Danielle Alberti is the data visualization editor at Axios. She was previously a front-end web developer at Pew Research Center and is a journalism and anthropology graduate of the University of Colorado at Boulder. She worked her way through nearly every newsroom job (including paper delivery) before landing in data visualization, where she very happily manages a team of ten amazing developer-designers to make news every day.
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Welcome back to the PolicyViz podcast. I am your host, Jon Schwabish, and welcome to the final episode of season eight of the podcast. I am very excited to work into my summer break from the podcast. Don’t worry, we’ll be back in the fall with more episodes. But I’m excited to wrap up this season, it’s been a long one, it’s been a hard one, a lot of stuff going on, so I am pleased to get through it. I hope you’ve enjoyed all the different episodes, learned a lot about data visualization and presentation skills and data communication. So I’m going to get right to this final episode with Danielle Alberti, the graphics editor at Axios. I hope you’ve been watching Axios’s work, they have been doing amazing stuff, and they’ve been growing tremendously, as you’ll hear in the interview, we talk about the growth of the team, the changes in the tools, their approach to different types of content, how they write around their visualizations, which I found shocking really, to say the least, like, the amount of words that they don’t write is pretty amazing. And we also talked about Danielle’s approach or thinking on how to communicate large numbers, which has been an important consideration in the last few months as we neared and passed the mark of 1 million deaths from COVID here in the United States. So I’m going to move on to the interview with Danielle, and here is that conversation.
Jon Schwabish: Hi Danielle, how are you?
Danielle Alberti: I’m doing well. How are you doing?
JS: I’m doing great. Thanks so much for coming on the show. Very, very excited, not only because I’m excited to learning more about the Axios visuals team, but also because just a big fan of all of your craftiness on the Twitter, and get your daughter’s little reading knocked together. And what are you, crocheting now, is that what I’m seeing?
DA: Cross stitching.
JS: Cross stitching. How’s that going?
DA: I just finished my first piece in a few years, I did it a lot as a kid, but picking it up again as an adult.
JS: Nice. Again, a little non-screen time.
DA: Yeah, that’s kind of the goal. I started sewing a couple of months ago too, bought a sewing machine in February, and just immediately started sewing clothes for me and my daughter, and yeah, just kind of trying to pick up a few things to get away from the screen ever once in a while.
JS: Yeah, I started umpiring at the Little League because I was like, I need to not be on this, because it’s just, I mean, you probably know this too, like, here’s my office, and five feet away is the house, and it’s like the siren call of the computer all the time, and outside for a little bit just breaks the spell.
DA: And I feel like especially as a person who’s working in news, I feel like I really just have this need to be attached to the news all the time, and it’s so difficult to break away from that.
JS: Yeah, and it’s so depressing now.
DA: Yeah, and it’s constantly depressing, and it’s horrible, and you still feel like this obligation that you have to be there and witnessing it, and sometimes it’s helpful to have something to just kind of force you to get away.
JS: Yeah, absolutely. So Axios, so I have been watching from afar, obviously, the changes, evolution, you always seem to be hiring these days for more people. So what does the team look like right now?
DA: Well, let’s see, when I started, about two and a half years ago, it was me managing two other people. As of right now, I’m managing a team of nine other people. So data visualization is 10, and then our illustration team is 10, and we work very closely together, which is why I mentioned them that we all kind of coexist in the same Slack room, and we edit each other’s work and help each other with ideas and frequently collaborate on projects. So yeah, we’ve gone from a very, very tiny team to something that has just become like this Axios’s visuals army. And, yeah, so it does seem like we’re kind of hiring a lot. As local has been expanding, we launched in two new cities today, we had to keep hiring visuals people to keep up with that. We initially started out with this formula of how many newsletters do we need to launch per new hire or vice versa, I guess, how many new hires for extra newsletters, but I hired several engineering types last year, who have increased a lot of the speed and batching capabilities of a lot of the charts that we’re trying to make. And so, it’s made it so that we aren’t growing quite as fast as we were last year. So yeah, just all a very roundabout way of saying, yes, we have been growing rapidly, but we’re slowing down a little bit in this next year or so.
JS: Okay. As the head, is that lead editor of the graphics team, how has your job change, or maybe better put, like, has the process changed by which all this content sort of funnels and goes out the door?
DA: Gosh, like, so, it’s completely unrecognizable from when I started, when I first started, we, like I said, there were three of us, and we basically had an in-house chart builder tool, and we had Illustrator, and we had D3. And most of what we were making was being made in this chart builder tool and Illustrator. Today, we use Datawrapper for most of our quick turnaround stuff, and we’re using Svelte for a lot of our more customized things. But then we also have tons and tons of templates and tools that we’re using to really speed up the process. So a lot of our sports graphics, they’re just templates, where we’re pulling it up in the console, typing a couple of lines, and it’s spinning up this standings table or something like that.
JS: And that’s in the internal tool, that’s the templates that are built in?
DA: Yeah. And we have a couple of other internal tools for building stock charts, other kind of finance charts, and then, we have just a few other templates that require a little bit more finessing like maps and stuff that require a bit more actual work, but are still really, really increasing the speed that we’re able to do that stuff at. So we’re using Illustrator a lot less, and we have a lot more power over what we’re actually doing. So yeah, that process has changed just phenomenally. And whereas before, it was our tiny team of three, just had to rush, rush, rush, rush, rush to get everything out the door, we were constantly bombarded, just trying to survive. Now that we’re at like this team of 10, and we’re more efficient, and we’re working faster, we have a lot more time that we can focus on building new tools, and really kind of working more on the long term stories and finding our own stories and doing a lot of the fun stuff that we always wanted to do.
JS: Yeah, so I want to ask you about that, before we go on to that. So in this previous iteration, it was D3 and Illustrator and this internal tool. Do you find now with the changes in the tools and using Datawrapper now, do you find that you’ve moved a little bit more towards interactive graphics, whereas before maybe it was more static images?
DA: The vast bulk of what we’re making is still going to be static, most of what we’re doing is static or Datawrapper interactive.
JS: Right, a tooltip or something.
DA: A little tooltip. That’s the vast majority of what we’re doing, it’s super quick turnaround, it’s usually going to run for the newsletter the next day, that’s it. But we have a lot more time now that we’re able to devote to things that require a little bit more creativity, some more investigation, a lot more time. It’s really freed us up that we can chase our own stories.
JS: Yeah, so that’s really what I wanted to spend some time talking about, because Axios clearly is a different animal than a New York Times or a Post because they seem to be a lot shorter, quick hit, newsletter type pieces, as opposed to the big dive deep. And I’m curious, we can talk about the evolution in a minute, but I’m curious from a data visualization visuals perspective, do you like the numerous more shorter hit things rather than the deep dive pieces that maybe take a few months to do?
JS: Now, both personally, and maybe as a team, I think, yeah.
DA: Yeah, I would kind of push back on the idea that being something that’s really painstaking takes a lot of time, takes a lot of creativity, is necessarily a deep dive. At Axios, our thing is smart brevity. We’re trying to get people to learn as much as they can, in as short a time as they really can, and we’re really extending that to our storytelling team, where we want our longer form graphics to still be fairly brief, fairly concise, low word count, but tell a big story in a short amount of time. And frequently, that means that we’re spending a lot of time working on that, conceiving of it, designing it, editing it. But the reader may only take a couple of minutes to actually digest it. In our COVID million deaths project, we worked on that a while, but ultimately, the word count is probably under 100. There’s almost no words there. It’s completely a visual product, and so, that’s something that that we really try to push when we’re doing our more immersive storytelling is that we want it to still be very concise.
JS: I mean, that must force you to think really differently…
DA: It does.
JS: So can you talk a little bit about that, because, I imagine, for a lot of pieces, even if it’s just an exploratory map, there’s a lot of words wrapping around that, it explains the data and explains this and that, but yeah, like you said, a 100 words is like, that’s like nothing.
DA: I think that in data visualization, especially outside of journalism, but also frequently inside of journalism, a lot of people have trouble editing down what they have. They think that if I have all of this data, I need to show all of this data, and it’s such a difficult skill to punch that down into what is the story that you actually want to tell. I think for us that’s something that we focus on really heavily is, what is the story exactly that we’re trying to tell, what is the exact angle that our writer is trying to get across, what is the most interesting piece of this data, what is the thing that the reader needs to understand to come away from the story with like an understanding.
DA: And that’s a skill that we really have to hone in on a lot. I definitely see it a lot as people join my team, that it takes a little bit for them to kind of figure out how do we nail down exactly the right pieces of data to get the information across to the reader without including a bunch of extra stuff. So that’s just kind of a skill that really needs to be developed, and yeah, it’s definitely something that requires a lot of attention and forethought. I am coming from Pew Research Center, where giving everybody everything, it’s generally the preference. You want it to be a data dump, you want people to explore everything, have everything. That’s fantastic, like, a lot of the time. With our election project that we launched today, that’s a data exploration project, and we want people to be able to explore that. We want people to be able to look at different issues and different districts, compare the district that they know to the neighboring district that their aunt lives in and see what kind of deductions they’re coming up with. But the vast majority of our work is not going to be the data exploration kind of work, it’s going to be the storytelling kind of work, it’s the “this is what you need to know”, and that’s a difficult skill to master.
JS: Yeah, I mean, it seems like, the way you talked about, it seems like, in a lot of ways, the words get us off the hook a little bit, because we can have the graph and then say a whole bunch of other stuff off to the side that we don’t actually visualize, and it gets us around a lot of this editing requirements. Do you find that a reporter brings a story and they have X number of things that they want to talk about and you help winnow them down? Do you find that in that winnowing process, you’re like, hey, those three other things actually were pretty interesting, but we’re just going to do them as separate stories, and then, does it come back as like a repeat – not a repeating thing, but like four little pieces instead of like one traditional wannabes?
DA: Yeah, that definitely happens sometimes, as well as vice versa, honestly. I think that’s one of those things that goes both ways. I think one of the things that’s really nice about our newsroom is that we’re really empowered from the very top to, like, we are journalists, we are reporters, we are data journalists, and we are able to tell reporters that we work with, whether this is good data or not, does this data match the story that you’re trying to tell, what does this actually mean, should we be talking about this, and we have that power to be able to work with them. So rather than just being a service desk, we are collaborators. And so, that is something that frequently happens is that we’ll be talking to these reporters, and sometimes it is, you know, I saw this other really interesting thing in the data that we should probably either be including in this story, or we should be writing another story about, like, can we follow up on this. And conversely, a lot of the time, reporters have the same, you know, if I have the data, I must use the data impulse that everybody has, and frequently, we have to be like, look, I can’t put 12 lines on this chart, I’m not…
DA: If we put 12 lines on this line chart, it’s not going to help anybody. We need to figure this out, what can we actually trim and work with them to figure out what can we take off of this to really, or should we consolidate things, how can we make this work more for the reader. So we definitely go both directions with when we work with our reporters.
JS: Yeah, so does every reporter, the visuals for every news story, do they all come through your desk, or, are the reporters able to use that in house tool to build a bar chart in Datawrapper, for example?
DA: Yeah, so our reporters are the primary users of the in house tool now. It’s called Hermes, it does line charts, bar charts, and heat tables. So the reporters use that, but it all has to go past my desk, basically, for edits. We need to make sure that it’s following our house style, that the data is accurate, we have to do our number checks, we make sure that all of our charts are edited to the exact same standards that anything else would be. So yeah, they may build it themselves, but it’s still going past us.
JS: Okay. So you mentioned the piece that you all did, marking the milestone of a million deaths in the US from COVID. Definitely, I think kind of a different piece from your team, both in terms of its kind of size, I’m not sure if that’s the right word, but like, size on the web, I guess, but I’m curious, and for folks who haven’t seen it, and I’ll put a link to it, it was sort of these ever growing squares as you sort of scroll down over the course of the pandemic, I guess, generally, and on the last episode of the show, I talked to Aliza from the New York Times about their stories about the same milestone, I’m just curious about how you and your team think about communicating these really large, important numbers?
DA: I think it’s impossible for a person to understand what a million means. People aren’t capable of actually thinking in that quantity, you understand a person’s death, you understand 10 people’s deaths; but at a million, that’s just not something that people can understand. And we wanted to provide some context for, okay, you may not understand what several hundred thousand people would be, but maybe you’ve been to Nashville – what would it look like if just Nashville ceased to exist? And then, so many of the events that we included are events that you grew up in American public schools learning as like some of the most disastrous things that have happened in US history, and when you compare these numbers against wars, they dwarfed them. So we wanted to add that kind of scale, and clearly, there was, as is always the case, in data visualization, there’s a lot of conversation about how trying to represent a million people with just abstract shapes on a page isn’t enough, and that’s true, it’s not enough. But it is something that can provide a little bit of context to just what is the sheer magnitude of it.
JS: Yeah, I mean, I think the thing that bothers me a little bit about that whole sentiment that if we use any shape, bars or squares, whatever, that we can’t get people to really feel the impact of a million deaths or a million whatever it is, a million big things, I think that argument in a lot of ways sort of misses the point that that’s not really what these pieces are about, it’s about putting in context, not necessarily making you feel at like a visceral, heartbroken level.
DA: Right. I think that there is a place for the kinds of stories that are talking about the individual victims, and this person with their family and this person with their hopes and dreams, and this person’s photo, and you can do individual portraits of people, and that’s clearly going to absolutely break your heart, but there’s also a place for showing, like, okay, so we’ve seen this as a portrait of five people, those five people are this little tiny blip in this massive block of hundreds and hundreds of thousands of more people who are exactly like that. So showing that kind of scale is something that you can absolutely provide in addition to the microcosm of who these people are.. There’s a lot of space on the internet, we can do all of it. No data visualization, no journalistic project has to be everything for everyone, and that’s something that we have to talk about a lot.
JS: Yeah, I think that’s right, and I think it’s often like more of a body of work to look at, rather than each kind of individual piece. I mean, I look at some of the folks on your team, like, Will Chase, and Will’s been on the show, and I go through his work, and it’s sort of like, you kind of see this body of work, and sometimes he’s playing around, like, he’s playing with the illustrations, and now I know there’s an design team, so he’s clearly working with them. So yeah, I think oftentimes, maybe we in the DataViz field pick a project from an organization that’s putting out a lot of stuff, and picking apart, and not see this larger narrative that they’re pulling together.
DA: And, I mean, you see that all the time with journalism, in general, like, why are you covering this story when there’s also this other story that’s more important. Well, we’re covering that story too. We are covering both stories. And so, yeah, that’s definitely something that DataViz is subject to is, yeah, we can do million COVID deaths, we can do midterm Google trends, we can also do hot dog prices in Detroit. These are all things that we can do. I think it’s one thing that I think is kind of important to notice that in data journalism, in general, we’ve had a rough two years, it has been a lot of counting deaths, and every day, it’s putting out more charts of horrible things, it’s been a really rough couple of years for data journalists. And in addition to all of that work, I definitely like it when my team can kind of branch out into the silly, branch out into the more abstract, and do something that they can actually have fun with, because they need a break, they need a break from the misery.
JS: So then let’s pivot away from the misery a little bit, although, maybe depending on your view. I want to talk about the new election project. So this is, I’m going to put the link on the show notes so people can check it out, but it’s a collaboration between you all, the Google News Lab and Alberto Cairo, is that right?
JS: Okay. And so, this seems to be sort of a different type of piece for your team, it’s more of a big exploratory piece, so can you just talk about it a little bit?
DA: Yeah, so we’ve been working with Google Trends for, I don’t know, probably about a year on a few different projects. No, actually, we’ve been working with Google Trends for several years. We put out, every year we put out the [inaudible 00:24:28] news Sparkline that always gets a lot of attention, and that one’s fantastic. And so, spinning off of that partnership that we have with them, we’ve done several different projects with them, and this is definitely the most ambitious so far. And yeah, we want to visualize what are the trends that are really going to, what are the issues that are really going to matter in November, what are people really focusing on. But we also want to make sure that we’re kind of tracking it in real time, we’ve been collecting this data, I want to say, since around Christmas, and so, we’re collecting this to see what the actual trend lines look like throughout this entire process. We’ll be updating it, I believe, weekly, to just show how things are changing as we get closer to the elections. So you’ll see that we released it today, the data is from last week, I think ending last Monday.
JS: Yeah, the 24th, I think is what I saw.
DA: So you’ll certainly see that, like, gun control is not quite as high up as it will probably be in our next update. But because of that, we’re getting these snapshots in time of what are people talking about, what is at top of mind right now. I think what’s really interesting about it is seeing there’s a barcode element that I think is just really fantastic, and being able to see what is the overall distribution across all of the districts, but then selecting whichever district you want to focus on, your district, and seeing where it falls in the barcode I think is just a really cool feature to see just what matters to us.
JS: Yeah, it is interesting. I feel like I have seen more maps being combined with some sort of distributional elements, so that it’s, if it’s this one’s congressional districts, so you’ve got, I don’t know how many districts there are, there’s 3000 zip codes or whatever it is, so all these lines, but I’ve seen other ones, where it’s like, there’s a little bar chart above the top, I use a thing where I put like convert the legend into a bar chart, just getting that distributional piece because you looking at this map, and you’re like, I mean, the classic is the presidential election, like, Wyoming all red, but I don’t know how to see the distribution.
JS: The other piece that I really like about it, and I tend to, once I play around with it, then I look at like the small things is you own sort of this little information tooltip thing that tells the reader what a cardiogram is, because you’ve got like the corp, so people haven’t seen it, it’s a corp of map by congressional district, and then just little button you can click, and it will toggle over to a cartogram, and so, it all sort of animates into these little boxes around the thing. And there’s just like this little perfect explainer like what a cartogram is.
DA: So the origin for that, anyone who follows me on Twitter has probably seen me explaining cartograms to readers who, not so kindly asked me if I know where Louisiana is. Every time we use a cartogram, we end up getting comments of like Axios, they’re so stupid, why don’t they know where Iowa is. And I’m just like, I’m in my 30s, I’m a DataViz professional, I’ve seen Arkansas, like, this is not… So I always have to explain what is the purpose of a cartogram. The purpose of cartogram, being that it’s a map form where you distort the map, in this case, to show different geographic areas with the same size and shape, so that larger geographic areas don’t get more emphasis than smaller ones, basically, that Texas should not be more important than Rhode Island in certain cases. So I always explain that to people on Twitter, and most of the time they come back and they’re like, oh wow, I didn’t actually get that, thanks for explaining that. And so, my team kind of jokes, like, it’s my personal mission to just teach people what cartograms were. So I really loved that when they built this, and kudos to Lemond, because we definitely heavily, we were heavily inspired by them for the way that Will built out the cartogram, showing the boxes inside of the state shapes.
DA: And Jackie wanted to include an explanation of what a cartogram was. They were worried that if they just put that word there, people weren’t going to bother clicking on it, because they don’t know what it is, and so, they wanted to explain what it was. And so, I’m pretty sure that she almost, word for word, ripped off one of my Twitter rants. And so, that ended up making it in there.
JS: And so, Twitter is good for something.
DA: It’s not at all.
JS: Yeah, that’s right, it did remind me of that Lemond cartogram from the election, and it’s the core platform, it’s the map with the squares inside. That’s been great.
DA: I completely adored that cartogram. I thought it was just so beautifully done. And so, yeah, we were heavily inspired by that.
JS: So I’m curious, and I won’t keep you all day, but I’m curious, because the way I talk about that Lemond map, and for those who haven’t seen it, it’s the outline of the United States with all the states outlined, and then it has little squares in each state for the number of electoral votes, the way I talk about that map is, oh, if I think about the average Lemond reader in France, probably doesn’t know the details of all the states in the US electoral votes. So it makes sense that you’d give them the geography and the sort of little unit charts on top of it. I’m curious, so for your readers, why did you include the toggle between the two – just to get that cartogram?
DA: No, I mean, Wyoming is why.
JS: It’s great, yeah.
DA: Wyoming is one congressional district, and my own congressional district in Arlington, Virginia is impossible to actually find on a geographic map. Wyoming is one congressional district.
JS: One district, yeah.
DA: It’s really important, I think, to balance those by showing that my congressional district, even if you can’t find it on a map, has more people than the State of Wyoming does, and you should be able to find both of those districts with equal ease on this kind of map. And then putting those little squares inside of the geographic states, I think, makes it a really fantastic bucket. I think that congressional cartograms are typically lots of little squares or hexagons that are roughly in the shape of their original state, but they’re all kind of grouped together, in a way where they’re very blobby. I think that the way that Lemond and now Axios has put them into their geographic states, their tiny little buckets, that really help it keep stay organized.
JS: Yeah, I like that states as little tiny buckets.
JS: This is great. Danielle, thanks so much for coming on the show. I’m really excited to see what you guys come up with next. I love all your work.
DA: Thank you.
JS: Thanks so much.
DA: So good to talk to you. Thanks.
And thanks everyone for tuning in to this week’s episode of the show. I hope you enjoyed it, I hope you enjoyed this entire season as much as I did. This is wrapping up season eight of the show. As I tell people, everyone has a podcast, but I’ve been doing it for a while. If you would like to support the show, please consider sharing it on your networks with your favorite podcast provider. Check out my offerings on Patreon, or if you’d like to get some mobile DataViz his right to your phone, check out my Winno app, two to three times a week I send out a little text with a new visualization, a new technique. I’ve got some giveaways going on with some books that I’m just trying to clean out of my office. As you can see behind me if you’re watching the video, I’ve got a lot of books in my office, trying to clean some of them out. So anyway, this is the last episode of the season. I want to thank all the folks who helped me out. Every time an episode comes up, Ken Skaggs, Sharon Stotsky Ramirez, the folks who transcribe the show, and everyone else who has joined me and taken time out of their day to talk to me about their work, I really do appreciate it, and I hope you and your family and your friends have a safe and happy and healthy summer, and I will see you in the fall. So until next time, this has been the PolicyViz podcast. Thanks so much for listening.
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