The Challenges of Storytelling
Whether you’re working in data analytics or data science, the ability to turn your data into a story is an essential communication skill. Your insights are only valuable if you can communicate them effectively. Many professionals clearly see key trends in their data. They present their findings by repeating these insights as clearly as possible. They are then surprised when others fail to see the value in their work. The issue is not the quality of their insights, it is the lack of a story. We all process information a bit differently. Facts connect with some, and not others. Stories, however, are universal.
Data professionals do not need to be natural storytellers. Instead, they simply must understand the elements of a good story.
Great stories don’t simply communicate events and facts. They frame them in a way that engages the audience from start to finish. Great stories have 3 essential parts: characters, conflicts, and a conclusion.
I have a good friend who is terrible at telling stories. No matter how interesting the topic, his stories always seem to fall flat. He tends to focus on facts and is generally unable to create conflict or tension with relatable characters. Simply put, his stories are boring. When listening to him, I am rarely concerned about the conclusion. His stories end with a casual laugh and then someone quickly changes the topic.
My sister, however, is an amazing storyteller. She is able to make the most mundane events engaging. Her stories seem to connect with everyone. People laugh. Listeners pay attention. Others retell her stories. She combines characters, conflict, and a conclusion seamlessly. A good business story must do the same.
Characters + Conflict + Conclusion
First, every story needs characters. Data affects somebody. Identify these people. It could be stakeholders such as customers or employees. It could be the general public. Data-