Join Bill Shander for an in-depth discussion in this video Listen: Data storytelling with Neil Halloran, part of Data Visualization, Storytelling, and Information Design: A Lesson and Listen Series.
- So now I'm at the LinkedIn studios recording booth, and it's time to start talking to our guest, Neil Halloran, we're gonna talk about our theme for the day. Neil is the creator of The Fallen of World War II, which is a great video and interactive piece about World War II, he also created something called The Shadow Peace, which is the, essentially a sequel to The Fallen, focusing on nuclear weapons. Before we talk to Neil, I want to just sort of dive in and take a quick look at one of his pieces. We're gonna look at The Shadow Peace here.
(clicking) - [Narrator] Every second on average, 4.6 people are born into the world. That's 140 million births a year. (clicking) (ominous music) (clunking) (dramatic music) There are currently over seven billion people alive today.
(dramatic music) Humans are dying at less than half the rate we're being born. About two die every second, which is 60 million deaths a year. (foreboding music) And the difference between the birth rate and the death rate tells us how fast the world's population is growing. (ominous music) (clicking) We're gaining about two and a half people per second, or 83 million a year.
- So that's the opening sequence to The Shadow Peace. It's got so many great components, so many great aspects to it that make it an incredible data storytelling experience. It got those simple visual elements, the cubes, to represent the data points. It's got the overall visual metaphor of the hourglass that sort of puts it all into context. It's got the sounds, those clicks, to help you understand the rate of data going by, the people being born and people dying, et cetera. The use of the camera to essentially zoom in and out to sort of focus in on detail, and then also allow you to see the big picture.
And also the great pacing, it doesn't rush to get you to the point, it allows you to build a little bit of anticipation, to wonder what we're leading towards. The narrator's voice, of course, really helps solidify and tell the story, helps us understand what's going on. And the use of simple, spare on-screen text to really help bring everything home, sort of like labeling a chart properly. All of these things combined make Neil's work really great, a great example of data visualization for regular folk. And I'm really excited to have Neil Halloran joining us now, so Neil, thank you for joining me, and welcome.
- Well thanks for having me, this is great. - Excellent, so Neil, today's theme is data visualization for regular folks, and I think a great example of that is the Fallen and the Shadow Peace. And so I wanted to start off just by asking you, I know that you try to get a general, sort of wide audience for your work, and you did that very, very successfully. What do you think is the most important thing that essentially you were trying to do when you're creating those pieces, I guess the example being, was it more about the data that you were talking about, was it about the emotional component, was it the visual design, what is it, do you think, that made that so successful at reaching a wide audience like that? - So yeah, as you said, I'm definitely trying to create an emotional experience, one that's also cinematic, and the thinking behind that is that communicating data is something that is difficult to do in a lot of ways, when you're trying to talk to, or appeal to wider audiences, and anything you can do to make something more interesting, especially if you're trying to grab someone's attention who's not being paid to look at your work, so if something pops up in somebody's social media feed, to try to hook them, emotionally, from the beginning, hold their attention throughout the piece, deliver something, some kind of a message that will, something that would give them reason to share it to their friends.
Those are the things I'm thinking about as I'm working on it and trying to accomplish, and so a lot of it is about creating that kind of emotional appeal, which, in the case of telling a story about World War II, and tallying death, it's more obviously an emotional story when you're talking about the human loss. But I'm trying to do that with other topics as well, to try to create that emotional intrigue about the data.
- [Interviewer] So emotion is the number one most important thing, do you think? - I think so, I think that when you get into other topics, the emotion may be less obvious and pronounced and severe, so I'm doing a piece now on global warming, but I do think a lot about what is the emotional component to this, even if it's less dramatic. So maybe it's trying to make something engaging and interesting, and a good way of doing that is to think about emotion, and think about how people are feeling as they're looking at the data.
So I suppose that probably, that would be, that the one thing would be creating an emotional story about data would be the one thing I'm trying to do. - Okay, so, you know, emotion is one aspect, and it's sort of a semi-controversial component of data storytelling in a way, a lot of people in the data visualization community are sort of in a debate about emotion, in particular, even just the word storytelling. I think I can guess where you fall on the spectrum of how the term data storytelling fits, whether it's an appropriate term to use, et cetera.
But talk a little bit about storytelling, generally, and where you think it belongs in the world of data visualization and information design. - Yeah, so, I feel that in some ways, the debate about storytelling can be like a semantic food fight, where there isn't necessarily a lot of meaning, but in other ways, I think that there is something behind it, so... And the other thing I should say is that a lot of people who have been kind of putting down storytelling and questioning its use, are some of my biggest heroes.
And kinda thought leaders in the industry, so I totally respect what they're saying about it, I feel that, one of the problems with the word storytelling is that the first question is what does it actually mean, especially in the context of data, where there isn't always a linear component. In my work, there is a linear component, so it's a little more obvious to say, okay, there's an arc, this follows these more traditional things when you think about the story.
But I would define storytelling as whatever the thing is that makes an argument, or a presentation of data interesting. I don't think that an argument is enough if you're trying to appeal to wide audiences. And so a lot of this does get to this issue of are you trying to speak to people who are your colleagues, who are people in your organization or business, or people who are already familiar with what you're presenting, or familiar with data and looking at charts, or are you trying to speak to wider audiences, and if you are trying to speak to wider audiences, I feel like you need something that goes beyond a strong argument, or something that goes beyond you know, reason and good data, it's something else, and so in some ways, that something else that makes something interesting, you could say is story, but it is a fuzzy definition, and I can definitely understand why folks get frustrated with it, because it's, it's over-hyped, and it's a very sexy word, and sometimes it seems like it's a lot of sexiness, but not a lot of content behind what it actually means.
- This sounds like we're trying to create all this controversy in this interview, and it's not, this is the last bit, (laughing) I promise, but you had a really good Twitter rant recently, a few months back, talking about what you also just alluded to, the idea that the community, which I agree, there are these amazing people doing incredible work, leaders in the industry who have different opinions than I do on some of these topics, and I do agree it's mostly semantics, but there's also this issue in the community that you were talking about where the data visualization community is sort of really being driven by academics and technicians who sort of talk amongst themselves about data visualization, and is in a fairly academic and technical way, and it's not a lot of talk within the community about this more wider audience conversation, and bringing that thought leadership to a wider audience.
And so, I wanted to ask you about that rant, what triggered you to sort of go on that, start that conversation, and has your thinking about it evolved at all in the last few months. - Yeah, so, I did have a little bit of Tweet regret after that Twitter storm. I do believe what I wrote, but I feel like I was a little bit heavy on those Twitter keys. I wanted to stress that this is kinda like, it was a selfish rant in that, you know, from my self-absorbed perspective, I want people to be talking more about reaching wide audiences, and as you said, a lot of what data visualization is, is something which is academic, but also people communicating to colleagues, and part of that is some of the economics behind the industry, right, so a lot of people who listen to podcasts and go to conferences and buy books are trying to figure out how do I communicate data to people in my workplace.
I feel like that kind of drives, or skews a little bit of the conversation towards people who are trying to figure out how they can do it themselves. And trying to communicate to wider audiences is, a lot of that overlaps, so a lot of what we're talking about is even when you're having academic conversations, overlaps is what I'm doing for sure, but there is a lot that is different, it is a different challenge, and so, yeah, there's a part of me that wishes that conversation was happening more.
I had an experience where I presented a piece at a workshop, and one of the people who was there was from a favorite podcast of mine, This American Life as a producer, and I was begging people for feedback, as I often do, and I said give me harsh, honest feedback. And she approached me and said that what I showed wasn't interesting enough, like she probably wouldn't stay engaged with it.
And it was really, it was devastating, I was whimpering all the way home, but it was really good feedback, and she used the word story a lot. People who often are in the podcasting world, and in the film world, often use the word story a lot, and so I felt like, you know, if you look at the, the comments that are on Twitter, et cetera, within the data vis community, you see really harsh criticism of people if they ever skew a chart a certain way, or show a pie chart that you can't tell which piece is bigger, or the wrong kind of axis.
A lot of these technical issues, there's really harsh criticism when people don't do something right. I feel like if I need to get, if I want that criticism about, is this interesting enough, will normal folks be engaged in this, people within the community seem to be a little bit softer on that question, and are a little bit uncomfortable with the goal of trying to be popular to wide audiences. I think there's, for good reason, discomfort with where does that leave this craft if we're concentrating too much on cliques and views as opposed to being more, caring more about being effective in how we communicate data.
So I feel like there's a lot of good reason to have this kind of discomfort with the conversation going that way, but I also feel like it's really good to think about how do we reach folks who really need to be reached when it comes to this type of content. - I think it brings up a really good point, it's about a spectrum, right, between effectiveness, best practices, all that stuff that academics and technicians should and do care about, and then having emotional impact, actually making, changing people's minds, making them think, et cetera.
In that vein, can you think of a project that you've seen out in the universe, it could be one of yours if you want to, in the past 12 months that just, that kinda blew you away and said, wow, that was great, and maybe it accomplished both, things on both ends of the spectrum, I wish I had done that myself. (laughing) Anything that you can point to that is a great example of this kind of work. - Whew, yeah, there is so much great work is always being done, I mean, I think that one of the, the places that is really exciting to me because it does touch a little bit upon this idea of being, (clicks tongue) talking about data in a way which is more personal and more human, that would be some of the work done by Giorgia Lupi, and she recently did a project, it was called Bruises, I think it was, I forget the subtitle of it, but it's a really beautiful piece about a friend who had a daughter who was dealing with an illness, and it was so personal, and she likes to present data with A, kind of a real human component, but also handcrafted with drawings.
And so, that kind of work that is speaking to, thinking about data differently, and thinking about being more human with numbers, I thought was just really gorgeous, and set me back. Then there's this work being done New York Times just recently, they have a series, or certain charts that are animated, that look at income brackets, and children being born who are from poorer families, and richer families, where they end up, and it was really slick animation that really conveyed that information so well.
So I thought that was amazing, so there's always, you know, there's always so much amazing work being produced, and it's just an exciting place to be, for sure. - Yeah, so, not at all surprised that those are two of the references you would bring up. (laughs) Also two of my favorite projects, I just incorporated that New York Times piece into my workshops, it's a great example, and Giorgia does amazing work, as always. So listen, Neil, thank you very much for joining me on this, I'm sure our audience will greatly appreciate your insights, and hopefully we'll run into each other out in the community one day soon, and look forward to seeing your next bit of work that you release to the public.
- Great, thank you so much. - So that was really great having Neil here today, as I mentioned before, Neil really is a master data storyteller, he does an amazing job telling stories that are backed by data in a way that's incredibly engaging and accessible to regular folks. Of course, that's our theme for today, right? And I really appreciated his insight into how important it is to inject emotion into your data stories, and to make them engaging in a way that gets people who, as he said, aren't getting paid to look at your data. (laughs) Which is a hard thing to do, it's hard to get regular folks to be engaged in these data stories, and he does it just about as well as anybody, so I really, really appreciate him being on the show, I hope you all enjoyed it as well.