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Episode #31: Rees & Mushon on DataViz Empathy
156 Plays9 years ago
A few weeks ago, I attended an interesting one-day workshop on the responsible and ethical use of data in the data visualization field in New York City. The Responsible Data Forum brought together about 35 people to address issues and topics...
The post Episode #31: Rees & Mushon on DataViz Empathy appeared first on PolicyViz.
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Transcript
Introduction to Juice Analytics and Podcast
00:00:00
Speaker
This episode of the PolicyViz podcast is brought to you by Juice Analytics. Juice is the company behind Juicebox, a new kind of platform for presenting data. It's a platform designed to deliver easy-to-read, interactive data applications and dashboards. Juicebox turns your valuable analyses into a story for everyday decision makers. For more information on Juicebox or to schedule a demo, visit juiceanalytics.com.
00:00:35
Speaker
Welcome back to the PolicyViz podcast. I'm your host, John Schwabisch. I hope everyone survived the snowstorm if you're on the East Coast, and I hope you survived the very nice weather if you are basically anywhere else in the world. I'm excited for a sort of special episode.
Impact of Data Visualization on Empathy
00:00:50
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I have two special guests with me today to talk about whether data visualization
00:00:55
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can elicit empathy from our readers. I'm joined by Mushan Zaraveev, who's the co-founder of Schwal Design Studio. He's a faculty member at Schenkar College, and he's also involved with the Public Knowledge Workshop and recently hosted the Responsible Data Forum in New York City. Mushan, welcome to the show. Hello, hello. Nice to be here. Thanks for coming. And I'm also joined by Kim Reese, who's the co-founder of Periscopic. Kim, always glad to talk to you. How are you doing?
00:01:22
Speaker
My question. Great. Well, thanks to both of you for coming on. I want to dive into this topic of whether data visualization can elicit
Ethical Data Visualization and the Responsible Data Forum
00:01:30
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empathy. But before we do so, I want to ask Mushan to talk a little bit about the responsible data forum that took place a little over a week ago in New York City, about 35 people coming together in a room to work for what? We were there for about 12 hours working on a whole variety of topics related to working responsibly with data.
00:01:49
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Mushan, can you just tell folks a little bit about why you put that event together and sort of what the goals were for the day? Yeah, so the Responsible Data Forum is basically a series of events organized by the Engine Room, which is an NGO working on technology and social change.
00:02:10
Speaker
And they've been focusing on responsible use of data for quite some time and attacking it from different angles, whether it be hosting, privacy, security, you name it. And for this event, they wanted to address the questions of ethics and responsible use of data visualization, especially in the context of social change. And I've been working with the engine room for quite some time.
00:02:39
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And they've invited me to help them lead this and to facilitate the event itself. So what we were trying to get to is to get people in the room who have been grappling with these questions concerning their use of data visualization.
00:02:57
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in the context of social change and have been known to be somewhat critical of the youth and go a bit beyond the hype and ask some tough questions. So indeed in January 15, we met at ThoughtWorks in New York City. We also collaborated with Data and Society from New York on this event.
00:03:21
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And these 35 people, for the first half of the day, we try to kind of hash out the questions and the issues we want to address. And then for the second half, the different groups, six groups basically worked on different topics. We had a group working on inclusion, a group working on literacy, risk, transparency.
00:03:43
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uncertainty and goals. I think you, John, was in the inclusion group. Yep, culture and inclusion. Good times, stuff coming out, hopefully. Yep. There's a lot that is planned right now for follow-ups. A couple of people working on follow-ups. There are some new collaborations coming out of this forum. And part of what we've done towards the forum is to launch this
Empathy in Data Visualization Blog Series
00:04:10
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blog post series and their first blog post in the series trying to write a bit about the question that we are interested in going into this event was my blog post about Empathy and it was titled
00:04:25
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data is the unempathetic art. Right. And that spurred some Twitter conversation between a number of us and we've now seen a few blog posts following up from that event from all things about ethical questions with data and then using data visualization for social change and all sorts of other interesting things. I want to get into
00:04:45
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one of those blog posts in a minute. But before we do that, let me just ask sort of the general question of whether a data visualization itself, can that raise empathy from our readers?
Techniques for Evoking Empathy in Visualization
00:04:58
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And I want to start with Kim, primarily because Periscopics motto is do good with data. So we're going to talk about one particular visualization Periscopics has done.
00:05:08
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But Kim, what's your take on creating visualizations that elicit empathy or raise people's feelings about data and making social change? Yeah. So at Periscopic, like you said, our tagline is do good with data. So we touch on a lot of different topics from income inequality to gun violence to climate change, animal conservation, species conservation, that sort of thing. So we need people to react. We need people to sort of get riled up about things, get excited about things.
00:05:37
Speaker
and want to make change in the world. So we definitely try to get at people's emotions. And it's one of the things that's almost mandatory, I think, in our line of work. And especially when you're dealing with numbers and things that people typically see as dry or boring, you really have to.
00:06:00
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imbue them with something to get people at least interested in your subject matter. So yeah, we really try to evoke empathy. So how does one evoke empathy through a visualization? Does it need images of people? Does it need that individual aspect to it where you talk to an individual person or do you think you can do it by showing the data itself?
00:06:23
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That's a tough area. I think it is very difficult to make the numbers come across when you're just showing an abstract representation of people. But it's also very difficult to show individual people when you're talking about people at scale. If you're talking about a million people, you're talking about a thousand people even. It's very difficult to include imagery that will speak to each individual.
00:06:51
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you really have to think about the individual piece of that data. So I always talk about, you know, looking at the atom of your data. So whether that's a person, it's a child, if it's an animal, if it's, you know, whatever that individual piece is, you really have to get into the life of that, that being and, and speak to that individual. And then the greater piece will come out of it. So for instance, with our Gunvis, each line represents a person and
00:07:17
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At a certain point, the lines become completely obscured. It's more the onslaught of these lines overlapping each other that gives you that sort of dread, that sinking feeling.
00:07:28
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stop. It's relentless, right? There was a great article written by Jacob Harris about the dot, you know, the person represented as a dot and how to see people in data. And it brings up a lot of great questions and a lot of great questions about representation. And it's tough, you know, it's very tough. I don't think there's a one size fits all solution to representing individuals in data. But I think that there are, there are, you know, what's right, when it's right, kind of.
00:08:00
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to use imagery. I think color plays a big part, pacing plays a great part. I think, you know, if you look to filmmaking for some techniques to evoke emotion without exact imagery, there are a lot of techniques in animation and filmmaking that work really well.
00:08:18
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And Mushan, you led this whole event at RDF. Do you think a lot of the things that we were talking about led us to a path leading us to similar conclusions that it's not necessarily about highlighting an individual story, but about showing the different paths or the different stories of everybody and just in a visual way, but not necessarily using quotations or animation or pictures?
Storytelling vs. Statistics in Visualization
00:08:41
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I think, you know, there were
00:08:43
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different opinions in the room as we was pretty clear by the very first spectrogram experiment we had there where people quite argued with each other. But the sense was that I think most of the people in the room were not so keen on giving up on things like empathy or so excited about the big picture surpassing the individual story.
00:09:10
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There were some people in the room interested in storytelling, not only from the perspective of data, as in data storytelling, but also
00:09:21
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like storytelling as a point of departure rather than only statistics. Another thing that is important to say is that within the group in the room, there were people that had more experience with working with data visualization for storytelling and advocacy, and others had more experience with using data visualization for investigation, which I think kind of created quite different uses.
00:09:50
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But when we were talking about empathy, I think we were talking more about the advocacy. I think Kim would agree with me that many of the works that you guys are doing are advocacy-oriented. So I think what got me into this debate was actually
00:10:11
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A different debate on Twitter, people getting angry on Twitter. Somebody was wrong. No one on this call, but someone was wrong.
00:10:27
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But there was this popular science published this infographic commemorating the 70th anniversary to the Hiroshima bomb. And Kate Crawford, folk, the database people on her list to kind of ask what what do they think about the sterilization of of whole and and the conversation quickly escalated into people not liking it at all.
00:10:54
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And there was some going back and forth. But I think what Alberto Cairo said really struck me. And I can quote actually what he was saying said, it's like saying that no matter how good your writing is, it can't convey mathematical proofs as effectively as mathematical notation. This is simply because equation equations were created in part with with that goal.
00:11:18
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I'm just very skeptical to the idea that data visualization is a medium that can convey or even care about conveying or increase empathy. This is a great discussion though. So when he was arguing that visualization and empathy don't go well together, I've never thought about it that way. And it was something I wanted to explore.
00:11:46
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And Alberto and I started talking about it off of Twitter.
Rationality vs. Empathy in Public Decisions
00:11:51
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And this is something he actually writes about in his upcoming book. And there he's actually quoting Paul Bloom, the psychologist who wrote a very interesting article and he's writing a book about empathy and the title of the article was against empathy. So
00:12:09
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what Paul Bloom says is not only, Paul Bloom is not talking about visualization specifically, but he's saying that empathy is actually counterproductive. The quote I've been using in my article was, our public decisions will be fair and more moral once we put empathy aside. Our policies are improved when we appreciate that 100 deaths are worse than one.
00:12:38
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even if we know the name of the one and when we acknowledge that the life of someone in a faraway country is worth as much as the life of our neighbor, even if our emotions pull us in a different direction. So that was even more provocative, as in
00:12:57
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It's not only a question of whether visualization can convey empathy, but the question whether we should even care about empathy when we're doing visualization. I don't know this bloom guy, but that's complete bullshit. I read that too. And that irks me more than anything on the planet. That's saying we can't have emotions and because emotions cloud our decisions.
00:13:25
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I think the complete opposite is what the kind of world I would want to live in where our emotions make our decisions for us. You see a kid fall down on the street and you say, oh, that's too bad and walk away. I mean, that's bullshit, you know, like the emotions should be guiding us and telling us how how to create our world. So to be fair, Paul goes on and says that rather than empathy, we should focus on compassion.
00:13:53
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Because empathy, because it's not about us empathizing with someone, it's about someone. So in the case of the kid falling down the stairs, he would try to feel compassion for the kid rather than empathy. He's concerned about people focusing on empathy and not acting. So empathy without action is
00:14:15
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Yeah, so empathy can get us to be kind of frozen, because we're just empathizing so much, rather than understanding the position of compassion, which kind of works better with the rationalism that Paul Bloom is getting to, and that I think Alberto Cairo argues that is the fault of his organization.
Empathy, Action, and Visualization's Role in Change
00:14:44
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So so empathy with action is compassion. So this so this leads nicely into into a post that that Stephen Lambert wrote after RDF is that used periscopics gun visualization as an example that he really likes the visualization. But he what he found lack it was that there was no
00:15:02
Speaker
links or instructions on what to do next that yes, I now care about this topic, but what do I do do next? So I guess the next question is, is it the responsibility of data visualization creators to help the reader go on to the next step? So Kim, I'll let you start with that since it was one of your projects. Yeah, so that's a tough one. I mean, for a lot of our clients, we really press them to have action.
00:15:29
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have a call to action, you know, let's guide people to do something. And especially for the clients that we have, they do have goals that they need to meet. So we press them to have a call to action. Sometimes they don't. And it's more just to be informative. With the Gunvis specifically, we decided not to have a call to action because
00:15:50
Speaker
The piece was more about inclusion and not so prescriptive. It wasn't meant to be directly about gun control, although it definitely has that leaning, and it's kind of obvious that it's meant to be about gun control. But we didn't want to beat people over the head with it. Because the other problem you have, if you have a deliberate call to action,
00:16:13
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And it's somebody who's not sort of in that choir, you know, you're preaching to the choir. Typically when you have a call to action, somebody who's already inclined to do that thing you want them to do.
00:16:24
Speaker
Right. So sometimes when you have a call to action, it's a big turnoff and people are just like, oh, it's one of those people. I'm not going to read this or look at this thing. So with the gun piece, we deliberately left that out. And and it actually spurred a lot of really interesting conversations with people on the other side of the fence. It somewhat succeeded in being inclusive and sort of starting a conversation rather than being prescriptive. You know, I don't necessarily think
00:16:53
Speaker
everything needs a call to action, right? So that article by Steve Lambert, great article, but I do take issue with this mandatory call to action. You know, there's a,
00:17:03
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He has a line in there that says, if there's no call to action, there's nowhere for me to follow through on my outrage or despair, no government official to pressure, no legislation to support, no number to call, no organization to join. Boohoo, you know, like, what, am I your fucking mother? Do I need to, like, buy you a postage stamp to put on your envelope? Do I need to freaking type your Google search to figure out who your congressperson is? You know, like, come on, there are obvious ways to, you know, figure out what gun control group to join, you know?
00:17:32
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It's a big issue that's in our country forever.
00:17:35
Speaker
You know who your congresspeople are. You can easily find out, you know. I would take some issue with that because I think in the interaction design hat that we often wear and in interactive visualization, it's a part of the thing and that's a part of what you do. You don't wait for people to go do Google searches now. You do aim at a certain flow. But the question is, I've been teaching a class to students about activism and the internet.
00:18:04
Speaker
And at some point they've learned that they cannot use the term raising awareness next to me without me going crazy. With all due respect, raising awareness for what? The problem with raising awareness and the great thing about it is that you can't really measure it. So you raise awareness and you feel good about yourself.
00:18:28
Speaker
But it kind of looks like you've addressed the question, but you haven't really done that. And nobody can call you on it because nobody can say that you haven't raised awareness. So my students don't say raise awareness next to me.
00:18:42
Speaker
But I think that's something that Steve was bringing up in his article is the problem of compassion fatigue. There's something so powerful about your piece that you feel so, and there's something very depressing about it. And that's a part of what makes it so strong.
00:18:59
Speaker
activists, should we be depressing people or should we be pushing them to action? So yeah, you're not anybody's mother in that sense, or fucking mother in that sense. What is the goal of this conversation if it doesn't change the reality on the ground?
00:19:18
Speaker
Right. Yeah. I mean, that's a tough one. I think the answer would be different if we were Brady organization or something, you know, like if we were an organization that had a deliberate, you know, here's a policy we want change, here's some legislation we want to push through, you know, then would have probably had a direct call to action, you know, send this on to your representative, blah, blah, blah. But I think there's a space for
00:19:44
Speaker
the pieces that bring up questions, the pieces that bring numbers to light in a different way, in a way that nobody's seen them before, in a way that nobody's thought about them before. And I think that's what the gun piece did, is that it, it's sort of, you know, punched in the stomach with how many people get killed every year with, you know, by people with guns. You know, that's all it was meant to do, is to say, okay, people are getting killed, lots of kids are getting killed, you know, lots of young people.
00:20:13
Speaker
Here is the life, you know, here are the years that are lost of people's lives because of gun violence. You know, it's sort of meant to take a different angle on it and to make people think a little bit. And hopefully, you know, it's slow change. It's not like I think in our country, especially we have this immediate we need immediate action.
The Effectiveness of Awareness and Action
00:20:32
Speaker
We need immediate gratification. And if we don't get things signed and put a fucking paper in the mail, you know, like
00:20:40
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then it's a failure. But is that a failure to raise a question and have people look at this, look at a topic in a different way? We got a lot of people who were gun advocates who wrote to us. I had conversations on Facebook, on Twitter with people I never would have had conversations with otherwise. And they were meaningful. They weren't just trolling. They were real, meaningful conversations. And so that, to me, makes it worthwhile.
00:21:07
Speaker
And how important is the reputation or the previous work of an organization? So Kim, you sort of drew this distinction between the kind of products that Parastopic does versus a gun advocacy group, you know, Prorecon like Brady or other groups. How important is the previous work and the reputation of an organization, both in terms of their political leaning and the type of work that they do with the data when they're creating pieces where the purpose may be to draw empathy or to highlight a particular
00:21:35
Speaker
issue that may not be part of their even their core mission. I think you know one of the frustrations is if you align it too closely with your communication, your standard communication, your standard calls to action that you know you're only going to be preaching to the choir and or the counterpart to that raising awareness.
00:21:59
Speaker
which I also have an issue with because it's nothing. It's basically saying nothing. I want to, you know, wake up every day, you know, whatever year I want the sky to be blue. Oh, great. Wonderful. You know, hopefully some days the sky's blue. It's a meaningless statement and there's no measurement and clicks don't count and who knows what eyeballs mean. And you know, it's, it's kind of pointless.
00:22:22
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So I think there's a lot of room to be a little bit outside of that action lane and to be really provoking and trying new, you know, even alternative mediums, if you will, you know, I think a lot of people are just stuck on the web and stuck in their own website. And it's like, well, the people who are coming to your website are the people who always come to your website. Like how are you going to get more people? How are you going to get new people? How are you going to change minds?
00:22:48
Speaker
I think if we keep preaching to the camps that we're in, it's really just sort of treading water. So, Mushan, I would guess you would agree with that at least to some extent, right? I mean, part of the first exercise at RDF was making provocative statements and getting people to line up with that statement or with another statement. I think Kim is right about the danger of calling to action and kind of defining the boundaries of the camp.
00:23:15
Speaker
And I think, and I don't necessarily have a quick solution to something like that, but this is definitely something that I feel we should think about and we should continuously explore. I would have loved to see the not only works that I come across, but my work be kind of getting the best of both worlds, getting people who wouldn't necessarily be discussing
00:23:39
Speaker
to discuss something on the platform that data can give us which is this common ground and the data is there, let's discuss it kind of thing.
00:23:52
Speaker
but then also kind of get them to agree with me and go do what I want them to do. So there's no doubt that this tension, that's what makes the question of using empathy also hit same tension, like, and I think what one of the issues with empathy and visualization is that we're looking at a chart, we're automatically kind of trying to solve the puzzle and it gets us into this kind of
00:24:21
Speaker
rationalist framework. And I think a lot of the work that you guys have been doing with the gun violence piece or gun death piece is exactly use that visualization or add to that visualization the things that would add the empathy and would add your opinion and your perspective to it.
00:24:42
Speaker
And this balance, we haven't talked about it, but in the article that I wrote, I used your work specifically as an example of when I think empathy is introduced in a very elegant way and in a way that doesn't turn the visualization into some kind of a propaganda or not in a bad way anyway. So this tension is there because we've been using
00:25:12
Speaker
We've been thinking about visualization as such a strong tool for advocacy, but if the price of that is either devoting of any emotion in this kind of high law of neutral data, or on the other hand, kind of blatant propaganda, then we're kind of losing a lot of nuance between the two extremes.
Data as Language in Arguments
00:25:38
Speaker
And I wonder if we lose some of the objectivity that people value.
00:25:42
Speaker
Well, we don't have objectivity in general. Especially not in advocacy. And that's another spiel. I generally think we should go beyond the sculpture, the things that data, this objective thing that we're just bringing into the conversation. And that's where the conversation ends.
00:26:06
Speaker
But data is just another part of language. And we use data in our arguments. And you can make a strong argument when you use data because you have some foundation for your argument. But the fact that you're using data doesn't make it less of an argument. So I think if we're clear about that, and no matter how neutral or objective our visualization might look like, it is still an opinion. It is still a statement.
00:26:34
Speaker
in visual language and verbal or sexual language. And if we're clear about communicating our argument as an argument, I think ethically we're more than okay about presenting our opinion explicitly. I actually think it's more ethical to be explicit about your opinion than to kind of hide it under a shred of objectivity.
00:27:02
Speaker
Yeah, I agree with that completely. I think it's something that, you know, most people, they see the word data, they see, you know, things, numbers represented visually, and they just say, oh, this is supposed to be factual. This is a chart, it must be factual. And once more people understood that it was part of language, I think then people wouldn't get so riled up about bringing emotion into data visualization, because it's language, it's a means to convey an opinion, it's a means to convey an argument.
00:27:30
Speaker
it or leave it. You can twist it a different way and look at the counter. The gun vis is about gun murders in America, but if you compared it to car deaths in America, it would look like a totally different animal. We chose not to because ours wasn't about contextualizing death in America. It was about gun violence specifically. But if you had a different opinion, you could certainly
00:27:53
Speaker
change the visualization still very factually, but make it look completely different for your needs. So I think that emotions are fine in visualization, but you do have to read them as though they are language.
00:28:06
Speaker
And I think it's really clear in your case that, especially in the part where you did a pretty bold move by using a completely different data set of trying to assess how many years they have lost all of these victims. And that is a pretty unorthodox way of mixing data. But at the same time,
00:28:28
Speaker
If you don't talk about the years that they've lost, that's just kind of giving in to the narrative of that person is dead, there's nothing to speak about the years that they could have lived. So it's not like even the reality of the death can tell the whole story because that's what happened. A part of what happened is what didn't get the chance to happen. And to do something like that, you need to take an opinion.
00:28:54
Speaker
to take a stand and say, I know this is not reality. I know that the life that they could have lived is not a part of reality, but I think it needs to be assessed because we are protesting against what happens in reality. Right, exactly. That's very well put. Actually, when we're talking about the... One thing that I do agree with Alberto Cairo is that
00:29:22
Speaker
visualization and empathy don't naturally go together. You actually need to deliberately use and invoke empathy. And that would not happen by you just representing data visually.
00:29:38
Speaker
And I think you guys with the Gunbeth piece have done quite a lot of work on adding that drama and humanizing each dot and each arc to add that.
00:29:54
Speaker
And we can start, we can argue about when does the visualization end and the extra layers that may include empathy begin. But at the core of the argument, I think I think there is a problem about visualization and the human perspective. I don't necessarily agree with that. I think that I think the moment you choose to do something with the numbers is the moment you've
00:30:23
Speaker
added, you've taken a stance and decided that, you know, even if you just made a line chart, you've chosen to represent a certain number in a certain way, right? Whatever color you choose, even if it's black or gray or what have you, it's still a color. Now, those are minimal things that you can do. But even the fact that you're choosing to say something about a number, you know, I could have said,
00:30:52
Speaker
Something about the number of people killed in 2010 with guns. I think it's like 10,000 something.
00:31:00
Speaker
But the fact that I chose to even say anything about that number, even if I'm not visualizing it, I'm still telling somebody for a specific reason, right? I agree that you're making a statement and expressing something or communicating something. But for it to invoke empathy, I think in most cases,
00:31:24
Speaker
You would need to do that deliberately, unlike, you know, speaking face to face, for example. Actually, one of the strongest articles that were written towards the responsible data forum is Catherine D'Ignazio, what would feminist data visualization look like, which I really recommend everybody reads.
00:31:44
Speaker
She actually brings out Donna Hallway and quotes from her. And she's talking about the idea of Donna Hallway called mapping, the God trick.
00:31:57
Speaker
So it's this trick of seeing with nobody, so an eye that sees but doesn't have a body of its own. So that's true about the visualization at large because in a lot of cases the position of the speaker and that's part of the problem that people don't see visualization as part of speech because the speaker is hidden.
00:32:22
Speaker
You don't read texts, you don't hear their voices, you don't see their faces. You see this abstract representation of mathematics and it aligns itself with something beyond human even though we completely agree that it's not beyond human. So I think you need to situate the human within it deliberately for there to be this presence.
00:32:52
Speaker
Yeah, it's funny that you bring up this God concept. I'm going to be giving a talk exactly about this because I think that we
00:32:59
Speaker
You know, there's been so much of a hubbub about storytelling and data visualization and, you know, is it good? Is it bad? How to do it? We're all storytellers and blah, blah, blah. And I'm so over it. And I realized the reason it bugs me so much is because storyteller, the term storyteller really diminishes what we're doing. We are gods. We are the gods of data. We are creating these realities that are
00:33:24
Speaker
you know, could be completely out of context, you know, whether we're good gods or bad gods or, you know, just, you know, gods of whatever we're, we are creating things that are necessarily stories. We're not telling stories. We're creating realities based on numbers and concepts and abstract things. And so I think it's, I mean, I think we have to start grappling with some of these big, big questions.
00:33:51
Speaker
That's exactly what the responsible data form was all about. I should have participated.
00:34:00
Speaker
Well, so I really liked this episode because I really didn't have to say anything. So I got to be like everybody else and just listen to you two talk about it. This is fascinating. And I think we really just scratched the surface, right? We didn't talk about just thinking back to the RDF forum. We didn't talk about a lot of things we talked about there, things about uncertainty and about risk with data and what we created and the culture of people who are creating who we're creating for. So I think I'm going to have to invite you both back.
00:34:29
Speaker
Well, there will be a series of video interviews that is trying the audio feeds and there are still quite a few blog posts in the pipeline, quite a few projects. So if you follow the policy, they will retweet everything. Yeah, retweet everything and everything will go up on the now long list of things to accompany the show. Kim, Mushan, I want to thank you both for coming on the show. This has been really fascinating.
00:34:57
Speaker
Thanks, John. And thanks to everyone for listening. I hope you enjoyed this somewhat longer than usual episode, but truly fascinating. So thanks everyone for listening. And of course, if you have comments or suggestions, please shoot me a note on the website or on Twitter or via email. So until next time, this has been the policy of this podcast.
00:35:29
Speaker
This episode of the PolicyViz podcast is brought to you by Juice Analytics. For 10 years, Juice has been helping clients like Aetna, the Virginia Chamber of Commerce, Notre Dame University, and US News and World Report create beautiful, easy to understand visualizations. Be sure to learn more about Juicebox, a new kind of platform for presenting data at juiceanalytics.com. And be sure to check out their book, Data Fluency, now available on Amazon.