arms in a certain way. They grabbed both of their elbows as a way to protect themselves from the poke in the arm. There are a lot of people out there like that, and we need them to participate in our medical studies. So I'm going to tell you a little bit about someone I found in one of our reports. Let's call her Tracy. She participated in one of our medical studies for a drug being developed to help people sleep. The first day of the study she showed up with her own pillow. She must've been optimistic about how well it would work. She was hoping that this new pill would help her since she had some trouble sleeping during periods of high stress. It turned out that Tracy was one of the participants who didn't get any benefit from the drug. When she left, she told the nurse that her father was a doctor, so she felt some obligation to participate in medical studies. She said she could never be a doctor because she was scared of blood and needles. A few months later she decided to participate in a flu vaccine trial. The study required needles for the vaccination and for later blood tests. So why did Tracy decide to participate?
The obvious answer the research lead's question is that Tracy participated because she felt an obligation to do so. After all, she didn't actually benefit in any way from the sleep study. She felt as though she couldn't contribute to helping others with their health issues directly by being a doctor or nurse, so she would do her part by participating in studies.
Now, think about the story you just read. What do you recall? Clearest in your mind are probably the details — the description of how people held their arms when they were afraid of needles, Tracy's name, Tracy bringing her pillow to the sleep study, what her dad did for a living, the trials she participated in, and so on. All of these details make it easier to remember the story and to remember the conclusion drawn from the story — that Tracy participated in medical studies because she felt obligated to do so.
When you tell a data science story, try to use details to paint a picture in words. They help your audience connect to characters, setting, plot, conflict, and resolution.
Avoid the Temptation to Deliver a Presentation
Data science is a combination of science and art. The data science team follows the scientific method to explore and discover — to add to the organization's growing body of knowledge and insight. The team then uses the art of storytelling to convey that knowledge and insight to people across the organization in a compelling and memorable way.
Business presentations are boring. They're not structured to be interesting. They're static. They communicate the current state of affairs. They’re like a verbal “reply all” to the organization's stakeholders. That’s usually fine for status meetings, but it falls short when you need to convey a point, make it stick, and transform the audience in a positive way.
Avoid the temptation to merely deliver reports or presentations. Use the data and the findings from your analysis to tell a compelling story. And be sure to include the details.
Frequently Asked Questions
What is data storytelling?
Data storytelling is the practice of blending narrative techniques, data visualizations, and data analysis to communicate insights effectively. It enables data scientists and analysts to present data in a compelling way that is easy to understand and act upon.
Why is effective data storytelling important?
Effective data storytelling is important because it helps transform complex data into a clear and compelling narrative. This makes it easier for stakeholders to understand key insights and make informed decisions. An effective data story can turn raw data into actionable insights.
What are the key elements of data storytelling?
The key elements of data storytelling include a clear narrative, compelling data, and effective data visualization. A clear narrative helps to tell a story with data, compelling data ensures the story is engaging, and effective data visualization aids in interpreting and presenting the data.
How can I create an effective data story?
To create an effective data story, start by identifying your audience and their needs. Gather the right data and focus on key data points that support your story. Use compelling data visualization techniques to present data clearly and embed it within a narrative that is easy to follow. Ultimately, your goal is to make the data meaningful and actionable.
Can you provide examples of data storytelling techniques?
Examples of data storytelling techniques include using data visualisations to highlight trends and patterns, weaving a narrative that explains the data context, and using storytelling skills to humanize the data. Data storytelling examples often use charts, graphs, and infographics to communicate insights effectively.
How do I ensure that my data storytelling is compelling?
To ensure your data storytelling is compelling, focus on clarity, relevance, and engagement. Present data that is most relevant to your audience and ensure the narrative provides context and meaning. Use visualization tools to make complex data more understandable and engaging. Ensure your data story resonates emotionally with your audience.
What are common challenges in data storytelling?
Common challenges in data storytelling include dealing with complex data, ensuring data accuracy, and making the data story engaging. Ensuring the data is relevant and presented in an understandable way is critical. Overcoming these challenges often requires strong storytelling skills and a solid understanding of data visualization techniques.
How does data visualization play a role in data storytelling?
Data visualization is a critical component of data storytelling as it helps to present data in a visual context, making it easier to understand and interpret. Good data visualization can turn complex data into clear, impactful visuals that support the overall narrative of the data story.
What are some tips for improving data storytelling skills?
To improve data storytelling skills, practice merging data analysis with narrative techniques. Focus on honing your ability to identify key data points and trends. Enhance your data visualization skills to present data clearly and effectively. Additionally, studying successful data stories and storytelling techniques can provide valuable insights into creating compelling data narratives.
How can data storytelling be used in business contexts?
In business contexts, data storytelling can be used to communicate insights from data and analytics, support strategic decisions, and persuade stakeholders. By creating a data narrative that is both compelling and easy to understand, businesses can leverage the power of data to drive better outcomes and achieve their objectives.
This is my weekly newsletter that I call The Deep End because I want to go deeper than results you’ll see from searches or AI, incorporating insights from the history of data and data science. Each week I’ll go deep to explain a topic that’s relevant to people who work with technology. I’ll be posting about artificial intelligence, data science, and data ethics.?
This newsletter is 100% human written ?? (* aside from a quick run through grammar and spell check).
Travel Agent at Advantage Solutions
3 周Basically. Thanks to you we are able to be of some leadership to the following generation of AI intergration.
Travel Agent at Advantage Solutions
3 周#大丈夫???? ありがとうございますにもっとも！