Join Bill Shander for an in-depth discussion in this video Compare and contrast, part of Data Visualization: Storytelling.
- [Man] Stories are at their heart almost always about comparing and contrasting things and often about how things connect to each other because the key component of every story is ultimately about the transformation of a key character and what he or she goes through to achieve a goal. Remember our definition from earlier. We're always evaluating at least a binary choice comparing what ifs and looking at the why's. What decisions were made along the way? What connection can we make between those decisions and outcomes and what connections did our character make to end up where she is? What if Luke doesn't succeed in his training with Yoda? Does the world come to some horrific end? Often, the options in fictional stories aren't presented as options so much as they are the alternate reality that we might wish the characters had adhered to to avoid the disasters we have to witness them experiencing, cringing all the while.
And in documentary and journalistic stories, we're often confronted with comparisons and contrasts because the story is about something that hasn't happened yet or is happening and we have to make a choice to change the outcome like in An Inconvenient Truth, Al Gore spends a lot of time explaining the data about climate change essentially making comparisons between where the world is headed now and where it could go if we make certain changed to how we operate as a species. That by the way includes some great data storytelling and I recommend you watch it if you have a chance to.
And data itself is often about contrasts comparing different data sets to each other, comparing different variables within the data, et cetera. So some data really lends itself to telling a this or that or us versus them or good versus bad type of story. When you're telling a story of comparisons, it's really important that you're sure you're comparing apples to apples and that means carefully choosing what you compare and the methodology you use to be sure your data really is comparable. This is where you start to think about choosing your subjects carefully.
For instance, you wouldn't compare Margaret Thatcher and Moe Howard on just about any metrics and if you did, say you're comparing their incomes, you would have to normalize that data by adjusting for inflation and possibly converting their incomes into percentiles or some other index to account for their different occupations and geographies. You get the drift. To compare things, you have to make them comparable. So for instance, looking at this Global Brand Simplicity Index from Siegel and Gale. As it says below, tt's a study on how consumers rank leading brands and the role social media plays in their lives and why simplicity pays.
The whole idea here is we're looking at different brands, different companies that have different levels of simplicity in terms of how they are perceived as simple experiences, et cetera and how they perform against each other. This is all about comparisons. Like any index, an index is always about comparing things to each other. Notice that for instance, there might be an index comparing countries to each other or comparing schools to each other, comparing in this case, companies to each other. It's really one of the most classic data stories there is. Indexes. So this story starts very broadly.
First, they explain what they mean by simplicity and building a better brand experience but the story's very broad at the very beginning, right? How do these companies compare to in this case companies that aren't quote on quote simple? So how are the simple companies performing compared to the others? Right, it's a very broad look at the data. Then it starts to narrow in looking at just those in the index. What is this list of companies and literally, how do they compare to each other? This is what a ranking is. So this project, SelfieCity, is a really interesting project where this group of people took a whole bunch of selfies as you can see.
Took them from a bunch of different countries. I think they scraped data from I believe Instagram and essentially looked at all these selfies and measured them in a whole bunch of different ways and so at the very beginning, the first thing I can do is look at the different collections of selfies so I'm looking at selfies from Moscow and just visually, I can see what they look like compared to let's say the selfies from Bangkok or Berlin or if I scroll a little bit further down the page, I can really do some more data driven comparisons and look at in this case gender breakdowns, okay? Gender and age profiles per city.
So for instance, I can quickly see that people taking selfies tend to be a little bit younger in Bangkok, right, the average age is 20.3 amongst women and 22.7 amongst men. I can see just by the shape of the distribution, they tend to be a little bit younger in Bangkok for instance than Moscow where it's a little thicker in the middle of the distribution. A lot more women take selfies in Moscow, very few men versus Bangkok where it's more closely evenly distributed, et cetera. We're comparing.
We're comparing these cities to each other. We're comparing genders to each other. We're comparing age groups to each other and how they do this particular task which is in this case is taking selfies or I can compare them based on their let's call it smileyness, okay? Bangkok which is in Thailand which is actually known as The Land of Smiles. They're very smiley, right? They have a lot of people who smile a lot in their selfies as opposed to Moscow which seems to be more dour, okay? Not a lot of smiles in the selfies in Moscow, okay? I'm able to compare these things.
It's an interesting way of looking at this data. And then I can go down here to this tool which is called the Selfiexploratory which I'm gonna click into which is really this sort of interactive data experience. Again, looking at all of this selfie data and essentially what I'm looking at here is all of these things, I have geography, I have age, gender, pose, which way they're looking and various other features. Are their eyes opened or closed? Mouths open and closed, et cetera. And so I see all of the images down here so I'm looking at 3,840, all of the selfies in this case, a random collection of them but let's say I just want to look at happy ones.
So I can click and drag and say just show me happy ones and it filters to just show me happy selfies. And you'll notice that it also then shows me up here all of these other elements change. So if I click out of that, if you keep your eye on the geography over here, because I say just show me happy selfies, the Rio bubble gets bigger and the Moscow bubble for instance gets smaller cause there are fewer happy people in Moscow selfies than there are in Rio selfies.
So I'm able to do all these comparisons as I interact with this entire experience. Really interesting way of looking at the data, making comparisons and in this case, this part of this experience is sort of dashboardy whereas the other part was more of a storytelling experience. In either case, I'm looking at comparisons across all of these different measures in a very compelling and very easy to navigate way. So this one isn't just about comparisons but that does become a really core part of the story as you play with the tools and start to compare gender, age, geography, et cetera.
Comparing and contrasting are among the most common data stories out there and as you can see, there are many ways to look at comparisons from how broadly or closely you compare things to what perspective you take to slice into the data. As always, focus on the story you want to expose with your data and the comparisons and storytelling around them will hopefully become clear quickly and be sure to understand that even when you're not intending to focus exclusively on comparisons, your audience might go there anyway so be conscious about how your data ends up being comparable to other things and what story you might be telling even unintentionally.
Join data visualization expert Bill Shander as he guides you through the process of turning "facts and figures" into "story" to engage and fulfill our human expectation for information. This course is intended for anyone who works with data and has to communicate it to others, whether a researcher, a data analyst, a consultant, a marketer, or a journalist. Bill shows you how to think about, and craft, stories from data by examining many compelling stories in detail.
- Creating a narrative structure for data
- Applying narrative to data
- Identifying what you want to say with the data
- Analyzing what your data is saying
- Determining what your audience needs to hear
- Leveraging tables, charts, and visuals
- Ensuring your narrative provides context and direction