Data Visualization Is Data Visualization Is Data Visualization

The principles and practices of data visualization do not vary from one domain to another. They are the same. Data visualization applied to business differs only from data visualization applied to education (or healthcare, or government, or various branches of science, or any other domain you can imagine) in that each domain has its own data that must be understood before it can be visualized effectively. How the data is visualized, however, does not vary from one domain to another. All domains pull from the same repository of visual representations and, to work effectively, follow the same design principles and practices. While it is certainly true that some data domains might routinely rely more heavily on particular charts than other domains, that difference does not constitute a separate branch of data visualization. If you’ve developed expertise in data visualization while working in finance and you suddenly take a job working in healthcare, you will need to learn about healthcare, but not anything new about data visualization that is unique to that domain.

Over the years, as a data visualization practitioner, author, consultant, and teacher, I’ve applied my skills to many domains. To do this effectively, I had to learn enough about those domains to make sense of the data, but what I then did to visualize the data didn’t vary from one domain to another. From time to time, people who worked in a specific domain asked if I would write a book or teach a course about data visualization for their domain in particular. Would it make sense for me to write a new version of my book Show Me the Numbers that is specific to the needs of education, healthcare, or marketing organizations? The lessons that the book teaches about chart selection and design for communicating quantitative data effectively are illustrated throughout with examples drawn from multiple domains; examples that can be easily understood by everyone. A separate version of the book for each domain isn’t needed. You could certainly argue that marketing professionals might prefer to only see data visualizations that are based on marketing data when learning the skills, but would that provide them with any real benefit compared to familiar examples from multiple domains? I don’t think it would. In fact, using examples from multiple domains reinforces the fact that data visualization applies to all domains equally and in the same manner—the skills are transferable—which is a useful reminder. During the early stages of the learning process, focusing on the concepts and skills of data visualization rather than on the data domain is appropriate, even if you only plan to apply the skills to a single domain.

The first edition of my book Show Me the Numbers almost exclusively featured business examples. I chose to do this initially because the business examples that I created (e.g., graphs that featured revenues or expenses) were easy for any reader to understand. As a consequence, however, every once in a while someone would describe Show Me the Numbers as “data visualization for business,” which drove me nuts, because it artificially and unnecessarily limited the book’s audience. For this reason, when I wrote the second edition, I was careful to mix in examples drawn from multiple domains.

As a data visualization professional, it is perfectly reasonable for you to focus on a particular data domain if you wish because increasing your expertise in that domain will make you a better visualizer of its data. Just bear in mind that your visualization skills in particular, as opposed to your data domain expertise, are entirely transferable. When I first started teaching public data visualization workshops many years ago, I quickly observed that classrooms filled with people from various domains, rather than workshops that I taught privately for individual organizations, offered a real advantage to my students. Sharing experiences, discussing the material, working together in exercises, and even commiserating about the challenges that they faced when visualizing data, was richer in diverse groups drawn from various domains.

Data visualization is data visualization is data visualization. If you learn the skills well, you can apply them broadly.

4 Comments on “Data Visualization Is Data Visualization Is Data Visualization”

By Carlos Barboza. July 17th, 2020 at 10:50 am

Hi Stephen,

Thanks for reinforcing this point “Focus on learning the concepts and skills of data visualization first, before the data domain is appropriate”. I see many new comers to the data world jumping into making flashy charts and dashboards yet, the lack of knowledge about color theory, gestalt laws, etc. is highly evident.

Carlos Barboza

By Nigel Hawtin. July 20th, 2020 at 2:12 am

Hi Stephen
Short and sweet reply to this blog…totally agree.
Thank you.

By Jim. August 29th, 2020 at 3:13 pm

I like the overall point you’re making, but would suggest that differences between disciplines can reasonably arise based on the different types of data that are more common. The example that came to mind when you mentioned moving from finance to healthcare was the high proportion of data in finance that exists as time series data. If you are used to doing lots of charting of stock prices over various timescales, you may find that in healthcare you end up using quite a different mix of visualisation types.

By Stephen Few. August 29th, 2020 at 3:30 pm

Hi Jim,

I absolutely agree with you. What you’re arguing is what I was trying to say when I wrote: “While it is certainly true that some data domains might routinely rely more heavily on particular charts than other domains, that difference does not constitute a separate branch of data visualization.” It is true that some data domains rely more heavily on particular types of charts than others due to the nature of their data and the nature of what they must understand from the data. In fact, some domains use charts that are somewhat unique because of their unique needs. Those rare cases, however, which do not constitute distinct branches of data visualization.

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