A guide to creating data visualizations for data storytelling

8 min readJan 23, 2023

Introduction

Data visualization is the process of creating graphical representations of data in order to better understand and communicate information. This can include a wide range of visual formats such as charts, graphs, maps, and diagrams. The goal of data visualization is to make complex data sets more easily accessible and understandable to a wide range of audiences.

Data visualization plays a crucial role in data storytelling. It allows us to take raw data and turn it into an engaging and informative narrative. Data visualizations can be used to highlight patterns, trends, and insights that might not be immediately apparent from looking at raw data alone. They can also be used to communicate complex data in an easily digestible format, making it more accessible to a wider audience.

The purpose of this guide is to provide a comprehensive overview of how to create effective data visualizations for data storytelling. We will cover everything from choosing the right visualization for your data to designing effective visualizations and best practices for data storytelling. Whether you are a data analyst, a marketer, or simply someone who wants to learn more about data visualization, this guide is for you. We will be providing practical tips, tools, and resources to help you create compelling data visualizations that will help you communicate your data effectively.

Choosing the right data visualization

A. Types of data visualization

There are many different types of data visualizations to choose from, each with their own strengths and weaknesses.

Some of the most common types include:

Bar charts: These are used to compare the values of different categories. They are useful for showing changes over time or comparing different groups.
Line charts: These are used to show changes in data over time. They are useful for showing trends or patterns in data.
Scatter plots: These are used to show the relationship between two variables. They are useful for identifying patterns and outliers in data.
Other types of data visualizations include pie charts, histograms, heat maps, etc.

B. Choosing the right visualization for the data

Choosing the right visualization for your data is crucial to effectively communicating your information.

Some factors to consider when choosing a visualization include:

Nominal vs. ordinal vs. quantitative data: Nominal data is data that can be divided into categories. Ordinal data is data that can be ranked in some way. Quantitative data is data that can be measured. Each type of data is best suited for different types of visualizations.
Data density and distribution: The density and distribution of your data will also affect which visualization you choose. For example, a scatter plot may be more appropriate for data that is widely distributed, while a bar chart may be more appropriate for data that is more tightly packed.
other factors to consider include the complexity of the data, the size of the data set, and the audience for the visualization.

It is important to know that there is no one right visualization for all data, it is important to experiment with different types of visualizations to find the one that best represents your data and tells the story you want to tell.

Designing effective data visualizations

A. Principles of good design

Designing an effective data visualization requires attention to detail and a keen understanding of the principles of good design.

Some key principles to keep in mind include:

Simplicity: Keep your visualization simple and uncluttered. Avoid using too many colors, fonts, or elements that could distract from the main message.
Clarity: Make sure your visualization is easy to understand and interpret. Use clear labels and annotations to guide the viewer through the data.
Other principles of good design include using hierarchy, alignment, and contrast to create visual interest and guide the viewer’s eye through the data.

B. Color theory and usage

Color is an important element of data visualization, and it can be used to draw attention to important data points and guide the viewer’s eye through the data. However, it is important to use color carefully and intentionally, as it can also be a source of confusion and distraction.

Some things to consider when using color include:

Using colors that are easy to distinguish, especially for those with color vision deficiencies.
Using colors that are consistent with the data being represented.
Providing a key or legend to explain the meaning of colors used in the visualization.
Avoiding the use of too many colors or using colors that clash.

C. Titling and labeling

Titles and labels are an essential part of any data visualization, as they provide context and make it easier for the viewer to understand the data.

When creating titles and labels, make sure they are:

Clear and concise
Accurate and informative
Easy to read
Placed in a logical location on the visualization

D. Creating a narrative with data visualization

Data visualization is an effective tool for creating a narrative with data. By highlighting patterns, trends, and insights in the data, data visualizations can be used to tell a story about the data.

To create a narrative with data visualization, consider:

Using a clear structure to guide the viewer through the data
Using annotations and captions to provide context and additional information
Highlighting important data points or trends
Creating a clear call to action or conclusion to the visualization.

Creating a narrative with data visualization is not only about the design but also about the data you choose, the message you want to convey and the audience you want to reach.

Tools and software for creating data visualizations

A. Popular tools and software

There are many different tools and software available for creating data visualizations. Some of the most popular options include:

Excel: Excel is a widely used spreadsheet program that can be used to create basic data visualizations such as bar charts, line charts, and pie charts.
Tableau: Tableau is a powerful data visualization tool that allows users to create interactive visualizations and dashboards. It is commonly used by data analysts, business intelligence professionals, and other data-focused roles.
R and Python libraries: R and Python are programming languages that have many libraries for data visualization, such as ggplot2, matplotlib and seaborn. These libraries are powerful and flexible, but they do require some programming skills to use.
Other popular tools and software for creating data visualizations include Power BI, QlikView, and D3.js.

B. Pros and cons of each tool

Each tool has its own strengths and weaknesses, and the best choice will depend on your specific needs and skill level. For example:

Excel is relatively easy to use and widely available, but it has limitations when it comes to creating more advanced visualizations.
Tableau is a powerful tool for creating interactive visualizations, but it can be more difficult to use and may require a significant investment.
R and Python libraries are flexible and allow for more advanced customization, but they do require some programming skills.

C. Tips for using the tools effectively

Understand the data: Before you start creating a visualization, make sure you understand the data you are working with. Understand the structure of the data, the types of data, and the main trends or patterns in the data.
Choose the right tool for the job: Make sure to choose the right tool for the type of visualization you want to create. Excel is good for basic charts and graphs, Tableau is good for interactive dashboards and R or Python libraries are great for more complex visualizations.
Keep it simple: Simple visualizations are often the most effective. Avoid using too many colors, fonts, or elements that could distract from the main message of the visualization.
Test and iterate: Don’t be afraid to experiment with different visualizations and make adjustments as needed. It’s often helpful to get feedback from others to see how they interpret the visualization.
Keep in mind the audience: Consider the audience for the visualization and design it in a way that will be most effective for that audience.

Best practices for data storytelling with data visualization

A. Importance of context

Context is key to effective data storytelling with data visualization. By providing context, we can help the viewer understand the data and the story we are trying to tell.

Some things to consider when providing context include:

The source of the data and any relevant background information
The time frame of the data
The limitations or assumptions of the data
The intended audience and purpose of the visualization

B. Avoiding common pitfalls

There are several common pitfalls to be aware of when creating data visualizations for data storytelling:

Using the wrong type of visualization: Using the wrong type of visualization can make it difficult for the viewer to understand the data.
Overcomplicating the visualization: Overcomplicating the visualization with too many colors, fonts, or elements can make it difficult for the viewer to understand the data.
Not providing enough context: Not providing enough context can make it difficult for the viewer to understand the data and the story you are trying to tell.
Not testing the visualization: Not testing the visualization with a variety of people can make it difficult to know if the visualization is effective.

C. Ethical considerations

Data visualization and data storytelling also raise ethical considerations, such as:

Transparency: being transparent about the data source, methodologies and limitations of the data
Bias: avoiding bias and ensuring the representation of data is fair and accurate
Privacy: protecting the privacy and anonymity of data subjects

D. Tips for presenting data visualizations

Keep it simple and clear: Make sure the visualization is easy to understand and interpret.
Tell a story: Use the visualization to tell a story about the data.
Use annotations and captions: Use annotations and captions to provide context and additional information.
Highlight important data points or trends: Highlight important data points or trends to guide the viewer through the data.
Practice: Practice presenting the visualization to make sure it effectively communicates the data and story.
Be aware of the audience: Consider the audience for the visualization and present it in a way that will be most effective for that audience.

By following these best practices, you will be able to create effective data visualizations that tell a compelling story about your data.

Conclusion

In this guide, we have covered the importance of data visualization in data storytelling, how to choose the right visualization for your data, how to design effective visualizations, tools and software available for creating data visualizations, best practices for data storytelling with data visualization and considerations when presenting data visualizations.

To continue learning about data visualization and data storytelling, there are many resources available online such as:

Books: “Data Visualization for Storytelling” by Ryan Sleeper, “Storytelling with Data” by Cole Nussbaumer Knaflic
Online tutorials and courses: Data visualization courses on Coursera and Udemy, Data visualization tutorials on YouTube
Blogs and websites: FlowingData, Information is Beautiful, Data Viz Project

Now that you have a solid understanding of how to create effective data visualizations, it’s time to put it into practice. Start experimenting with different tools and techniques and see what works best for you. Remember that creating great data visualizations is a process of iteration and improvement, so don’t be afraid to make mistakes and learn from them. And always keep in mind the purpose of the visualization and the audience you want to reach.