It’s obvious that when it comes to S&M in data visualization, you are talking about style and manipulation of data. What else could it mean, right?
I had the opportunity to review the Wall Street Journal‘s data visualization style guide. I found the WSJ’s style guide to be somewhat help, somewhat obvious, and somewhat dated.
This was my favourite sentence:
“Admit colors into charts gracefully, as you would receive in-laws into your home.”
It doesn’t get better than that.
There are so many factors that data journalists must consider when it comes to visualizing data that it can get a bit overwhelming. I thought that some of the WSJ’s tips were obvious – use clear typography; certain colors and charts visualize the data better than others, etc. Then I tried to create a Google fusion map of a data set on New York City public schools that are undergoing construction. It is not that easy to manipulate and design data so that it best reflects the story you are trying to tell. In fact, I am still unsure of how to present the school construction data set.
It made me realize that the most important element in data visualization is the editing process.
The WSJ lists four aspects in data visualization: research, edit, plot, review. I think that editing is key. Without fixing your raw data set, your audience may not understand what your key point is. You have to edit your data just like you have to write a nut graph for your story. That’s where I went wrong with my Google fusion table on school construction. I wasn’t sure what I wanted to emphasize from the raw data, so I didn’t know how to edit it properly.
Editing your data also includes, like the WSJ’s style guide suggests, making sure that the data is accurate. You can’t work with flawed data. I wasn’t sure whether the Department of Education’s data was accurate because, under the construction expenses, some amounts had decimals and others didn’t. I don’t think a playground would cost in excess of 500,000 dollars. If it does, then I have a different story to work with.
Government data tends to full of jargon. I think it took me a few months to actually understand some of the terms used when I used to work for the Ontario government. In that sense, editing raw data also means that it is my responsibility as a journalist to translate the data to make it understandable for readers. Doing so may require contacting the original source of the data to clarify any misunderstandings. Data journalism doesn’t mean working behind a computer. The WSJ’s stresses credibility of information and I couldn’t agree more.
So, another data golden rule: Every journalist should practice data visualization S&M.
On a side note, I wonder if the WSJ’s style guide is up-to-date? I wonder if design elements change if the data visualization is online and interactive? And do the style guides for data visualization change depending on the news organization’s region? I am curious to know if the WSJ altered its style guide to reflect cultural meaning for its Asian edition.