How to become a data storyteller in 2023

Data storytelling is a process that involves collecting, organizing, and presenting data to tell a story. It aims to help users understand the context of the data and provide an engaging experience for them. For example, if you’re working on improving customer satisfaction using predictive analytics on your website’s conversion rate, you may use data storytelling to help you understand this process.

Data storytelling can be done in many ways—from mapping data points on a graph to creating infographics that include images or videos. It’s important to consider your audience when doing this kind of work because they will grasp different methods differently, depending on their level of knowledge.

Data storytelling combines data into one place where everyone can access it easily and understand your processes. It helps with communication between different groups within an organization or a company.

What is data storytelling?

Data storytelling is a way to make data more accessible, understandable, and actionable. It can help you decide what products to develop or how to improve your internal software and tools. Alternatively, you can use it to explain your value proposition to customers (even potential ones).

In the digital marketing world, data storytelling refers to creating visualizations meant to convey insights from data sets in an easy-to-understand way. This type of visualization is referred to as a data viz (short for “data visualization”). It can include charts, graphs, maps, and other visual representations that help people understand key trends within large datasets.

Check out our blog on the rising demand for data science!

Data storytelling techniques

Data storytelling can be used for various purposes, including:

1. Talking to people

This data storytelling technique involves talking with users about what they want from their organization and how they can help them achieve it. It helps the organization understand its customers better and find out what works best for them.

2. Insight postcards

Insight postcards are a great way to share your data-driven insights with the world. These postcards can be used as a stand-alone piece of collateral for an event/presentation or as part of a larger infographic you create to share with your audience.

3. Infographics

Infographics can be broken into three different types:

• Pictorial infographics: These are simple images that show facts in an easy-to-understand format. They’re usually accompanied by text explaining what each image represents.

• Data-based infographics: These use data from sources such as Google Docs or Excel files to create interactive visualizations that help readers make sense of complex topics or data sets.

• Timelines: These convey historical events or trends over time (for example, how much traffic there has been on a certain highway over the years).

4. Sharing dashboards

Dashboards can be created using a few different diagrammatic formats, including:

• Bar charts – These charts display information about numbers or quantities, such as sales figures or profit margins. They can be used to show how much each product category contributes to the total revenue or how much each department contributes to the total cost overruns of a project.

• Pie charts – These are often used to compare parts of your business with others, such as expenses between departments or sales between two quarters.

• Line graphs – Line graphs show information about changes over time and are useful when only two variables are involved in an analysis (for example, quarterly vs. annual sales).

You may be interested to know the importance of data science? 

Data storytelling tools

Data storytelling tools are a great way to make your data analysis more accessible and engaging for your audience. These tools can help you tell stories that are easy to understand, so your audience can get what you’re trying to say without reading through the unnecessary text.

• Juicebox
• Toucan Toco
• Nugit
• Flow Immersive
• Flourish
• RAW Graphs
• DataWrapper
• Tableau Story Points
• ArcGIS StoryMaps
• Qlik Sense Stories
• PowerBI
• Observable
• Infogram
• Visme
• Piktochart
• Sisense Narratives
• ChatGPT
• Phrazor
• Luminoso

Data storytelling examples

Data storytelling is a new and exciting field that can help you tell your story in an engaging, impactful, and informative way.

The best data storytelling examples are:

• We Feel Fine by Jonathan Harris
• The Physical Traits that Define Men & Women in Literature by Erin Davis (The Pudding)
• Animated Sport Results by Krisztina Szucs
• Buy or Rent by The Upshot
• Why Do Cats and Dogs…? by Nadieh Bremer and Google Trends
• Fry Universe by Chris Williams
• US Gun Deaths by Periscopic
• Explorable Explanations by Bret Victor
• Cicadas, A Data Story by Kayla Brewer
• 30 Years of American Anxieties by The Puddling

How can I become a data storyteller?

This section will examine some key steps of effective data storytelling. 

1. Find the story within the data.
2. Consider your audience.
3. Determine what data matters and what doesn’t.
4. Analyze data and find insights.
5. Identify the most effective data visualizations.
6. Provide context.
7. Structure your story.

Becoming a data storyteller is a great career path for those who want to use their data analysis skills more practically. Data storytellers can translate the results of their analysis into stories that can be accessible by other team members and are easy to understand.

The first step is learning to make sense of your data. This means understanding each variable’s meaning and the relationship between variables. You should also be able to describe the relationships between variables in terms of cause-and-effect relationships or patterns.

Next, you can begin plotting the variables on graphs to make them appear statistically significant (which means there is enough information in the data set).

Finally, once you’ve plotted all your variables together on one sheet or spreadsheet (or multiple sheets), it’s time for some real-world testing!

Read articles from publications like The Wall Street Journal and The New York Times to learn about the industry and its trends. Find out what kind of data analysts work at your company and how they use their skill set. It may give you an idea about the working structure of professional projects!

Read our blog to know more about the career path in data science.

Data science job roles and demand

Job roles	Demand (People opted)
Business Analyst	23.3 million
Data Analyst	86,681 in the US
BI Developer	41,196 in the US
Statistician	12,486 in the US

Conclusion

Online Manipal offers a unique opportunity for students to access data science programs from reputed institutions such as Manipal Academy of Higher Education, and Manipal Institute of Technology (MIT). Along with online MBA from MAHE, you can choose Data Science and Business Analytics to learn the art of data storytelling besides becoming future-ready data professional. Nine-month PGCP in Data Science and Machine Learning program from MIT is a short-cut to enter this niche domain.

The syllabus covers data analytics in details – from setting up a plot with Python and R, to create charts in Excel to writing codes in Python or R. The course also includes quizzes that assess students’ understanding of the material being taught, which will help students stay on track for each topic. Enroll in a suitable course matching your interests today!

Key takeaways:

• Data storytelling is the act of telling stories out of data. It synthesizes data into information that makes sense and can be consumed by a non-technical audience. 
• The best way for people to learn data storytelling is through tools like Tableau or Power BI. 
• The key is picking up on what matters most to your audience and showing them how you got there through data storytelling techniques like visualizations or graphs. 
• You will need basic knowledge of statistical methods like regression analysis or linear regression models so that your findings make sense to your reader.