Data science isn't just a bunch of numbers. It's not just charts, graphs, and tables. And it certainly isn't just data. Analytics is about telling meaningful stories. This process is called data storytelling.  Understanding your business, industry, or customers in new and profound ways will help you redefine the rules of success in your market. It's about using creative storytelling techniques to show decision-makers new and exciting ways they can succeed by leveraging your collected data.

From consumer products companies to high-tech startups, the business has changed. Companies are no longer competing on features and price alone but also:

 

• Who they are delivering value to

• Why would the consumer care about it

• How it connects to their mission

• Most importantly, What the consumer takes away from that experience. 

 

These aspects of data storytelling must be part of what data teams deliver to evaluate the decisions being made effectively. These teams must be able to share the context of how a particular decision was made (which can be argued against since other factors could have been considered) while defending why they chose that specific path.

 

Today, many companies have started including data storytelling skills as prerequisites for analysts, while others have created dedicated data storyteller roles to complement existing analytics teams. Thus, it is essential to master storytelling skills as well. To become an expert data storyteller, you can check out the best data analytics course by Learnbay.  

 

Data Storytelling and Data Visualization

 

Storytelling in data science is an art that combines storytelling, data science, and analytics. The goal of storytelling in data science is to make your data more human-readable and understandable. In order to achieve this goal, you need to understand the audience you are presenting to and what they expect from your story.

 

For example, if you are writing a report about online sales growth over time, then it may be helpful to show the growth rates over time using graphs and charts. But suppose you are writing a report about the success rate of different marketing strategies. In that case, it may be helpful to focus on one specific metric at a time instead of showing multiple metrics simultaneously.

 

Data visualization is a powerful tool that every data scientist should be familiar with. In order to master data visualization and storytelling, it is necessary to use the right tools that enable the user to create high-quality graphics. How data is visualized can be more important than the numbers themselves and convey various emotions. We tell stories with numbers, but sometimes it gets tricky and requires creativity to develop a tale cohesively.

 

There are many ways you can use data science to tell a story, including:

 

1. Creating visualizations that tell a story

2. Determining which variables are most important for your story, then finding out what those variables actually mean

3. Generating new hypotheses based on existing data

 

Components of data storytelling

There are three main elements of data storytelling:

 

1. Data:

You can only build your data story with extensive analysis of correct and complete data. Descriptive, diagnostic, predictive, and prescriptive analysis of data can help you get a more comprehensive view.

.

1. Narrative:

You can utilize a storyline, which can be either vocal or written, to convey data-driven insights, the larger context, and the actions you suggest and wish to instill in your audience.

 

1. Data Visualization

Creating eye-catching visuals to accompany your narrative and statistics may help tell your message in a way that sticks in the audience's mind. It might be in the form of a chart, graph, diagram, image, or even a video.

 

When working with data, you need to understand what you're looking at: why something happened or didn't happen, what trends exist in your data set, how they might change over time, and how everything fits together. This kind of knowledge doesn't come from simple math; it comes from understanding the context in which things happen. 

 

Why do you require storytelling skills as a data scientist?

 

The ability to tell a compelling data story is as vital to a data scientist's work as any other technical skill. As Gartner says, "storytelling is a non-technical trend crucial to the success of a data science project."

 

• Collecting and providing context for your audience when you present facts is essential. Value around data-driven insights is built by consideration of context. Data scientists' abilities to create compelling stories and communicate their findings to an audience are significant.

 

• The use of stories in marketing has a long and successful history, and for a good reason. A skilled storyteller wants the audience to feel something and understand what they're seeing or hearing. Moreover, an engaging story can make data and analytics much more understandable and approachable.

 

Conclusion:

 

Hopefully, you've found this article to be both entertaining and educational. As you can see, the story is just as important as the data. Only when both work together can you truly succeed in improving an industry through analytics and data science. When crafting data visualizations, the best way to do this is, to begin with a story you're passionate about. Once you have a clear idea of what you want to communicate, all you need to focus on is conveying that in as clear and simple a way as possible.

 

Do you want to become a data science professional and lead a team? Explore Learnbay's data science course with placement to broaden your understanding of the techniques and skills associated with real-world problems.

Publication: 22/11/2022 05:39