Understanding Data Visualization
When discussing data visualization, the impact technology has had on the subject is vast. Data visualization as a concept has been around for centuries now, dating back to the creation of maps. Since that time, not only has there been large technological advancements, but there’s also been an advancement and desire for data. Whether you’re watching the news or enjoying your favorite sport, there’s graphics and stats plastered all over the screen, all telling different stories and using different tools.
In theory this sounds like a positive and in many ways it is. Providing more ways to share information with the general public is good and it’s a good way to engage and inform the audiences. With the technology and talent many of these programs have, we also have some great ways of presenting this data that are both entertaining and informative
A good example of data being used in modern sports media by baseball analyst Brian Kenny
With how vast the subject has gotten through, it’s also shown some of the cracks that may come from all the advancement.
One of the big issues with data visualization being so prevalent, is that it allows certain individuals to create misleading visuals that deceive their audience. For example, the COVID-19 pandemic is currently having a major impact on the entire world. Knowing this, it’s important for people to have access to understandable, accurate data, so they can properly prepare and adjust to this virus wherever they happen to be. It becomes problematic though when viewing stories such as these. It’s not very difficult to find articles such as these and shows a major issue facing data visualization in the modern world, which is a lack of trust.
Humans can often be affected by their emotions in a discussion, as described in the appeal to emotion. Data on the other hand is supposed to be concrete. Data is simply a group of miscellaneous facts, with data visualization being a way of organizing these facts so that they have meaning. When people don’t trust the data though, or when they say it’s being shown in a misleading way, it takes away when of the main strengths of data visualization and turns it into another argument based on emotion.
Beyond certain groups being misleading, data design has also gotten over complicated in some regards. With all the options available, it’s easy to want to try to cram in as much data as possible, though many times this just ends up leading to the data being muddled. This has a little less to do with technology and more so just a general design concern, though technology still has a hand in it
What makes a “good chart”
In a way, what makes an effective showcase of data is subjective. Nearly everyone is going to have slightly different views or opinions on a visual artifact, so it’s pretty much impossible to find an objectively correct way to do it. This doesn’t mean that there aren’t certain styles and methods that have a more broad appeal than others though. I’m going to run through a few different charts and explain why they work for me personally, as well as the appeal they’ll have to the general audience.
This chart is relatively simple but effective in my view. From first glance, you have a clear understanding of what the chart is trying to say, as well as what the largest numbers for the subject are. The colors are also very effective, as they’re easy to differentiate (especially for a colorblind person like myself) and consistent throughout the whole graphic. The only issue I have with the design is the text for “models” and “goals”. I feel the sideways type makes it difficult to discern from a glance and doesn’t have much purpose as there’s enough space to have them laid down like the rest of the type in the graphic
For this next one, you’ll need to click the link to fully view the chart, but I feel it’s still a very engaging and effective method of showing the outbreak of the pandemic. When you first scroll down you can quickly see it’s a globe, giving you a basis for what the graphic will look like.
The first part of the chart you'll see
As you continue to scroll down, more and more red dots fill the screen, as well as countries changing shade, giving an easy to understand example of how the virus is spreading. The countries turning lighter gray are getting their first set of cases, while the red dots show how far the cases themselves are spreading. There’s also a consistent tracker on the top left, showing the number of days, cases, and deaths.
How the chart looks after scrolling down a few days
Overall, this chart presents a quick and informative view of the virus and how it’s spread throughout the world. The only real issue I have is that you’re locked into the default location of the chart, preventing you from viewing the spread in certain countries at certain days.
As said before, there’s no objectively correct way to showcase data. What these charts show though, is that there’s some general guidelines that will make the data shown effective and engaging. Factors such as proper color differentiation, clear differences in data sets, easy to read information, and being relatively intuitive and easy to grasp make even the most complicated sets of data much easier for the general viewer to understand.
Clarke, Seán, et al. “How Coronavirus Spread across the Globe - Visualised.” The Guardian, Guardian News and
Fenalosa, Aldo, and Erwin Hilao. “The Kings Of Kicks.” - Information Is Beautiful Awards, 2019,
Flores, Rosa, and Joe Sutton. “Florida and Georgia Facing Scrutiny for Their Covid-19 Data Reporting.” CNN, Cable
News Network, 20 May 2020, www.cnn.com/2020/05/20/us/florida-georgia-covid-19-test-data/index.html.
Originally published by AnyChart. “What Is Data Visualization? Definition, History, and Examples.” Hacker Noon, 11