Data Visualization: an inexact science

“Increasingly, managers and professionals are expected to make their arguments and decisions based on data and visualizations.” Hugh Watson states in a Business Intelligence Article titled Data Visualization, Data Interpreters, and Storytelling. I’d have to agree. In fact, there was a section in a presentation I recently gave at the Northeast Annual Giving Conference devoted to using data (in the form of charts and graphs) to leverage buy-in from colleagues.

The presentation was on the topic of utilizing data to drive annual fund solicitation performance. One of the slides we showed was a scatter graph of the performance of our various solicitation emails in 2020. The graph had been prepared in Tableau by a company that we are working with. When my colleague and co-presenter showed me the table she wanted to use, I clammed up. “Just… make sure we explain what they’re looking at” was my best advice.

Watson echoed my concerns about data visualization that may be confusing to those who are not fully trained in how to read the image. “I have mixed feelings about some of the newer visualizations—such as treemaps and starbursts—and when and how they are used. Many of them result in screens that “pop” but are difficult to interpret.” The whole point of data visualization is to allow the user to more easily understand huge swaths of information – like all four-hundred-something emails we sent in 2020. But, when the format of the visualization is too cluttered, or just too unrecognizable, it creates a disconnect for the viewer.

A beginner’s user guide to the program Tableau reiterates this sentiment. “Effective data visualization is a delicate balancing act between form and function. The plainest graph could be too boring to catch any notice or it make tell a powerful point; the most stunning visualization could utterly fail at conveying the right message or it could speak volumes.”

Many who perform knowledge work are likely familiar with data visualization in many forms from the traditional (pie charts, bar graphs) to the more contemporary (heat map, word map, or scatter graph.) But as the Tableau beginner’s guide states, data visualization is a balance between form and function, and when that delicate balance gets tipped toward form, we begin to enter the realm of data storytelling.

Data storytelling can take on many forms from involved infographic’s like Density Design’s work with Greenpeace about sustainable fishing (left)to the work of German artist Sarah Illenberger (Right) who uses mundane objects to explore and explain data.

It’s Sarah Illenberg’s work that served as my inspiration to use an everyday object to create a data visualization – One Million Eggs.

With three backyard ducks, we have a glutt of duck eggs. There were about 50 sitting on my counter when I started thinking about what I wanted to use to create my data visualization piece. The eggs were a natural starting place… but what would my eggs represent? Well… eggs, of course.

The human female has approximately one million eggs at birth, the most she will ever have. By puberty she will have half that number, and will loose approximately 1,000 eggs every month thereafter until menopause. In this graph, one duck egg was equal to 100,000 human eggs.

The graph to the left was the inspiration for my egg graph, but I did find myself taking a few artistic liberties to make the numbers work.

My original plan was to create a clean decline by removing one egg from each column and then adding age corresponding age numbers at the bottom but it quickly became apparent that would be unwieldy and there would be too much data that wasn’t named. I was afraid the columns that didn’t correspond directly to an age would confuse the viewer.

I also added the grey eggs as a secondary piece of data – the number of genetically abnormal embryos rises substantially as a woman ages.

Using every day objects to tell a data story was an interesting exercise, but the creator must be very cognizant of the objects chosen and whether they can properly express the data in their form. I had a very clear idea in my head when I set out to create this piece, but once I started laying out the eggs, it became apparent that they would need to be a representation of the data rather than an exact visualization of it. How important it is for your graphic to represent the exact numbers will help determine what type of visualization you need to create – a bar graph in excel, or an egg graph on your kitchen table.

3 thoughts on “Data Visualization: an inexact science

  1. Drew Furtado says:


    This blog post was awesome! As a male, I have never thought about how many eggs the female body produces. The graphic was excellent at demonstrating the total egg count of females.

    My one suggestion would be to include a title on the graphic, that way it’s easy to understand what data you’re representing. For me, I had to read the blog post to fully understand what the graph represented.

    Love it so much, Jenny!


  2. melindalgarza says:

    Hi Jenny!

    I think you did a great job of tying in the readings to your blog post. You were able to inform without over-simplifying the information. Also, the way you were able to share the experience you had on the topic made it sound more personal and not just like a blog post about analytics or statistics.

    When you started talking about the eggs on your kitchen counter, I didn’t know what route you’d take, but was happily surprised with what you decided! It is such an important topic and it shouldn’t be taboo to discuss.

    Grammar notes:
    – Try to be consistent in the way it’s presented. “Design’s work with Greenpeace about sustainable fishing (left)to the work of German artist Sarah Illenberger (Right) who uses mundane objects to explore and explain data.” —- “left” is lowercase and the “Right” is capitalized. This is obviously a very minuscule edit, but it’ll make the writing look neater overall.
    – “By puberty she will have half that number, and will loose approximately 1,000 eggs every month thereafter until menopause. In this graph, one duck egg was equal to 100,000 human eggs.” —– In this case, it’s lose, not loose. (Again, just a very minor typo)

    I thought it was such a great addition to add the gray eggs, very creative!

    In comparison to the graph for your inspiration, I think you did an amazing job of displaying your data. I’m definitely looking forward to seeing more of your work!

    – Melinda


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