How I transform my data visualisation in form of a story

Using Design Thinking to craft data storytelling

9 min readApr 2, 2022

A look back…

Recently, I wrote a blog on how I used Tableau to create a visual dashboard showing the diverse pizza topping preferences of Britain people. Let’s take another look at the visualization:

As the (biased) author, I believe that the analysis and design are sound enough, but not with the storytelling. Specifically, despite a quite appealing design structure (as you can tell that viewing graphs alongside a pizza slice is pretty intriguing…!), it does not provide a comprehensive picture of the preference discrepancy between men and women in terms of toppings popularity. In other words, the graphic lacks both a clear message and a narrative about the gender disparity.

Thus, in this blog, I’ll propose a more novel approach based on storytelling principles and design thinking concepts for effectively communicating the hidden message hidden in data and for better understanding the gender-specific pattern of pizza topping preferences in the United Kingdom.

Visit my Tableau dashboard here.

Storytelling — A humankind’s integral part

Before moving on, I would like to reflect on the importance of narrative in human history. For as long as humans have been carving shapes into stone as a means of illustrating everyday phenomena to as recent as our efforts, to unearth the truth behind many life myths using technology, we’ve been looking for a way to communicate our need — to tell a story.

Photo by The New York Public Library on Unsplash

In the data space, data visualisation is an essential part to realise the ideas that a data analyst/data scientist would like to tell from what he or she has discovered. And a good visualisation cannot be done without data storytelling!

The Dataset — Where can we get the insights?

The dataset used in this article is the same one as the previous blog. Let’s take a glimpse once again at the dataset:

Dataset: Pizza Preference in UK 2017 (YouGov, 2017)

The Principles — What should we keep in mind?

According to a recent Forbes article (Dykes, 2022), data storytelling is a method for presenting data insights in a logical and compelling way, and it combines three main components:

Data
Visuals
Narrative.

As depicted in the above figure, when the narrative is going with data, it explains to the audience what is the visualization is about and why it is essential to pay attention to the insights. When visuals are added, they can help enlighten insights that are hidden without charts or graphs. Finally, when narrative and visuals are combined, it helps to connect the audience with the message, or in other terms — engage.

Based on these principles, I will use them to further improve the storytelling aspect of our existing charts.

The Process — How can we do it?

If you are still reading, I know you are probably getting bored of the theoretical side of this blog. Instead, let me tell you a story.

Before mitigating to data, I came from a business background and graduated with a bachelor's degree in Marketing. And one of my most favourite and valuable things that I’ve learned throughout my 4 years of college is how to come up with a designer mindset without actually going to any of the design schools: The Design Thinking concepts.

“Design thinking is a human-centered approach to innovation that draws from the designer’s toolkit to integrate the needs of people, the possibilities of technology, and the requirements for business success. ”
(Tim Brown — IDEO)

There are a variety of Design Thinking models that currently exist, but what I would like to come across today is called the 3I model, which stands for 3 stages — Inspiration, Ideation, and Implementation.

3I model — Design Thinking process (illustrated by me)

Step 1— Inspiration

Different roles/industries will come up with different tasks in this step. In my case, the inspiration stage is all about:

Understanding the context — including knowing your audience, the purpose of creating the chart, and;
Be inspired! — Collect information (design material) & conduct research about the chosen topic.

My purpose to create data visualisation this time is to dig deeper into the different trends of pizza topping preferences slicing by gender. In terms of audience, the visualisation will be relevant for personal interest, in particular for those curious about how their personal preferences shape differ from others of the same/different sex. Furthermore, this can also be helpful for pizza companies to further understand their customer preferences and provide better sale strategies.

To come up with the final dashboard layout, including the storytelling components, I have conducted several types of research related to different aspects such as design elements, colours, human psychology, etc. to help with the topic acknowledgment. Some of the best resources that I came across can be found as follow:

Choose the right graph to communicate your data: https://www.data-to-viz.com/
9 Fundamental Design Principles: https://www.idashboards.com/blog/2017/07/26/data-visualization-and-the-9-fundamental-design-principles/
How to tell a story with Data: https://mihikamahajan.medium.com/how-to-tell-a-story-with-data-7ffe137d5c29

One of the most evident things that I have observed from a set of internet pizza inspo or food, in general, is that hot colours such as red, yellow, orange, etc. have always been among the top choice of designers when constructing an advertisement of food posters.

This is also confirmed by an article by Tracegains in 2021 where the author claimed that a set of hot colours can be used to improve our taste perception (Storey, 2021). Explicitly, the article also mentioned how people can associate different colours when it comes to food such as Red — Appetizing, Yellow — Happiness, Orange — Satisfying/Energizing.

My Google photo search for the keyword “pizza poster” (4th April 2022)

Another essential part that I’ve done in this stage is to observe, learned and be inspired by other works. If you might ask the question “Why didn’t you come up with your own work?” then you gotta know my working philosophy — “Work Smart, Not Hard” (or you can say that is just another way for me to get away with my “plagiarism”).

Step 2— Ideation

After having all the resources that I needed, the next step is where we started to play around and put all of our ideas together. I’d like to call it the brainstorming/ideation stage.

I have come across quite a lot of amazing graphs are done by Tableau users, and as I was totally stunned by the greatness of their design, it was really helpful to help me shape the ideas of how I should construct my final graphs. For better self-reflection afterwards, I have collected all graphs that are the most relevant to my idea and consolidated them into a single slide for a better view.

My summary decks for data visualisation work of Tableau users (feel free to take the credit if yours is included!)

Another method of getting a sense of how your visualisation should look is through individually developing your own “mood board”.

“A mood board is a type of visual presentation or ‘collage’ consisting of images, text, and samples of objects in a composition. It can be based on a set topic or can be any material chosen at random. A mood board can be used to convey a general idea or feeling about a particular topic.” — (Wikipedia, 2022)

I ended up choosing the combination of 3 colours: red, yellow, and orange. My finished mood board is presented as follows:

My “mood board” for dashboard inspiration

Everything comes up so far is going great. However, the idea is just an idea if it only exists in our heads. It’s important to write down and partially recreate the look of our dashboard with a prototype. I don’t own any fancy design tools so pardon me if I’m only doing this in an old fashion way — sketching.

Implementation

Before, the Implementation phase is a real kick in the stomach. If I can capture how it used to be like in one picture, the image taken from the series The Simpson is exactly how I felt before:

Thanks to the Design Thinking process, I have an almost full picture of what my ideas should be and it did take off a lot of burdens that it used to have during this stage. Based on the sketch and the mood board that I made earlier, visualising it using Tableau shouldn’t be too difficult from now on.

It’s also important to mention that a collection of testing, asking for feedback and then testing again is also a key to improving my visualisation in the best way. Walla! The part we’re all waiting for — My final output:

Compared to the previous dashboard, I have made some additional changes:

Butterfly over stack — Instead of using the stacked bar chart to display the gender disparity in terms of pizza topping preferences, I have changed it to the butterfly chart to provide a better snapshot of the difference between men and women. I have also included pie charts depicting the top 5 most popular pizza toppings with the comparison by gender.
The message — In order to better communicate the key message, I have replaced the viz title description to communicate the main insight taken out of the graph. A small subtitle section is also provided below the title to give the audience ideas of the context and my personal preference compared to the findings.
The colours — In this workbook, I have assigned the colour of females as Orange and Men as Yellow. Therefore, to ensure consistency throughout the data viz, every typo giving the information about each gender will go after the assigned colour palette. This is to help the audience better identify the colour association of each sex communicating in the graph.
Ooh…more info, intriguing! — There is nothing better to learn more things than just looking at blunt statistics. Thus, I have added a few fun facts in addition to the main graphics to increase viewer interest when skimming through the dashboard.
Visual elements — An appealing graph can be done without appealing visual elements. After all, data visualisation and storytelling is an art, and though people said “less is more” — it definitely wouldn’t hurt to include visual elements to look my dashboard more interesting plus can illustrate the information.

The Result — What insights do we get?

As transparent from the butterfly chart, men are much more likely than women to like meat on their pizza. They are also much more likely to enjoy chillies and jalapenos, whilst women are noticeably more likely to enjoy spinach. Another noticeable trend observed from the pie charts is that 3/5 of the top favourite pizza toppings are male-preferred. While explaining the origin of this phenomenon can be varied, one of my hypotheses is that the result is influenced by the number men participants and thus, the data itself can provide biased outcomes.

Key Takeaways

There is a link between data, information, and storytelling. There might even be a moral imperative to recognizing and enjoying this relationship if only to ensure we’re telling each other genuine, fact-based stories when it’s essential we do so. How do we know if a tale is true? We think critically about the information we’ve heard. If there are questions, we dive deeper to evaluate the data, the way it was acquired, and the information it apparently supports. Telling stories with integrity is vital and from what I can tell, it starts with the analytical rigour that data and data collection methodologies bring.