The Value of Storytelling in Data Science

Data is more than a mere sequence of numbers. Data is a complex puzzle. When that puzzle is constructed meaningfully and viewed from the appropriate perspectives, it tells an impactful story. A good data scientist goes beyond simply analyzing numbers and regurgitating results.

A good data scientist adds value to those numbers by turning raw data into information, and subsequently, information into insight.

This is what separates NC State’s Institute of Advanced Analytics (IAA) from its peers. Students in the program are equipped with far more than analytical prowess; they understand that data, at its fundamental level, is a story waiting to be told. 

Let’s first take a step back and note that technical skills are important. You can’t solve a puzzle without the appropriate tools and techniques to do so (programming, modeling, data wrangling). Of course, these are all coveted skills in the data scientist’s toolbelt, and rightfully so.  But they are only one-half of the equation. Oftentimes, what happens after the analysis is completed is where the true value is created. 

So, you’ve collected, cleaned, analyzed, and drawn conclusions from your data? Good, you’ve generated information, and most of the work is now behind you! However, the final portion is the most valuable part. The final step is turning that raw information into interpretable, and more importantly, actionable insight that your client can use to make decisions. 

More often than not, your client can’t directly use your mathematical or statistical findings, at least not in their raw form. Be it a lack of understanding, lack of sufficient context, or simply lack of time, the raw analytical outcome is often not as important to those who didn’t actually complete the analysis. What usually matters most to the people who hired you are two things:

  1. How does this impact them (or their business)?
  2. What does this mean / what should they do?

In order to answer these questions and generate actionable insight, you must learn the art of storytelling. Transforming analytical findings into a coherent and meaningful story is the last, yet most influential, step in the data science process. 

The analysis you perform and the insights you generate as a data scientist will diminish in value if you aren’t able to effectively communicate those findings. Simply presenting numbers and data from your analysis rarely leads to a sound delivery of ideas. 

Storytelling allows you to engage multiple parts of your audience’s brain. Rather than presenting raw findings and numbers, through storytelling, you can evoke an emotional response from your audience to help your points be remembered and acted upon. Storytelling also helps usher in a rational perception of the findings. A cohesive and connective delivery of information allows the audience to see the full arrangement of ideas, especially if those ideas or findings are contextually sensitive. 

Context is what builds the value that surrounds insights generated through data. Through storytelling, data scientists can help build the context needed to successfully explain their ideas to everyone present in a manner that is both digestible and relevant. 

At the IAA, the art of effective communication through storytelling is a pivotal piece of the program. Ultimately, storytelling helps bridge the disconnect between data and knowledge. Data is a puzzle. But the most important element of data science isn’t the construction of the puzzle; rather, it’s the portrayal of a meaningful story from the image that the puzzle creates.

Columnist: Hunter Johnson