Become a Creator today!Start creating today - Share your story with the world!
Start for free
00:00:00
00:00:01
Amanda Cox on Data Journalism, AI, and Democratizing Design
0 Playsin 1 day
Amanda Cox reflects on her career path from the New York Times to Bloomberg News, highlighting her efforts to make data more accessible and meaningful through journalism. We dive into the shifts in data journalism—from scarce print real estate to the rise of AI tools—and how these transitions affect newsroom priorities, audience interaction, and storytelling techniques. Amanda emphasizes the importance of reducing friction for domain experts and considers the future implications of AI in data analysis and design.
Keywords: data, data visualization, Amanda Cox, Data journalism, Bloomberg, USAFacts, AI in journalism, Visual storytelling, Data accessibility, Media evolution
Subscribe to the PolicyViz Podcast wherever you get your podcasts.
Become a patron of the PolicyViz Podcast for as little as a buck a month
Follow me on Instagram, LinkedIn, Substack, Twitter, Website, YouTube
Email: jon@policyviz.com
Recommended
Transcript
Podcast Updates and Future Plans
00:00:12
Speaker
Welcome back to the Policy Biz Podcast. I'm your host, John Schwabish. I won't lie, folks. It's been a long few weeks. I have been busy with lots of different things, lots of people pulling on lots of different strings as the world has changed, especially here in Washington, D.C.
00:00:31
Speaker
Lots of things have changed in my research agenda and the sorts of projects that I promoting. pursuing and able to pursue. And so I've been a little busy. I've been spending a little less time worrying about getting podcasts out the door and running blog posts about bar charts and pie charts.
00:00:47
Speaker
I've even slowed down on getting my sub stack newsletters read and of course even written. But... I'm back. It's been a little bit of a pause. I've got a back level of podcasts to get out the door.
00:00:59
Speaker
And so what I'm going to do starting this week is get you five more shows for the end of this season. This will be wrapping up the 11th year of the show, 11th season. I've got five podcasts. going to do one every week. I'm just going to get them out the door so that you can enjoy all the learnings about data visualization, data journalism, and
Amanda Cox's Career Journey
00:01:20
Speaker
more. And to kick off the this sort of end of the season sprint, I am so happy to be joined by Amanda Cox from now Bloomberg News, previously of the New York Times. Amanda Cox.
00:01:35
Speaker
If you don't know the name, you haven't been working in data visualization long enough. Let me just put it that way. Amanda was one of the originators of modern data journalism at the New York Times. She is statistician by training. She is just an amazing data communicator and has worked long and hard at communicating data and think about communicating data and data journalism, and all the good things ah that make this field so interesting. And so I was so excited that Amanda was able to sit down with me from her office in new York City and chat with me about her work now at Bloomberg and just reflecting back
00:02:13
Speaker
on the last several years of working in the field and how things have changed and where things are going to go. So I think you're really gonna enjoy this episode of the podcast. If you've been working in the data visualization field for a while now, ah you are going to really enjoy this because Amanda's gonna take us on a little bit of a tour. And if you're relatively new to the field,
00:02:35
Speaker
This is an episode you should be listening to because this is gonna give you some perspective on where data visualization is now, where it came from over the last, you know, roughly decade or so, and where it's going to go, particularly in the news industry.
Expanding Data Journalism at Bloomberg
00:02:50
Speaker
So I hope you're gonna enjoy this week's episode of the show.
00:02:52
Speaker
And here's my conversation with Amanda Cox only on the Policy Biz Podcast. Hi Amanda, long time no see. Hey John, good to see you again.
00:03:03
Speaker
ah Yeah, and you have a great view behind you. And now I see the ferries going. It's classic New York. It really is. It really is.
00:03:14
Speaker
So it's been a while since we chatted. And last time we spoke, you were at the Times. And then you went to USA Facts. And now you're at Bloomberg. So I thought we would start by maybe just talking about what you've been doing since leaving the Times. If you want to talk a little bit about USA Facts. And then we can talk about your team at Bloomberg.
00:03:30
Speaker
Sure. a So you are right that when I left the New York Times, I joined this little organization in Seattle called USA Facts, which is a Steve Ballmer endeavor whose mission is to make government data more accessible.
00:03:46
Speaker
on He thought government data was too hard to access in answering some of the questions he had about his own like opportunities in the world, about like you know kind of connected to some of this philanthropic ideas about like what are the gaps where can my money do the most good um whatever and so uh set up this little uh little organization trying to make trying to make that data easier to access and and to find for people and so i went out there and spent a little bit of time, about a year and a half, um setting up a team to do some more advanced visualization capabilities there. um And then not super long into that, um I got a call from Bloomberg that said, ah we are going to do a massive expansion in our data journalism, data visualization,
00:04:32
Speaker
and associated efforts. going to have 50 new people. you want to come help lead that? um And that was a little bit interesting to me. And so I, i a after many months of talking about that with Bloomberg, joined Bloomberg to help run what are called the data journalism teams there.
00:04:50
Speaker
And it's distinct from data viz and Bloomberg structure. So it's more people who are doing ah reporting around data analysis with data. I work with a couple of different teams. One of them is mainly focused on investigative projects.
00:05:05
Speaker
um One of them brings kind of top tier engineering talent um to newsroom, journalism stories. And then one of them does some of those things ah and works on, you know, shorter term, um you know, stories that don't necessarily have an accountability angle or, you know, it's just
Challenges in Government Data Accessibility
00:05:22
Speaker
news stories, right? So data journalism at Bloomberg is my is my remit now.
00:05:27
Speaker
Nice. um Let's talk about USA Facts for just a second. So were you working on the data collection side or was there like a team doing that and you're on the more dissemination journalism communicating side?
00:05:44
Speaker
Yeah. um My, you know, the team that I worked on and, know, it doesn't, uh, was just a little, you know, we're trying to figure it out, trying to figure out what makes sense at this organization, really. And so we were doing a kind of, I think, they the nomenclature is special projects, which is like a ah ah word that I've come to think, like, I don't actually like it because I like when you're like core to the institution, right? Like, yeah but you know, in the same way, I think that, like, you know, if you don't have the staff on weekends and holidays in a news organization, you're not actually part of like a news organization, you know, like, you're that, yeah you know, like, if you could just like accent and it doesn't matter, that it like doesn't really matter.
00:06:18
Speaker
But Anyway, but so we were, you know, some sometimes ah collecting our own data, especially because ah sometimes I think the coolest data, you know, you don't get it. You know, it doesn't come in like a one click download or it doesn't come with an ingestion system. Like it actually requires some reporting. Like there was a project that I was fond of that, ah you know, it was a puzzle.
00:06:40
Speaker
At this point in time, it was still a little bit of a puzzle. Why? ah Why car thefts were rising so much in the time when other types of crime wasn't wasn't really rising. So like murders are going down a little bit. Robberies were going down a little bit. Car thefts were up like 20%. I forget what year this is. And police departments have this data by.
00:06:57
Speaker
and ah police departments have this data by a yeah So we got some clues that it was about Kia and Hyundai being stolen with USB ports, right? Like there is a TikTok trend that people thought, but that data by making metal, like the insurance and groups had it, but like it wasn't part of real government data, except you could ask police departments for it and they would give it to you.
00:07:23
Speaker
So like, you know, one of the projects that I feel like I left most fond of there was just like the brute force, like let's just ask 50 police departments for this data. And so like in in that part, It's a very long way to say that like we did some of our own collection slash ingestion, but not in the way that like of the like bulk scaling that like USAFacts really dreams about doing.
00:07:45
Speaker
Yeah. Yeah, that's is ah interesting, particularly in in our current a climate around federal data, which we can talk about in a second. How do you make data easier, more useful? I kind of feel like data.gov was the first effort to do this.
00:08:02
Speaker
um But there's just so much of it. it's ah It's a really hard problem, a real hard nut to crack. um So,
Evolving Media and Data Journalism
00:08:11
Speaker
okay. So let's turn to to Bloomberg. So I guess I'm curious about how like your new team, does it expand, extend, broaden what Bloomberg was doing sort of before? Like what is the expand what is the goal of that bigger team?
00:08:26
Speaker
Yeah, I mean, it's, you know, more, better, faster, those yeah yeah yeah those sort of goals. um and like we did You know, Bloomberg has people, you know, like my colleague, David Ingold, who's been doing data journalism at Bloomberg for more than a decade.
00:08:41
Speaker
But it's about, you know, turning his team from a three person endeavor to a 12 person endeavor around the world, right? Like that sort of, ah like, I think, you know, aye more smarter,
00:08:54
Speaker
ah is sort of the though the big sort of levers of expansion. Break more news, that kind of what kind of idea. Yeah. It's, it is interesting how, well, let me ask you this way. Like you've been doing data journalism a long time, like, well, not too long. Let's not age ourselves too much. for like A long time. It's a long time. So like, what are the big, like, do you view that we can start here, but like, do you view this expansion as like a kind of like odd or massive change in the way media works? Like you don't hear about a lot of places expanding their graphics desks anymore.
00:09:28
Speaker
Is that true? don't Maybe that's true. Um, I don't know. Maybe I only read like the times in the post and like, yeah. yeah I mean, the times graphics department has gotten quite large, right? Like in my era, it was like 25 people. And now I think it's like closer to 50. So like, I, that could be lies. Don't like, I haven't been there in years now, but, um,
00:09:48
Speaker
you I think ah Bloomberg data is at the core of its identity. And so there are questions about like, why are we not the best in the world of this in our newsroom capacity, right? like Which is quite different from a lot of other people's jobs at Bloomberg.
The Role of Mobile and Interactivity
00:10:01
Speaker
The giant organization that like, right you know, you trade your assets and you monitor a law and you do all kinds of things on your Bloomberg terminal. But like, you know, I do think there was some question about like,
00:10:13
Speaker
given what Bluebird is and what it wants to be in the space of data, like why are we not, you know, we should have more of this and we should be better at it, is sort of the... you know, is the thinking behind that. Yeah. Okay. Yeah. I mean, that makes sense. I mean, they do have, they are pulling in, I mean, they are ingesting like a ton of data, right. And they are the best at it probably in in the world.
00:10:36
Speaker
So then a broader question for you, like in your work, like what are some of the biggest changes you've seen in the world of data journalism?
00:10:47
Speaker
Yeah, I mean, if you define data journalism more broadly than in my current job title, but to like include the world of data viz and news media, yeah i do you know I have sometimes thought about changes in terms of, or I like to think about it, and I know that you are an economist, so you will like this too, is in terms of like what is the scarce resource?
00:11:10
Speaker
um So when I started forever ago, the scarce resource was really front page print columns, right? Like there's only in the New York Times as an an organization it's putting out one front page a day.
00:11:21
Speaker
ah There's only so much space on it, like, you know, like whatever. um Then I think there was an era where the scarce resource, you know, the thing that you had to fight hardest for to get or to save for your best ideas, or sometimes just use as signaling that this is the thing that we care about most because, you know, what do you spend your scarce resource on? It's the thing that it was web talent for a while, right? Like these were not jobs who had ah the world's best ideas.
00:11:45
Speaker
ah developers and engineers and thinkers about what the possibilities are on the web. So like those people, ah but the people who are amazing at that and best in the world at that, they were rare and special and you wrote them for save them for rare and special things.
00:11:59
Speaker
um the as that and that was a fun era because i feel like it was new and it's always fun when you are like kind of making the rules of the game or at least it's fun to me i've come to i've come to realize that but i like it when the like game's not established enough that you get to like you you know but who who decides who wins it's like well i decide who i win you know like i decide what the rules are so you know so first scarce is uh uh front page space physical space and a print pi product next era of scarcity is uh
00:12:31
Speaker
uh tech technical skills like how what can we do on on the internet and like in things that were not even possible in print because you're you know starting to think and then after that you but that got uh more uh you know like that work was popular so people hire more people who could do that it got a lot easier too the frameworks in the world have made some of that work a ton easier like you know like i am old enough that like I remember a time when making a county map was hard. And now I often say that, like, you know, and I can teach any eighth grader how to make a county map this afternoon. And like, you know, right like yeah so like the the tech improvement a lot. And then there was an era like what what is most special at the time for a little bit was like,
00:13:11
Speaker
these things that were called like panel eights, which were like eight pages of print, right? Like they would because it was like the front page, especially in the COVID era, and they would put a chart on that like every single day and not even a good chart, right? Like not every day, but like, you know, they would put a four by six, like giant, terrible chart with like nothing really behind it on it. Like so that, you know, it's no longer, but it was a big print, right? pink Print that is so expansive.
00:13:34
Speaker
I can't, uh, you know, I can't express my idea even on a whole page of newsprint. I need eight pages of newsprint back to back that I unfold them to express my idea. And I think that's related to like some of the the other changes um in the work in that like part of that, I think, at least in part, is a semi like a reaction to mobile, right? Like what's the opposite of eight pages of newsprint where I'm really working about hierarchy and how things like pull together. ah and And that is not to say like I do not believe mobile's are so limiting, right? Like I think
00:14:05
Speaker
sometimes we think the era of interactivity was was killed by mobile, except people who are really good at mobile and interactivity have changed the world, right? Like people buy houses on mobile phones. People like start relationships on mobile phones. Like those are both like interact, you know, people who get driving directions on mobile phones.
00:14:26
Speaker
Like they're very, it's like, if you do it right, it is actually your mobile screen. It is actually a very expressive medium. But if you do it like, wrong or isn't in that an adaptation? of Like, it's just a different set of skills. And so yeah I think of that, I think of that scare, you know, the changing thing of what is scarce, whether it's about a specific type of print space, is whether it's about ability to do things online, whether it's about ability to know how to use the space that like your audience is going to encounter. And I think that is one of the evolutions that that I have seen in this kind of game, more broadly defined, especially to include the include the visualization part.
00:15:05
Speaker
Yeah, yeah that that that's a really interesting take on the on the
AI's Impact on Data Visualization and Journalism
00:15:11
Speaker
evolution. What do you see? i mean, if you if you knew this, you'd be a ah gazillionaire, but like what do you see as the next, or or maybe a better way to put this, like where is your team sort of moving now looking ahead?
00:15:28
Speaker
Yeah, I mean, there is this question, you know, like the sort of death the era of data is that I think that I was best at, like, you know, the in the the I define the rule sort of era, that era is over, it's gone, right? Like it's killed by TikTok.
00:15:43
Speaker
ah You know, it's like do people do not want to consume information in the way, very not even, you know, TikTok, but as a like a short form video is like, yeah like that's not a game I am good at. But I do think there are ways the world is changing. Like, you know, I am currently,
00:15:59
Speaker
uh probably too bought into the hype of ai i think it's changing our work i think it's changing what's possible i think it all has already changed that in like ability to unlock things at quick speed that have some like semi-structured things i think you know i'm to the annoyance of some of my colleagues i am i've bought into the hype right like i am i am i've completely like i think the world has changed in the last two years under our feet and like only five percent of the world knows it and i am like one of you know i'm a crazy person in that space but i think it's like unimaginable that that space will not or should not change change this type of work yeah even for other stuff too like even on the vis side like i am i completely believe that there's a really interesting dance that goes on ah between like
00:16:52
Speaker
text and design options and data choices in visualization. And that like defining two of those should get you to like getting the third one almost automatically in smart ways. And there's a ton of people I believe who like want to be better designers and want to make charts with points, like as you, you know, as you know, in your book and like,
00:17:13
Speaker
Like they wreck, you know, it's one of the like, I recognize my work is bad and I just don't know how to make it better. And I do think that we are in the cusp of an era where you can just make it, I'm trying to emphasize this, make it better, you know, make it in or, you know, and, and worry about, worry about the medium that it's going. Here's the annotations I want to put on.
00:17:34
Speaker
ah They need to work on mobile, like do your best, right? Like, I think that is a challenge that should be, should be easier by now. in terms of like what it was new or going. But also like, you know, some of what is new is also what is old, which is like use use this work to break news and figure out stuff about the world that you couldn't figure out in any other way.
00:17:55
Speaker
Like some of this stuff is like, ah you know, specific web frameworks are not going to last. But that core value of like, we are doing this work to get to a headline. We are doing this work that is immutable.
00:18:09
Speaker
Yeah. immutable yeah do I wonder, do you view it as as potentially problematic in the sense that do we want to get make tools that anybody can do the entire process of data ingestion, analysis, and visualization?
00:18:29
Speaker
Or do you still think that you know you kind of do need to know some core things about data and about analysis and about statistics, or it do you think it is gonna be sufficient to ask the AI tool to kind of do that process for us?
00:18:49
Speaker
I'm a believer in democratization, right? like Like, you know, even that, like, silly county map example, though, like, I want more people with real domain expertise ah feeling like they can get their ideas done with um very little friction. Like, I'm a total believer in that.
00:19:07
Speaker
Like, are there ah stupid things you can do with data? Sure. But there are stupid things you can do without data, too. So, like, I don't view, like, you know, I don't feel like it's a one path, like,
00:19:18
Speaker
is dangerous and one path is not dangerous in a way of like but i do i am a believer that like uh i'm a total believer in domain job domain knowledge people who know stuff about subjects they're gonna ask better questions they're gonna notice better anomalies and so like i'm a complete believer in uh reducing friction for people with domain knowledge Okay.
00:19:42
Speaker
Yeah. I'm with you. I mean, it's the reason why I like the tool Canva, right? Because like, I'm not a designer, but it it lowers that barrier. Even on simple things like resizing designs really quickly be for different uses.
00:19:56
Speaker
it just It just democratizes design and a way because I'm not a designer. And I'm sure there's lots of designers who hate these tools sure um because it potentially, you know, takes money out of their hands or, you know, work out of their hands, but, um, or you see it, right? Like you don't get the true greatness, but like, there's lots of work that happens in the world that is like 80% greatness is good enough or, you know, 80% goodness is good
From Static to Interactive Visuals
00:20:23
Speaker
enough. Right. and And there is still room for true greatness, right? Like there is totally room for, and I think the true greatness stands out even more in a way, but yeah.
00:20:32
Speaker
Yeah. um So this is ah my observation of of the evolution in the last, I'd say, you know, 10, 15 years maybe of data viz, is that, you know, up until a certain point, everything was static.
00:20:45
Speaker
You know, we had Flash, but like everything sort of popular was static. And then like D3 and other open source tools kind of came out and like everything was interactive, right? You could click on every bar chart and every line chart and there was a pop-up and a hover.
00:20:58
Speaker
And then there seemed to be this big pullback, um maybe because people realize it's hard to do with the mobile versus print versus desktop was sort of like really expensive and hard to do.
00:21:09
Speaker
Is that, is do you agree with that? Is that how the sort of pattern that you saw?
00:21:16
Speaker
I do agree with that. I do think I would note, you know, the era when everything was interactive was not a good era, right? And when you, when I actually, we you know, sometimes when I look back on some of my own work from that era, the version that holds up or comes comes closer to holding up is the mobile version of,
00:21:36
Speaker
where more editing was required. Even at the time, I think I hated it, right? It was like, why would I bother? Like, whatever. But I think, and is especially coming at it from a journalism space, it's really some of it, uh,
00:21:51
Speaker
is about, you know, we're, we were not great, like UI designers basically. And so we're pushing work onto people to be like, you know, i sometimes talk about it as like, that here is some data. I hope you find something interesting.
00:22:05
Speaker
um yeah That kind of like, you know, that is not actually doing like real journalism other than in specialized cases. Like I do think there are some cases where like ah just opening up that access to people is helpful and good work. And so like, there's no blanket statements about what is right or,
00:22:20
Speaker
what is right or wrong um in any of this but i would i would in general uh agree with your assessment i also think i think it has made it way harder uh for people in some ways uh getting into the work, right? Like, or you know, because when I started, like I was making bar, you know, bar charts and line charts for print. And that work is like very easy to edit. It's very easy to train on.
00:22:48
Speaker
Like, it's very easy to say like, okay, you were not good at this. And then, but, and there was like one version
Essentials for Aspiring Data Journalists
00:22:53
Speaker
of it. There was one size of it. It was a static file. it went with whatever. It's much harder to edit work for from someone who's young and new at this. Like, I would not be successful in the current like environment of like,
00:23:05
Speaker
make something on the internet and like parts of it move and like your editor doesn't necessarily even know how to edit it and there's like infinite like possibilities of how all the parameters would go together I do think like uh there is something I think collectively like a grappling with like how do people learn how to do this work anymore like when the on-ramp like the on-ramp has gotten just a lot a lot harder I think yeah even though you know the also because you know more people are doing the work and it's like more well known even as like a possible career you know the the pool of people like possibly interested in it is much larger so like that that talent goes up but the the introductory degree of like difficulty and like intro work i think has gotten so much harder yeah um so so on that i wanted to ask
00:23:51
Speaker
I had a discussion with a grad student yesterday who's you know sort of getting towards the end of their program and interested in journalism and data and data visualization and sort of trying to figure out like what is the right or what not the right path, but what is optimal.
00:24:05
Speaker
And I'm curious what you would say to that student, not so much on the technical skills, right? Like, yeah, go learn this tool or that tool, but like, what are maybe the soft skills or the traits that you look for?
00:24:22
Speaker
You know, I'll make it a little more concrete. Someone applies for a job on your team. Like what are the soft skills or what are the intangibles that you're looking for, um for people trying to enter the and enter this particular area?
00:24:36
Speaker
Yeah, I think one of the things that I admire most about journalism as a field is that it's a quick space hiring process. Like, you know, I'm someone who's, you know, come up with a year of doing a whole bunch of hiring. And I really don't care where you went to college or if you went to college, honestly. like um But I do care about seeing work that demonstrates your ability to master some subject mastermind. some some some domain of expertise some command of some subject matter um to you know that you're brimming with ideas and coming at things with curiosity and able and want ah to get past like weird roadblocks that you didn't like have a sense of like that you were going to get past and then and then there's a bunch of ways to win right like there are people who win at this game by being really excellent traditional reporters uh there are people who win at the game
00:25:32
Speaker
by being having top-notch tech skills and ability to turn other people's I you know ability to translate ah you know other people's ideas and detractable things is one ah one way that people win um some people win by just being like super pleasant to work with or enthusiastic about about things right like and so i I don't think you know I think there's a ton of ways to win but the goal is like You got to publish something and you want to publish something that breaks news and feel smart. And, you know, it feels like you are ah figuring out stuff about the world. And in in in all of those things, I believe that the real scare skill has nothing to do with technical skills. It's about ability to identify good questions.
00:26:18
Speaker
Right. Like it's about ability to decide, like, let's point all of our energy at that. That is something that like seems like it could pay off.
Collaboration in Storytelling
00:26:27
Speaker
Yeah, it's funny ah hearing you talk about this because I'm um in the stage, my my daughter's a senior in high school. So we're in that sort of college application stage.
00:26:37
Speaker
And it's very clear as we're going through this process that what the schools look for now is different than when I was applying for school. When I was applying for school, was like, how many clubs were you in, right? It didn't matter how invested you were how, and like, it was like raw quantity.
00:26:53
Speaker
And they're clearly now looking for quality. They want you to be invested in that thing. The thing that you know you're passionate about, the thing that makes you really you know sort of, i maybe in they're in the colleges, thing or they would call it unique. But like what is that thing that really makes you passionate? It's not about quantity. It's more about quality and deepness.
00:27:15
Speaker
um Whereas I kind of felt like when I was applying to school, it was like mile wide, inch deep. Yeah. Yeah, was like film club. I remember in high school being part of the film club and my mom would ask, do you discuss the films? Do you debate them? I'm like, no, we just watch movies.
00:27:33
Speaker
So it it is interesting to hear you say that. it It does sort of ring true of what I've been seeing on the you know the college application side that schools at least are looking for that depth, not necessarily the breadth.
00:27:47
Speaker
That's interesting. Yeah, I should be careful too, right? like I think I have been fortunate enough to work at some a relatively snobby institution. So there are people like who would still care, ah you know, at other places, you know, I do not speak for all of journalism when I say like, it doesn't matter if you go out to college, right? Like that is totally and oh yeah like the idea about like, what what are you creating and what are you really into? And have you, can you demonstrate like enthusiasm and agency about something? but Yeah.
00:28:12
Speaker
and But, but your point about being able to ask good questions I think ah enables a person to be deep and have domain expertise, but also to be nimble and to sort of be able to be, have domain expertise in some other areas as well.
00:28:30
Speaker
Yeah. I mean, I think there are, there are journalists who are incredible at that, who like, you know, like you throw them in a field who they know nothing about. and they can find the five people who and want make them want to like be helpful to them as sources because you know because they're fun to talk to and they feel like you're connecting ideas and you feel like you know there are people and like i that is you know uh that doing it through reporting is less my own skill than like just being patient enough to read all the footnotes right like you know so there's even in that like there's a ton of different ways to play the game about like
00:29:02
Speaker
how are you going to get to a level of expertise like fast and like, just like, what it what is, what are you most really sure? So that was another question I i actually had for you. um Have you found the relationship between sort of a the data team, data journalism, whatever you want to call the team, ah the relationship between the sort of data teams and the kind of traditional reporters, journalists, has that relationship changed over time? And like,
00:29:32
Speaker
How has that changed? or is it Or is it still like reporters go out in the field, they send stuff to you? Like, how has that evolved? Yeah, I mean, I think one of the things that I was so lucky at when I was part of the Upshot at the New York Times, and I didn't even realize like how lucky and rare it was in in real time, is that we were a team that had people who were traditional beat reporters with domain expertise and sources and cutting edge ideas,
00:30:01
Speaker
and people with data visualization skills. And it was one team. like it the teams did not cross boundaries. And I think about this a lot. you know Boomer a giant place with tons of resources, but also like because it is a giant place with tons of resources and many different kind of like semi-overlapping pen diagrams, I've been spending a lot of time thinking about that. like how how do we make those team borders artificial and porous so that it feels like real collaboration in this work? And most people have no idea what I'm talking about. Like I have, I have a teammate who's fantastic.
00:30:35
Speaker
who There's this example that we've been fighting about for he a year. He was like, no, that was fantastic collaboration between, we between different people. And I was like, what on earth are, have you ever worked on a real actual team where people like actually really like, We respected each other and we're like playing that like yes and improv game and it felt safe and it like you know and knows and it was our end product.
00:30:57
Speaker
That is the thing that I think the best work is and another thing that I am pushing now at Bloomberg that it feels so much better when it's our draft. like you know and are being the people who are making things across different different disciplines different skills whatever but because i do think there are things like for the best data visualization for example sometimes the reporting needs to be totally different and there are tons of examples of this that make total sense about you know even if you're thinking about like breaking new stuff like there was a classic one i think it was like the mh370 i think it was that plane crash where it was like you know it's lost in the ocean
00:31:34
Speaker
and youre And you want to know like what are people trying to do to figure out where this plane is? If you want to make a map, you want a map, you know, your get when you're recording is about a giant spreadsheet of ah wave heights and wind speeds located across these thousands of miles of ocean. Like your git is a spreadsheet.
00:31:56
Speaker
If you're a traditional reporter, ah you may be actually chasing exactly the same question, but like you don't want your source to send you a, you know, gigabytes of spreadsheet data. Like, you know, it's like, just tell me, tell me what you see when you look at this data. I don't want to show it. So I think, you know, the reporting,
00:32:13
Speaker
like needs to be different ah to make the actual best stuff. And so what I think we're trying to do and other people are trying to do is form little teams of people who recognize that, who want in on that, who recognize that like, like, you know, I think there's the other thing about is, uh,
00:32:32
Speaker
bending, you know, the the other thing at the outset that I took, it you know, you didn't realize it. I think I realized maybe a little bit at the time, but how special it was to be like, yeah, I'm going to write the lead of the story, taking in mind what the top graphic is.
00:32:46
Speaker
I'm going to make the top graphic figuring out what the lead is. And both of us might have to bend on that. Both of us might not do the thing that we would do if we were just doing it. If we were playing our own game, if this was a solo solo endeavor, I might make different choices.
00:33:03
Speaker
and But I think it's interesting when both parties are willing to bend a little and say, like, OK, because we are Because we are communicating with all of the tools at our disposal, which include words, which include ah visual patterns, which may include video or who knows what else. like yeah how How do I think about how those things are coming together as opposed to like, okay, I made my word draft.
00:33:29
Speaker
Can we insert some pictures into some some chunks of it? like It's just never going to feel seamless.
Closing Remarks
00:33:35
Speaker
Yeah. yeah Yeah, this is ah an issue that I face all the time. You know, how do you sort of bridge the gap between the communications sort of team or division or whatever, and the more analyst researcher side, who they're often talking past each other and maybe don't.
00:33:58
Speaker
necessarily kind of fully understand the skills and the pressures and the demands on, on, on either side. Like, do you, do you have a strategy when a reporter comes to you or you come to a reporter to sort of try to bring the two sides together?
00:34:16
Speaker
Part of it reps and trust and part of it figuring out ways to recognize competence and show up where people are like, yeah, this was better this way. Right. so think, you know, it's a machine. I have no magic answers ah to this question. And I think it's probably the real answers depend a lot on who the people are and what their actual priorities are. and yeah But I do think there is also some insistence on like, like just this morning I was going on a rant about like,
00:34:46
Speaker
i If a draft exists and I can't see it, like we're not playing. It's not worth my talent. right like that so That kind of like insisting on being like, oh, we're in this game together. And like you may have completely different priorities than I have, but if we want to actually make it fun, like we're getting both going to have to like show up with some level of ownership.
00:35:06
Speaker
ah Like I'm very like anti the conveyor about like in the situation you do prescriber. It's like here I've written my blog post and it uses words that like no regular human is going to understand. I talk it over the fence to my communications people. They either water it down or do whatever. they You know, like that's not fun for anyone, right? like Yeah, right.
00:35:23
Speaker
Yeah. Well, on that note of fun, um this has been great. It's great to see you again. um Welcome back to the States. And yeah, thanks so much for coming on the show.
00:35:34
Speaker
Sure. Thanks for having Thanks for tuning in everybody. i hope you enjoyed that episode of the show. I hope you'll check out the entire catalog of the policy. It is podcast, which you can check out on my website, policy.com.
00:35:48
Speaker
You can check out on Zencastr.com. You can check out an iTunes. And of course you can find many of the episodes in video form over on my YouTube channel. And of course, don't forget to subscribe to my sub stack newsletter. where I write a preview of all of these different shows before they post, along with sneak peeks of what I'm writing, what I'm reading, and other things that I'm thinking about.
00:36:10
Speaker
So until next time, this has been the Policy Biz Podcast. Thanks so much for listening.