- All right. Hello, everybody, and welcome. So we at Observable are so excited to host
- this discussion on how data journalists approach storytelling with data. So in a moment I will
- be introducing our panelists, but before I do, I just want to go through some quick logistics.
- So first of all, one of our panelists, Jackie Schrag, unfortunately will not be able to
- participate today, but we do hope she can join us in a future session. Now, what to
- expect today. So in just a moment I'll introduce our panelists and we will dive into our conversation,
- which we expect to run for about 30 minutes. After we wrap up the roundtable, we're going
- to have about 10 minutes of Q&A, and you're welcome to either add your questions in the
- chat during the roundtable at any point, or you can wait until the Q&A to do so. And we'll try to
- get through as many questions as we can in that time. Also, this webinar will be recorded, so
- don't worry if you have to hop early or if you want to revisit it in the future. All the
- registrants will get access to the recording, so you can always come back to it. Now, without further
- ado, let's welcome our panelists to the stage. So welcome, Ben, Kavya, and Jared. First, let me
- do a quick introduction of myself. So I am Will Chase. I am a senior product designer here at
- Observable. But prior to joining Observable, I did work in data journalism for about four years.
- I used to run the visual storytelling team at Axios, so returning to my roots here.
- So first, I want to welcome Ben Welsh to the stage.
- Hello. Welcome, Ben. Hello, hello. So Ben is a reporter, editor, and computer programmer. He
- is the founder of the Reuters News Application Desk, which covers the world's most pressing
- stories by developing dashboards, databases, and other automated systems. Welcome, Ben.
- Thanks for having me. Great. Next, I'm going to welcome Jared Whalen.
- Hello, Jared. Okay. So Jared is a Philadelphia-based journalist engineer
- at Polygraph and The Pudding. Previously, he's worked at Axios, the Philadelphia Inquirer,
- and the Delaware News Journal. His background includes visual storytelling,
- web application development, and data journalism.
- All right. And finally, let me welcome Kavya Beharaj to the stage.
- Hey. Hello. Welcome, Kavya. So Kavya is a designer and developer based in Brooklyn.
- She is currently an associate editor of data visualization at Axios. And before that,
- she worked in the nonprofit sector managing data projects, building web apps, and training others
- to better understand and use their data. All right. So thank you so much, everybody,
- for being here. We can go ahead and jump right into our discussion questions. So first, we are
- going to do a little bit of show and tell. So I'm going to ask each of our participants to share
- what they felt was one of the most impactful visualizations that they have made or worked on.
- And talk a little bit about it. Talk about, you know, what they think made it so impactful.
- So let's go ahead and start with Ben. Go for it, Ben.
- Hey. So I'm going to share my screen, and we're going to step into a time machine
- back to the year 2012. Who remembers that? And this is a graphic I worked on at the time with
- Robert Lopez and Kate Lithicum, two of my colleagues then at the Los Angeles Times
- in Southern California. And we put together this map, which is called How Fast is LAFD,
- where you live, to chart the response times to 911 calls by the local fire department
- there in the city. And you see it visualized there on the map, that sort of sideways key
- shape, believe it or not, is the actual city limits of Los Angeles itself. And it's surrounded
- by 87 other suburbs in LA County. And so if you ever see a weird shape like that, that's
- just because that's how the city was drawn up more than 100 years ago. And that is the
- jurisdiction of the city fire department that respond not just, of course, to structure fires
- and wildfires within the city limits, but primarily to emergency medical care situations.
- And there had emerged at the time, a sort of political controversy on a then very popular
- local news website called the Huffington Post, where a candidate for mayor who is trying to
- run for mayor again now in 2026, believe it or not, made some allegations about the fire department
- being substandard and responding to 911 calls. And that created a little bit of a political
- controversy and kind of raised the question within the politics of the city about what's
- going on at the fire department. And then curiously, the fire department invited me and
- Kate Lint to come over one day because we were asking, what's up? You got any data? Let's try to
- answer the questions from this Politico. And they called us over to City Hall East, if you've been
- there in downtown LA and put us in a conference room and they said, well, we got to tell you guys
- something. All the statistics we've been publishing for the last five or two years or so,
- they're all wrong. You know, the guy who had been our stats guy, well, he retired and it just kind
- of been on autopilot and we were looking at him and we realized he just did the math wrong.
- It was one of the stranger sort of moments as a reporter to just like be invited in for a
- confession, you know, and I think it really was kind of a well-meaning moment. They were trying
- to be transparent, but it's also pretty embarrassing, right? And so the front page
- of the newspaper the next day had a story by Kate and I that basically said, look, they say
- they don't know, right? And that just made the question an even bigger question. What the heck
- is going on with 911 at the city? This candidate had made very specific allegations. He couldn't
- really back him up and we couldn't, the department didn't really have an answer. So we filed a public
- records request for the complete database of millions and millions of 911 calls to try to
- answer the question ourselves, which we felt was kind of now in the public interest. And there's
- a whole long story I won't bore you with about fighting for the FOIA and getting the data and all
- the characters and blah, blah, blah. But at the end of the day, we kind of did our own analysis of
- those millions of calls and we used an outside standard, which is always a really powerful way
- to structure any kind of accountability work you do with data. So you see color coding on this map
- where green is good and purple is bad, pretty standard. But those thresholds and those cutoffs
- and the colors were all based on sort of the national response time standards of how fast
- calls are supposed to be. And that was a standard that the fire department itself had embraced and
- said it was doing well on. And so we were able to bring to bear sort of an outside standard that
- allowed us to evaluate, you know, kind of transparently and clearly and with, you know,
- colors on a map accountability or bring accountability to the question. And, you know,
- in any data visualization, if you want to get more investigative, you know, having that outside
- standard or something to measure up against that isn't just like according to you or according to
- its standard deviation in the distribution, but some real benchmark, right? So we had that strong
- benchmark and then it turned out, believe it or not, we had a reveal, you know, and that's a bit
- of Reuters ease. That's the term we use here at Reuters. When your report is going to tell the
- world something, it doesn't already know. And to me, that's the second piece that makes any powerful
- investigation really break through. And, you know, the reveal really was that the politicians claims
- were all wrong. You know what I mean? But that really was when you looked at the data closely,
- there were two or three really significant weaknesses and how 911 care is being delivered
- that were not being discussed in the city like at all. Like we're not even on the agenda. No one
- even talked about them. And they really surfaced and came up out of the data. And then we use that
- data to follow it out into the world and report on real world cases and show the impact and stakes.
- And so this visualization ran online as an interactive map, allowing people to personalize
- the story, which, of course, is another classic way to bring data out in journalism to look up
- their neighborhoods, but also to kind of tell the overall story, which ran on the front page of the
- print edition the same day. There's the print map that my colleague Tom Lauder put together
- with slightly different styles, but kind of the same idea. And you can see that the first
- angle we ran with the first day, and you might have to know LA to pull this out of the map,
- was that it was actually the richest neighborhoods in the city had the slowest response times,
- which is the Hollywood Hills where all the fancy movie stars live up at their mansions next to the
- Hollywood side. And the wealthier areas had the slowest response because they're in rural hillside
- communities is like the fundamental reason. But that was kind of the opposite of the way people
- had been talking about the issue. And so that was kind of a way into it that was kind of a reveal
- that gave you, hey, the truth of it is that it's actually the rich people who get the slowest
- response on average, and we built the whole story around that and told some real life cases of where
- people had died when calls had gone wrong in those areas. But if you look at the map closely,
- you'll see some other interesting things too, which is like, and this really stuck out to me
- at the time, is like, look, it's like the edge of the city on the borders is where it's really slow.
- What's up with that? No one talks about that. Well, we looked into it and it's common sense
- when you think about it, the fire trucks all sit at the fire stations. And if you're going to build
- 108 fire stations, you don't put them next to the city border because you want to put them where
- they have a radius around them that they're going to serve. But then geospatially, you guys are all
- smart and dated people here, you have a weakness. You're then slower to get anywhere near the
- borders and particularly that long thin strip of the city that runs down to the harbor. And so we
- did a second story that was about how these boundaries and the lack of connections with
- neighboring fire departments to pool services was another big issue with the system. And then we did,
- this is a boring story I wrote because we needed to have one that day. But then we did a fourth
- one that was about how actually when you looked at the segments of the call, from when they pick
- up the phone to when someone arrives at your door, there's two or three segments in there of like
- triaging what happened, sending somebody getting to the place. We found that that early call taking
- period was actually where the biggest lag was in the whole system. And we were able to focus on
- that discrete flaw. And we did a whole lot of other coverage and lots and lots happened. And
- it was all tied up in city politics and an election that happened. But the result is,
- is the next mayor made really deep and significant changes to how the 9-1-1 system works,
- including revising how the call taking is done in the calling scripts. And also established a
- new statistical unit in the fire department to do the type of analysis we were doing internally
- within the department for itself. They called it fire stat. And the fire chief was replaced by,
- as well as part of the new administration. And then that ultimately kind of sent me down this
- road of becoming a fire department reporter, though never really planning to, which yielded
- later investigations about a failed building inspection program that led to overhauls there.
- And we were able to uncover and expose nepotism and abuse of the hiring process that led to
- complete reform of that as well and a few other things. And it all started with trying to make
- this map on my laptop in like summer of 2012. And so that's my long and boring tale. I hope it
- wasn't too long. No, not at all. I was going to say not boring in any way. I think it's actually,
- that's a really great story. Every step of the process, I was going to say,
- oh, and so which areas are the green areas that you noticed? Is it just because it's
- harder to get to? And then what was the result? Did they overhaul the things? So yeah,
- you answered all the questions. Yeah, the green areas are where the fire stations are.
- And so many other fire services will preposition units in high volume call
- zones rather than have them sit at the station. That's like one common response to that issue.
- The fire department has not done that in Los Angeles. And if you paid close attention to
- the Palisades fire earlier this year, one of the key criticisms of how that was managed is they
- did not preposition units in areas that were prone to fire, even though there was a very high risk
- fire warning at the time. My colleagues who are still at the LA Times did some great reporting
- showing how that structural flaw still exists. Yeah, because it does really look like it's
- essentially gridded across the city like that. Yeah. The other thing I think is amazing,
- I'll just call out is that you mentioned this was made in 2012, and it is still running on your
- browser here more than more than 10 years later. So it's always nice to see the longevity of things
- like that. I'll publish static files if you can, guys. That's my static files. There you go.
- All right. Jared, why don't you go? Why don't you go next?
- Okay, certainly. Hi, again. Hi, everybody. My name is Jared from Philadelphia. The project I
- want to show today is one that over at Polygraph, which is the internal studio of The Pudding,
- which I hope many of you are familiar with, a project that we recently published that I thought
- kind of covered a lot of different elements of being a data journalist. For this project,
- we partnered up with the Trans-Journalist Alliance Association to take a look at the question of
- how can we kind of audit the news industry and look at how they are exploring a particular topic,
- in this case, trans issues. The way we went about doing that is we put together a script that is
- using the Media Cloud API, which is an organization that basically compiles news articles by
- keyword search and just allows you to, while not actually pull the body text for obvious reasons,
- allow you to say, I want to see all the articles that include these keywords. I want to pull all
- the articles from the New York Times that mention this topic. From there, we just put together a
- long list of keywords, a long list of things that we felt captured the breadth of trans issues
- across healthcare, across pop culture, across social issues, and then we're able to generate
- this corpus of hundreds of thousands of articles over the course of the last five years. From there,
- we were then able to run the data through a large language model classifier to then take those
- articles and group them by themes that we predetermined. For example, we were able to
- take all those articles, filter out the noise, and then come out with 18,000 articles about
- healthcare and body autonomy. From there, we then were able to then go into each of these
- broader themes and cluster those articles by events. From here, we're not only looking at
- the 18,000 articles, but we were able to find those 150 events that group those articles together.
- And why do all this? Well, the reason we wanted to do this is because we wanted a way
- to hold ourselves in the news industry accountable for the stuff that we're covering. How are we
- talking about these issues? So in this case, when you look for this broader theme, we can see some
- of the major stories that pop out, such as convergent therapy going before the Supreme Court
- or puberty blocker bans. And what we hope to do is create an interactive experience that allows
- the user to really dive in, look at these individual articles, explore how different
- publications based off maybe how their political lean are, and see how they are covering it.
- And then what I really enjoy is because we partnered with this organization, we were able
- to bring in their insights. So we were able to have them come in and say like, well, here is how
- a topic is being covered. And here are some insights, how we think maybe this topic should
- be covered, or how us in the trans community believe that it should be covered. From there,
- we basically present the data in ways that we hopefully communicate the story, such as
- publication, political lean by year, giving the articles themselves, showing these little
- bee swarms for each topic of how, based on your current filtering, how the articles are published,
- which allows you to see jumps across time when a story is in the news. And what we want to hopefully
- do with all this is as this project continues to grow, because we have this running on an ongoing
- basis, is be able to take a look back at any given point and look for those stories, look for
- how, as the political climate changes and things in both the news, but as a way of us in the news
- industry, change how we think about toppings and how to cover things, be able to actually put some
- data behind some of those preconceived notions. The response to this project has been
- overwhelmingly successful, which we always want to hear. And my hope is that as we continue to
- maintain this project over the coming years, that we're able to see the news industry in general do
- a bit more inward looking, but taking, investing the time to do audits of your own reporting,
- your own coverage to see how you are reporting on the communities you read you.
- Wow, that's amazing. Thanks, Jared. So when you say it's updated, do you mean that this is a
- sort of semi-manual process that you guys are returning to, or it's more of an automated
- updating process? Yeah, great question. So the way it works now, we have a monthly script that will
- one, pull the entire corpus of articles and then run it through our model to handle the classification.
- But as anybody who's worked with large language models, who's worked with machine learning,
- you know that when you're working with machine learning, you need to constantly training your
- model. So the way that we have this is we have a handful of configuration files that as new data
- comes in, it gives us the ability to manually audit, to manually make corrections, and then
- continue to train the model going forward. So big picture of the way this works is on a monthly
- basis, it's doing classifications based off the existing data. And then we have a more semi-annual
- approach to kind of look at the entire corpus of data for reclassification purposes.
- Awesome. All right, let's go ahead and move on to Kavya.
- Kavya, I think you're muted actually. My bad. Oh, good. Okay, so this is a
- project that I worked on with Will and Jared actually, and a couple other folks on our team.
- And this was right after that summer with intense wildfire smoke on the east coast.
- So I in New York was looking outside my window and seeing just orange. So what we wanted to
- accomplish with this article is basically kind of recap what happened that summer, what made
- the wildfire smoke that summer so unusual, and what kind of it means if this is our new normal
- with pollution exposure. So this was a collaboration with a lot of different people.
- You know, but the part that I want to zoom in on is the zoom in section that focuses on
- kind of like Ben was saying, localizing the story to you. So this is basically a dashboard that
- lets you search for some metro areas and look up how your pollution exposure might have been
- in that time period compared to the average over the last, you know, 10 or so years.
- And this is a part of the motivation for doing this is because Axios has many local bureaus.
- So we have newsletters that operate in more than 30 US cities. And so we wanted a way to
- take this really large national story and make it relevant in some way to folks. So this dashboard
- and the data that I collected as part of it was for that effort. And also, you know,
- including some tips for how to actually protect yourself from wildfire smoke because that's
- something we need to do now. So part of the design decisions that went into this
- was kind of trying to make a chart that can kind of speak on its own terms.
- So if you just look at it, it tells a complete story. This is the most recent data. This is
- where this is how it compares to the average, and a pretty clear indication of whether what
- you're experiencing is healthy or unhealthy, according to the standard set by the government.
- So one of the effects or the impacts of like taking the time to scrape this data and clean
- it and prepare it is that our local markets were able to run stories based on it. So it wasn't just
- published once. The stories got used a lot after that too, because we took the time to localize it.
- So yeah, that's all. Also, the folks who built this are also on the call. So if you want to
- say anything about it as well, you both are welcome. I can do a quick answer to this question
- that Amanda just asked in the chat. How did you create the smoke animations on scroll?
- So yeah, that was another collaboration. A lot of people worked on this section.
- Essentially, this is a background image, which is the map. So it's a static image. So that map was
- created in a combination of tools, QGIS and Adobe Illustrator. And then we pulled the data for
- how much smoke there is. So NOAA publishes satellite data that you can process to
- get this density of smoke. And then we processed those images to create transparent versions that
- just have the brown portion where it's the intensity of smoke and the rest of the image
- is transparent. And so then those are overlaid on top of that background map image.
- And Jared worked on a
- scrolly telling a little bit of code that would cycle those images. So it steps through all the
- smoke images. We have like one image, I think, for every four hours, I want to say. I don't
- remember if that's exactly right. But it steps through, you know, all the hundreds of images
- for the smoke that we have that represent that time period. Yeah.
- All right. Thank you, everybody. So moving on now into a couple more questions just about how
- things work in newsrooms, how you deal with data. And anybody, feel free to jump in here.
- So when you're handling a messy data set, what is your first step in figuring out
- if there's a story worth telling? I can start. I think that a good first step, or at least
- what should be a good first step, is when you get a messy data set, or really any data set,
- but especially one that's messy and a bit complicated, is before you jump in to try
- to tell the story with it, before you jump in to try to find a narrative, is you make sure you
- figure out why this data set even exists and how it's already being used. It's very tempting to
- dive right into it and inject your interpretation of what it could tell you, especially if it is
- messy and maybe it presents some like story agnostic challenges like pulling from a PDF or doing some
- weird scraping, because a lot of us in this field see that and while it may be tedious, we actually
- kind of get excited about figuring out that problem. But what can often end up happening
- is you spend a lot of time to get the data to where you think it should look like, only to find
- it doesn't actually tell you what you think. So whether it's referring to documentation, whether
- it's seeing how other news outlets or other places are using that data, or hopping on the phone and
- talking with the data manager, those are pretty much what should be your first step anytime you
- encounter a new data set. Yeah, and I would say another technique is to just really focus on the
- fundamentals of the table itself. What is a row? And I mean that in the most philosophical
- and Socratic sense, you know what I mean? Like said, what does each row represent? What is it
- recording, right? What does each column indicate? Like I had an interesting example with some
- students I was working with a couple years ago at DePaul University, where they had been given a data
- set from the city of Chicago that was trees planted by the city tree planting program. We were going
- to look, you know, they had set a target for how many they were going to grow. There's your benchmark
- guys. And we were going to map it to see who got the trees. It's almost like I do this same story
- over and over again. And we did our provisional analysis, and went back to the city to kind of
- tell them what we had found. And we learned something a little embarrassing, which was that
- there was a column with a very cryptic name that we didn't know what it was, and didn't think too
- much about. But it was actually the number of trees that had been planted in that row. So each
- row was not a tree, which is what we were working under the assumption of. Each row was a work order
- to plant one too many trees, right? And so our initial analysis was kind of fundamentally wrong,
- because we didn't understand, you know, everything we needed to about the data. What is a row,
- and what does each column mean? And just slowing down and forcing yourself to walk through that
- is, I think, crucial. Yeah, I think my thoughts on that were pretty much covered. But I think
- there is also a way that focusing on one row of data can also make clear what steps are needed
- to clean it. You know, as you're trying to understand what is actually being shown, maybe
- it's multiple things at once. It's not just one observation, you know, maybe there's multiple
- data sets mixed together, they all need to be teased apart. And that's another thing that can
- come out of just trying to understand the data on a basic level. And you can trust your gut,
- if something is confusing to you, then it's probably going to be confusing to other people.
- Yeah, that's great. Jared, I love your point about like, just pick up the phone and call somebody,
- because, you know, I think so many of us in the data world or who come from a background that
- might be more like engineering focused. That's just like never where your mind would go. At first,
- right? Your mind goes to like, Oh, what code can I write to do this? Or how can I investigate this?
- And I remember there's a hilarious quote, Hadley Wickham, who, you know, works at our studio,
- he went to a journalism conference one time. And he said that after talking to people, he said,
- the thing I learned the most is that like, what you can learn by just picking up the phone
- is incredible. And that is a thing that I would never, literally never do in my life.
- So yes, pick up the phone, don't be afraid of it.
- Kavya, I actually wanted to come back to you for the next question. I was,
- because I know that you've had some experience working in other industries, you mentioned you've
- worked in nonprofits, and done some, you know, sort of dashboarding work in in the business space.
- So I know a lot of people in our audience create visualizations for businesses or internal teams,
- dashboards, that sort of thing. And what principles do from data journalism, do you
- think that they can apply to make their own work more impactful? Of course, anyone else is welcome
- to jump into it. But yeah, yeah, totally. I think in my past job, I've had a lot of experience
- making dashboards that I'm not sure anyone actually used or saw. And I think part of the
- challenge there is that we're going in assuming that we need a dashboard, you know, and that all
- of the data points that we want to visualize matter. But I think there are more fundamental
- questions, you know, like, why do we need to visualize all this in this way, who is going to
- be using it and how, and how is it going to be maintained, which is was my job before this.
- And so I think that those are principles from data journalism that apply anywhere.
- Like anytime you want to do some sort of visual communication or chart, it is really helpful to
- understand who you are trying to help, to influence, to inform. And making something
- that's actually useful starts with figuring out why it exists. That's my take.
- Yeah, that's great. I love that. All right, let me let me ask for the room a little bit about
- about collaboration. So a lot of the pieces we showed here in the in the sort of show and tell
- section, you know, there were many names on those or lots of people that were involved in those
- projects, you know, you've got reporters, designers, editors, developers, all kinds of people, artists
- working on them. So what have any of you learned about working across roles that might help other
- people building data visualizations in collaborative environments?
- I think something that I learned, especially working at Axios is just the importance of
- like that it's okay to let go. I know a lot of newsroom developers, but I imagine a lot of the
- people who work in smaller companies as well. You're probably one of the few technical people
- on your team, you might very well be the only designer, the only developer, the only data
- journalist in your newsroom in your organization. So you might get used to being the jack of all
- trades and feeling very good about knowing, yeah, I could do this whole thing by myself. But when
- you find yourself in a collaborative environment, you often are and probably shouldn't be the best
- designer, the best developer, the best data journalist, all these things at once. So just
- learning to be comfortable and then kind of put your ego aside. Let people who shine at something
- let them shine at it. And not only will that make a better, a smoother and probably better
- process and probably better product in the end, it also will allow you to grow and achieve your
- preferred specialty. Something I might add to that is also from experience working here at Axios
- is that it helps to let the story steer the ship. As in every piece, every person who's
- contributing to the story is supporting the whole. And so the visual has to work with the words and
- has to work with the style and has to work with the social strategy. And all of those pieces are
- ultimately like our mission is to produce the best story possible and to tell a specific
- narrative. And it is humbling and also helpful to have that to fall back on when we're all trying
- to do our own little pieces of a story. Great. All right. Well, I think maybe the
- final question we can talk about before we go on to some Q&A section is I'd love if anybody wants
- to talk about how either technology or tooling for making visualizations has changed over the
- course of your careers and some sort of how has that impacted your work or maybe even what are
- you excited about for the future? Anything on the horizon that you're interested in in that space?
- I'm getting old. That's kind of scary to reflect on. I mean, I would say there's a few things. I
- think it's easy to take the cloud computing revolution for granted, just the fact that
- we're all able to just publish stuff from our MacBook with whatever flavor of platform you prefer.
- That wasn't the case when I started my career. You'd show up and they'd be like, you need an edge
- server in the basement to do anything and that costs X thousand dollars or whatever. That really
- I think opened up more than we appreciate, I think, in terms of opportunity. And then I just have to
- say, I think open source software just continues to be the seedbed and fuel or whatever the right
- metaphor is for all of it. I think sadly we've seen a decline of that in recent years in the
- news industry, which I presented a report on recently with my colleague Scott Klein.
- And I'm sorry to see that, but I'm proud to be here with Observable, who I know is one of the
- great leaders in that. So we just all need to contribute and keep investing in it because it
- does pay off. I think one thing that has definitely changed, I've been doing this for
- about a decade now, and the barrier to entry on getting to just getting started with a project
- is certainly lower. A lot of that comes from the open source stuff that was just mentioned. A lot
- of that comes from just more tools like DataRapper, like Flourish, like Observable, like all these ways
- that you can dive into DataViz or data storytelling or coding in general. But also just the amount
- of information there's. I mean, a lot of us I'm sure have had the problem of scouring the internet
- for some obscure Stack Overflow reference that had to fix some bug. And whether you're using
- something like whether using AI, whether you're using just Google getting better at searching,
- whether there's just more stuff out there, or maybe you're using like a Slack community.
- I think that it's never been a better time to start learning this stuff because you can get
- started so much faster. You don't need to struggle alone. There really is a very large community
- and ecosystem of information out there. Yeah, I think related to that is the amount of tools
- that we can keep on our tool belt is a lot bigger now because they have been so more established
- and there's a lower barrier to entry. And I think that's also exciting in the way that we can get
- more experimental with our data storytelling and make things that we imagine more real quicker.
- Yeah, totally. Great answers. All right. Time to move on to Q&A section. So we've got lots of great
- questions. I think a couple of these questions may have already been answered in the chat. So I'm
- going to jump around a little bit. So let me start with a question from Pedro. So Pedro asks
- about impacting the reader. In visualization, we generally encode information through visual
- variables such as symbols, shapes, sizes, colors, et cetera. This turns data about people into
- visual abstractions. How do you think we can humanize visualization in humanitarian content
- themes such as war, urban violence, discrimination, poverty, et cetera?
- I mean, I can just start. I think this is a huge and constant challenge that we face.
- I remember working on a project with some of their Axios folks on when we hit the million
- COVID deaths milestone. And I feel like that's also when this challenge came to the forefront
- for a lot of publications. How do we visualize, how do we get people to appreciate the size of
- one million deaths? And that might be a good example to look back on as different newsrooms,
- each one took their own approach. But I think it has to come from a place of empathy and trying to
- give people comparisons and ways in so they can relate to what might be colder numbers
- into something that they can understand and relate to. And then, you know, the impact happens.
- Yeah. I was just going to say that I think this is a question that's very important and it's hard
- because at the end of the day, as communicators, sometimes the most clear way to communicate
- something can come across as cold. And I think that can also, I think we think about this in terms
- of like databases, but the same thing can definitely be true in the traditional written
- journal or in video. And like, I think we all know examples of news content, whether it's a video,
- whether it's an article that just rubs you the wrong way or clearly doesn't have empathy or is
- clearly talking about the subject in a way that just misses the mark. And a lot of times it's less
- to do with what symbols are used or what colors are used and just how the general feel of it.
- So getting your content in front of people and sharing it with them. If you have a diverse group
- of voices in your newsroom, have them look at it, have them look at your content and see this
- anything strike you in a way that maybe we've missed. And I think that's probably a better
- approach than just saying, oh, here's our color palette that we think is good for this topic. Well,
- in all circumstances, is that going to be enough? Yeah, and this is a really great question and a
- big challenge. I know it's one that my colleagues in our excellent Reuters graphics department take
- super seriously. You know, I just posted in the chat a link to their work overall, but to two,
- I think, kind of outstanding examples of dealing with this. One by my colleague, Mario Zafra,
- and another by Ali Levine. And, you know, in both cases, you know, in almost all cases,
- our Reuters team in graphics is trying to use illustration and other more creative
- approaches to design, to humanize and to connect. And I think just showing people in Mario's case
- is one good example. You know what I mean? Not letting it be dots, you know, is one classic
- approach that goes back to the Isotype, you know, and some of the foundational tools of data
- visualization. And it's not my forte, personally. And so every time they put something out, I'm
- always kind of impressed and jealous of how they're able to bring their creativity and their artistic
- skills to bear. And I think we should, you know, as we can do more and more with data, and I can
- say more and more about cloud computing and Amazon, you know, lambdas and AI, blah, blah, blah.
- You know, we can't, we need more of that in journalism, but we cannot lose that artistic
- side of graphics, which is just so important. And, you know, I will not be replicated by AI anytime
- soon. Yeah, very true. Well, that's a great segue, maybe, into the next question. I knew it was
- coming. Okay. Generative AI. So, Ernesto asks, besides direct help in faster slash better coding,
- how do you think that generative AI is transforming the way we do storytelling with data?
- And it would be great if you have any examples.
- I think it's going to increase, I mean, to use kind of a loaded term, I think it's going to
- increase the accessibility of the type of work we do. It's going to make, it's going to bring
- more people into the conversation and allow and bring down the barriers or entry to just like
- figuring out how to get something on the internet or turn the data into a chart. And I'm seeing that
- every day here in our Reuters newsroom, where we recently launched a internal chatbot that through
- an MCP server is able to make charts and data wrapper just by having a chat. And we're going
- to pull in access to our financial data terminal next. So, you could have a chat that says,
- hey, can you get me the Apple stock price over the last 12 months and do it as a percentage change
- versus the S&P and Michael to make a line chart. And just by doing those kind of chatting maneuvers,
- you're able to publish a chart. We actually did one that way and put it on the homepage just two
- days ago. It was the first example. That one was by me because it's my job to be a full-time nerd.
- But I just see so much evidence that those types of interfaces or whoever you were going to call
- them are just going to put data and graphics tools in the hands of so many more people.
- And there'll be bad things to come out of that, but I suspect there'll be a lot more good things.
- Yeah, I think like obviously there's so much that can be said about how the news
- can use AI. But thinking for me personally, I tend to approach it just as like another tool,
- and every tool has its limitations. Every tool is good at something and bad at others. And what I
- would hope is that you will see more data storytellers using it for the things that humans are
- bad at and maybe not letting it just replace the things that hopefully everybody in this call like
- the fun parts of the job, the interesting parts of the job, and just trying to find that balance
- for you as you tell stories. Can this improve your work? If so, that's great, but just be aware
- that it is a tool and tools require the user to know what they're doing. Yeah, I recently had a
- conversation with Marco Hernandez from the New York Times, a great graphics artist at the School
- of Visual Arts. And he said, let the AI do the monkey tasks is the way he put it. But you always
- have to remember that you are the protagonist, which I thought was quite a zinger from Marco.
- Yeah, I think one way we're experimenting with AI at Axios is one of our reporters set up a chat bot
- that we fed in kind of some standards around data visualization and data that we have. And
- it's basically to help reporters triage a data set when they encounter it in the wild to see,
- you know, is this suitable for visualization? Is there any like pitfalls that might, you know,
- like missing data or things to be, you know, to be aware of when considering whether to cover it?
- But it's far from being gritty for prime time. But it's an interesting idea. I think
- it's exposing kind of how nuanced our job is, because there aren't always set rules for,
- for, you know, evaluating data sets. But even getting a reporter like 80% of the way might be
- helpful too. So worth both sides of worth pursuing, I think. Totally. All right, I know we're going
- on a little long here. But I do just want to get to this final question that we can close out with,
- because I know this is a question I get a lot of questions about this personally. And so I know a
- lot of people are curious about behind the scenes, like kind of a little bit of the plumbing of of
- how these visual stories and data journalism comes together in the real world. So there's a
- lot of aspects to this question. I think we can all go around maybe and talk about one or two of
- them. So current asks, I'm curious about tooling and collaboration in newsrooms. Does it look like
- Figma designers and then devs implementing those mocks? Or is it more like designing in code?
- How do you share work in progress? Do you have an internal staging environment?
- How do you share multiple prototypes internally for feedback? How does it migrate to production?
- There's not going to be one answer. You know, newsrooms are kind of all over the place.
- Everybody is is solving the same problems in slightly different ways. And so we could probably,
- if we tried with some post-it notes, come up with like, here's the 25 tricks, and then everybody,
- every newsroom is some mix and match of those 25 would be my hunch. In my newsroom experience,
- which is now more than 20 years of doing this, I've yet to encounter a newsroom team that runs
- like a truly formal product team, you know what I mean with that type of division of labor. It's
- more and more common for a team like ours to partner with our product team. And then they
- kind of force us into their their agile process or, you know, whatever you might call it. I think
- there's a lot of merit to that. But I think it's pretty unusual to see that in the newsroom, which
- which tends to run in a little more of a chaotic fashion. In my experience, and just having done
- this about a lot of people, I think you're going to see a lot of website development that's story
- specific being done in a static site framework that is roughly like observable framework,
- to be honest with you, it's going to be some strange Frankenstein combination of node and
- templating tools, maybe involving svelter react, maybe still being on an old templating tag thing.
- And every newsroom has its own odd variation on that that is then used to bake out or, you know,
- build a static distribution. That's just such a sturdy approach to publishing custom
- micro sites or whatever you want to call them, that I think it's pretty much been irresistible,
- you know. But there are many other ways things happen. And I don't want to
- bore you by doing a laundry list. But that's my impression. What do you guys think?
- Yeah, I think that it's always going to depend on the project and the person building it,
- specifically looking at the design by code or do it in Figma. I think every single time,
- especially when it's like a data heavy story, the data very much will inform the shape of it.
- If you do the whole thing in Figma first, whenever I'm developing, I'm thinking, man,
- I really wish we knew more about the data before we made these Figma designs. And then the other
- way around, when I'm designing my code, it's like, wow, this would be a lot easier if we had
- some cleaner prototypes designed out. So I think it just really is going to be on a case by case
- basis. Because especially if you're not doing the same thing every time, and it is more bespoke,
- more bespoke visualizations, more bespoke data story experiences, there's always going to be
- nuances that you probably wouldn't find in traditional static site building.
- Yeah, I think our approach varies based on timeline and who's leading the project. I think
- everyone is familiar with a slightly different suite of tools. So what I might do in a spreadsheet,
- my colleague might do an R, but we end up in the same place. I think in terms of feedback,
- Axios is kind of unique nowadays in that we're fully remote and a pretty small newsroom
- comparatively. So everything we do is on Slack, for better or worse. And a lot of the processes
- we have are kind of cobbled together slash what we personally think is the best approach at the
- moment. But that's also playing to our strengths. It's so unique to us as a team. So I think that's
- yeah, just echoing what folks are saying, it's going to vary.
- Yeah, totally. All right. Well, thank you so much to everybody on our panel here today. I think it
- was a fantastic discussion. Thanks to everybody for watching, for submitting your questions. We are
- sorry if we didn't get to your question. But just a reminder, the recording will be sent out after
- this. So no need to worry about that. And yeah, once again, thanks, everybody. And we hope you
- can join us again in the future for another discussion like this. All right. Bye.
Storytelling with graphics: From raw data to reader impact
By Ben Welsh • • Observable webinar in Zoom