The Importance of Storytelling in Data Science

Author:Murphy | View: 27609 | Time: 2025-03-23 19:46:13

Opinion

Storytelling has been a part of human culture for thousands of years, and it is no secret that people are naturally drawn to stories. Stories are a powerful tool for communicating ideas and information. They can evoke emotions and create connections that statistics and Data alone, in all their abstractness, cannot. To succeed, Data Scientists must be able to create stories from their work. Where complex concepts, statistical models, and large amounts of data are the norm, storytelling is crucial to ensuring that findings are understood, remembered, and acted upon.

A story takes all the abstract coldness of statistics and data then turns it into something relatable for our clients. Stories have a structure: a beginning, middle, and end that makes them easy to follow. They are relatable. They feature characters and situations with which people can identify. They create connections to other ideas and concepts, allowing clients to link new information with things they already know.

Moreover, people are not very good at understanding abstract ideas. Our brains are wired to process concrete, tangible information that can be seen, heard, touched, tasted, or smelled. On the other hand, statistics and data are often intangible and difficult to grasp. For example, the statement that 44% of people believe that pizza is perfectly acceptable for breakfast [1] doesn't feel very meaningful at first. Is that a lot? A little? But the statement that two of your five friends probably eat pizza for breakfast gives us more feel for the statistic. That seems like a lot to me! And if we wanted to nail down the statistic, we could make it even more tangible: Imagine your town holding a community breakfast, where residents were invited to try pizza for breakfast. If ten people attend, you could expect that 4 of them would happily eat their pizza!

Abstract ideas become concrete and tangible by weaving a story around data and statistics. They are more easily understood and remembered. A story about a customer who struggled with a problem and was helped by a data-driven solution can evoke empathy and interest. In contrast, presenting statistics about customer satisfaction may only elicit a shrug. When presenting data, it is crucial to not only communicate the facts but also to connect with the audience and make the data meaningful and relevant to them.

So, what is the role of storytelling? Let's consider a model that predicts the probability of churn – the loss of customers – for any given customer of our organization.

Communicating complex information: Complex models and extensive data can be challenging to understand. Storytelling can break down these complex ideas into smaller, manageable parts, highlighting key insights. For instance, we could describe a customer with a high predicted churn probability who recently moved to a new location and has been using the service less frequently. By focusing on the impact of these pieces of information on the model's prediction, we can help the audience understand the underlying drivers of the model.

Engaging the audience: Data can be dry and dull, making keeping an audience's attention challenging. Storytelling can make data more engaging by creating a narrative that connects with the audience. For example, we could highlight how a customer's usage of our mobile app is a strong indicator of customer loyalty and how the model took this information into account when making the churn prediction. This helps bring the data to life and makes it more meaningful to the audience.

Driving action: The goal of data communication is often to drive action, such as making decisions, implementing specific recommendations, or changing the way people think about a problem. In our example, we could tell a story about a customer who was predicted to churn but was successfully retained by the company's customer service team. Maybe the customer recently moved and has been using our service less frequently but the customer service team was able to retain them by pointing to a nearby store location that the customer didn't realize was there. In the future, we know that when a customer moves, we should send them details of nearby store locations for their new area. The story highlights the specific actions the customer service team took to retain the customer and shows how these actions could be used as a model for future customer retention efforts. This drives action by providing concrete examples of how the results of the churn model can be used to improve the business.

Making data memorable: When presenting data, it's essential to ensure that the audience remembers the key insights and recommendations. Storytelling can make data more special by creating a narrative that sticks in our client's minds. In our recently moved customer example, we could give the customer a fictitious name, or if we have records of their communications with customer service, we could incorporate some of those into our story. This makes the story more real for the audience. It becomes a very concrete thing that they can visualize. The audience will better understand and remember the data long after the presentation.

Storytelling is a powerful tool that Data Scientists can use to make their findings more relatable, memorable, and impactful. By combining data with a narrative structure, we can communicate complex information in a way that is accessible and engaging to our audiences. Additionally, storytelling can help to drive action, and make data memorable, thereby ensuring that the results of data analysis are effectively used to drive positive outcomes. As Data Science continues to play a critical role in decision-making, storytelling as a communication tool will become increasingly important for data scientists looking to make a real impact with their work.