We were incredibly curious to hear about the origins of 'Data Stuff Plus,' so we asked Pastor about the inspiration behind his project. "Coding has always been a hobby for me, and generally I like to work on larger projects (websites, and tools etc.) but it is hard to stay motivated when the end product is far away and there is not a lot of feedback," he told Bored Panda.
"I started to do mini projects to play with data and generate some of my own graphs and charts that I could share so that way I can at least get the sense of completion for a few things. The only downside is that I don't have a ton of time to focus on it (work, kids, life) so I have been reposting interesting stuff I find for a lot of it," Pastor said.
"To keep on it, I just sort of note anything interesting I find online when perusing and keep an ongoing list. That and it helps when random people find out it is my account and say they follow it! I'm like, damn, that's crazy."
Bored Panda asked the founder of 'Data Stuff Plus' for his opinion about what lies at the core of truly good data representation.
"I think it has to be simple and visually engaging. Especially on Instagram and social media in general, people are not going to even stop if the chart doesn't make them stop. That visual grab is the first thing needed to engage. Then, if the chart tells a succinct visual story I think it becomes engaging," he explained.
"Anything too complicated I find people can't/won't make the time to really dig in. If there is more nuance to the data that requires a bigger investigation I like platforms like Medium."
Pastor also wanted to clear up a few details. He pointed out that 'Data Stuff Plus' had now mostly turned into a reposting account. "So, the majority of the things on there are now the work of others. I still think it is great to spread the word on interesting data, but I would much rather do my own stuff if I had more time," he said.
"I have been posting more AI stuff recently and I get some pushback in the comments. The reason I am doing this is that AI is coming and progressing very fast and that people need to be exposed to it now when it is still discernible from reality. Almost like a way to train your eyes and mind to sniff it out while it is early enough."
Visual clarity is so, so important. Especially in this day and age when people’s attention spans are short and at a premium. So, the more clearly you present your data in a chart, graph, or map, the quicker and better your message gets across.
Typically, it’s a good approach to use fonts that are easy to read and contrasting colors that don’t get lost in the background. You want to create a user-friendly experience. In terms of the text itself, it should be edited down to the essentials where it’s short and snappy but fully encapsulates the main idea.
According to one The Economist post on Medium, there are numerous ways to mess up when trying to visualize data. Probably the biggest mistake you can make is presenting your data in a misleading way.
For example, you might (accidentally) draw bars that don’t quite match up with the actual size of the numbers. Then, some bars might look bigger or smaller relative to other bars, even if the numerical difference isn’t as vast or narrow in reality.
Or, as a chart maker, you might use specific colors to denote something without actually mentioning what it is that they mean, in the legend. Sure, it might be obvious to a lot of people. But not everyone will ‘get it.’ Stating the obvious is often an important part of making clean, clear, and communicative charts. You don’t, in fact, know what other people don’t know. So, it’s best to be thorough! Though, that being said, being overly detailed can lead to too much visual noise, too. Aim for a balance between the two, ideally.
Another way to mislead your audience is by using data scales and ranges that might make it seem like two factors are more closely correlated than they might actually be. For example, if two lines from two categories practically overlap on a chart, you might want to adjust the scale so that the data points are more visually distinct.
