Data Storytelling – The Most Valued Data Science Skill in 2021 and Beyond

Perhaps the most significant advancement of the 21st century concerning ‘Data’ is that it can be put forth in a way, which is understandable by a layman. Data is no more understood to be a bunch of numbers and statistics that go over your head. Organizations such as Google, Netflix, and The New York Times have changed how data is perceived and consumed in society, courtesy of their exceptional data storytelling ability. 

But there exists a massive skill gap when it comes to using data to tell a story. The organizations mentioned above are, no doubt, disrupting the market with their innovation. But what about start-ups and SMEs? Most of them are still to figure out a way to tell their data’s story. But, is it even important? Of course, it is, considering it’s a weapon of choice to tap into competitive markets and achieve innovations. 

In that light, this article explores data storytelling as a must-have skill for data scientists in 2021 and beyond.

What Exactly is Data Storytelling?

Data storytelling is the process of finding a compelling narrative and presenting it in such a way that makes it easy for people to consume and understand. In other words, it is the art of making an organization’s data accessible and usable by anyone. 

Perhaps, the above definition is vague in terms of data science. To that end, Data storytelling can be attributed to the use of visuals to create a story with data. The creativity of the user and visualizations used are what differentiate a data story from all other accounts. This explains why Netflix has leveraged its extraordinary data storytelling skills to disrupt the movie rating industry and become one of the leading streaming platforms in just 10 years of operation.

“When you package up your insights as a data story, you build a bridge for your data to the influential, emotional side of the brain.”

—Brent Dykes, Forbes

Is Data Storytelling the Biggest Skill for Data Scientists?

It won’t be fair to say that it’s the biggest. In fact, no skill, in this day and age, can be singled out. But I must assert that data storytelling is one of the most decisive data science skills — one that completes the process.

For instance, when creating a new model, a data scientist must explain how the employment and application of data. That’s only possible through data storytelling. 

In the past, people had to make sense out of data without the aid of visualization tools. Today, however, people have more options available to them that would make them understand your findings. 

This means that you are expected to use certain types of stories and presentations to back up your findings. In concrete terms, as a data scientist, you must extract actionable insights from the available data. 

How Can Data Scientists Create Great Data Stories?

Data storytelling is focusing on the different steps you take to get your data into a story format. Once you have it in a story format, you can use visuals to demonstrate a cluster of numbers together in a cohesive story.

The simplest way to start this process is to outline what your data is trying to say and then add visuals as needed. Here’s a detailed approach to achieving the same:

1. First things first, have access to great data

To create a great story, you must first have great data. You can’t just use any data and make it into an account. You must have contextualized data that makes sense in the scope of your narrative. This means that you need to know the nuances of your data, what it could mean, and where it’s coming from.

1. To evaluate the data quality, ask the following questions:
   1. Is the data duplicated?
   2. Does the data constitute erroneous values?
   3. How much does this data vary?
   4. What is the extent of missing data?
   5. Is the data in the proper format, and is it easily accessible?

For instance, you can’t create a compelling story using unstructured social interactions. The same goes for unstructured financial data and so on.

2. Develop a coherent and concrete narrative

With the correct data, you can then begin to develop your narrative by first deciding on what type of story you want to tell—is it an investigative reporting on how America’s health system is broken? Or maybe you want to tell a triumphant tale about how small towns are fighting against the opioid epidemic? Regardless of what angle you take with your narrative, one thing needs to happen—it must be coherent and intuitive. 

3. Build a persuasive proof

You certainly don’t want people to believe you just made the whole thing up. Therefore, your story should be backed by compelling evidence—either scholarly data or real-life stories. Ideally, both the data and the human element should support each other, one from within your narrative and one from the outside world.

4. Back your narrative with actionable insights

Once people are convinced that your data has a story to tell, they should also be made confident that your story will deliver tangible action items or ideas. Therefore, it’s key to back up your story with actionable insights and solutions for the problems at hand. 

5. Use visuals to tell the best stories

This is where data science meets design. You need to engage people’s senses through visuals like charts, infographics, diagrams, videos, and animations. The more senses you engage, the more immersive your story will be. This is what makes Netflix so successful, for it’s able to use a wide array of visuals to make its story come alive. 

Takeaways

Data storytelling is a skill that many organizations are adopting because it is an integral part of making data accessible to any customer and potential investor. For data scientists, mastering it is a must for the success of any organization. 

Do you agree with this article’s title? Do you want to share your own opinion about Data Storytelling, or do you want to share an example of a Data Story that can benefit others? Leave a comment here below!