Leave your response as a comment on this post by the beginning of class on February 14. Remember, it only needs to be three or four sentences. For these weekly questions, I’m mainly interested in your opinions, not so much particular “facts” from the class!
Here is the question:
Take a look at the Hoven article from this week (“Stephen Few on Data Visualization: 8 Core Principles”). Which one of the eight principles do you think is most important? Why?
I believe the most important core principle of data visualizations is asking “why.” Most people have the capacity to dissect what is occuring in a good visualization, however not many people take the time or effort to understand why such results are happening. Part of the reason visualizations are created is to attract people into asking “why”–but if people aren’t doing that, then they are simply mindlessly consuming useless data, and the purpose for the data and visualizations become lost.
I think the most important principle is simplify without oversimplifying. Often times when I read a data-driven article, my eyes look for a data visualization that can help me quickly understand what the article is trying to communicate. When a data visualization can capture the “essence” of data, it really helps boil down and even strengthen the argument that the article is making. Plus, it saves time! That’s why I think tools like Tableau are extremely powerful in the business world. When we’re inundated with too much information, a data visualization that can quickly communicate the idea within 5 seconds is superb.
I think the most important principle for data visualizations is to ask “why.” Data visualizations are made to help us interpret data by looking at them in different and usually more visually appealing formats. Once you have both analyzed and formatted the data the next step is to attempt to find the cause behind them. There is almost always an explanation for why the data are the way they are so I think it’s important that data visualizations include this next step or at least provide some insight into the reasoning behind it. With access to all these data, and with more and more resources and tools being created to display data, what should naturally come next is some sort of deduction towards the root of the pattern the data display.
I believe that the most important principle on data visualization would be to explore. The reason for this is that without really looking at the data, we may miss an important factoid or reasoning for the data points to come in like this. If we don’t explore the data and discover things, our conclusions may only scratch the surface and may become limited to what we already know or assumed.
I think the most important data visualization is “explore”. I believe that a great infographic will give the viewer the necessary data and background info for them to draw their own insights as well as encourage their intellectual curiosity about a subject. You will never be able to fit every piece of relevant data on one infographic. It should give you the main points and build a foundation for you to explore solutions and ideas.
I believe “be skeptical” is important because accepting data without wondering what went into it is very dangerous. The best way to understand something is to question where it came from and why it is the way that it is. In order to do that, skeptically looking at the answers you are given and trying to understand how they came to be make certain that you know your data is providing good answers. By questioning the data, we can get the most out of the data.
I believe that the most important principle is the explore principle. When someone constructs a data visualization, they construct in a way that explores the data allowing the viewer to discover things. The art of exploring a data visualization will heighten its capabilities of visualizing data and will allow the viewer to gain more from it.
I think the point, “Be skeptical,” is the most important principle. It is important to understand your findings can be wrong, and to always work with this knowledge at the forefront of your mind. Working with this in mind, you are less likely to become to emotionally invested in your work, and will have a more level minded approach to your data.
In my opinion, the most important principle from the article is to be skeptical. Being skeptical of the data deals not only with the conclusions the visualization presents, but also with the sourcing and subject of the data itself. These are important in identifying whether there are any agendas skewing the results or whether the data was gathered in a reasonable way. With the improvements to data analytics tools and the abundance of sources to help determine the reliability of the data, a healthy skepticism should help reduce how often someone is misled.
I agree with a number of my colleagues above: “be skeptical” is perhaps the most important to me of the eight listed.. In addition to the reasons given and already expounded upon (verifying collection methods, considering the influence of an agenda, etc.), I think it’s critical to be skeptical of the data because failing to do so, unlike some of the other core principles, does not simply make the data potentially less useful. A failure to be skeptical might make us better off without having the result in the first place. Misleading or inaccurate data can be dangerous, and a skeptical attitude is one reasonable safeguard against that.
(I know I’m late with this question, but I wanted to respond anyway. I somehow completely missed this question being posted and didn’t realize that it was up until just now.)
I think the most important principal of data visualization is “be skeptical.” The other principals, while also important, seem to be kind of straightforward and simple. They are all factors that anyone serious about data visualization is going to pay attention to. Being skeptical, however, is something that in my mind even people experienced in data visualization can get wrong. Trusting all of the results you get from data or only looking for certain results in a data set are problems that anyone can have, and are often not taken as seriously as they should be.