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Data Journalism in the Age of COVID-19

Betsy Ladyzhets discusses the importance of data shaping the narrative, and characteristics of traditional journalism needed for data journalists
Nov 2022

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Adel Nehme, the host of DataFramed, the DataCamp podcast, recently interviewed Betsy Ladyzhets, Founder of the COVID-19 Data Dispatch , and independent science, health, and data journalist focused on COVID-19.

Intoducing Betsy Ladyzhets

Adel Nehme: Hello everyone. This is Adele Data Science Educator and Evangelist at Data Camp. One thing we covered a couple of months ago during data literacy month was how data journalists curate data stories for the wider public. You know, over the past two years, data journalism has definitely hit the mainstream, especially given one of the biggest stories of the decade, Covid 19.

And there's no better person to talk with about the state of data journalism today than Betsy Ladyzhets. Betsy Ladyzhets is a science, health, and data journalist focused on Covid 19. She runs the Covid-19 Data Dispatch, a publication that provides news, resources, and original reporting on pandemic data. She's also a journalism fellow at Documenting Covid-19, public records and investigative project housed at Mark Rock and the Brown Institute Media Innovation.

Her work has appeared in Science News 5 38 MIT Tech Review, the Covid Tracking Project and other outlets. Throughout the episode, we chat about the state of data journalism today. The skills needed to break into data journalism, how the media and public institutions succeeded and failed in reporting on Covid 19, what the future of data journalism looks like, and more. Now onto today's episode.

Betsy, it's great to have you on the show. I'm excited to speak to you about the state of data journalism today, best practices for delivering data stories, your work covering Covid 19 and more. But before, can you give us a bit of a background about yourself and your work?

Betsy: Yeah, so I'm a data journalist who is on science and health. Mostly writing about Covid at the moment. I have a hybrid job, so I write the Covid Data Dispatch, which is a blog and newsletter about covid data, mostly focused on the US. I work part-time at M Rock, which is a public records data and investigative non-profit.

So I'm mostly, again, doing covid and public health-related stories. And then I freelance for outlets like Science News 5 38.

Adel Nehme: So I'd love to set. Age for today's chat by actually discussing the state of data journalism as an industry as a whole. The past two years have definitely been a boon for data journalism with the coverage of covid 19 election seasons and more. Given this context, how have you seen the landscape of data journalism evolve over the past few years, and maybe expand it slightly. How have you seen the appetite of the different audiences you serve? Change as the field has.

Betsy: Yeah, I think the pandemic really created a gigantic appetite for data journalism. I. And I think back to early 2020 when everybody in the world was just starting to learn about the scale of crisis that Covid would become, and we started to see these gigantic dashboards. I think John Hopkins was one of the first, and then a lot of news outlets like the New York Times.

Washington Post, and so forth, kind of created their own dashboards to provide case data and testing data and hospitalizations and basically any metrics that they could get their hands on. And I think people really had an appetite for that. We saw folks who wanted all the data points all the time, and at that time I volunteered for the co-tracking project, which also was a major.

For these kinds of metrics and the covid tracking project. Every day after our team of volunteers updated the data, there would be a Twitter post sort of sharing the day's numbers and immediately it would get like hundreds of likes and retweets and so forth and, and people commenting on what the day's numbers were.

So I think that kind of real-time interest was really unique. But as the pandemic has gone on, I, I've seen my colleagues who work with data trying to be more constructive. Looking for what audiences actually need on a day-to-day basis. Rather than just throwing a gigantic dashboard at you with every possible metric, we're thinking more about like, what are the questions that readers have and how can we answer those questions with data.

How can we provide audiences with local data or local information about their communities, which I think is what people really find useful and find actionable? 

Adel Nehme: That's great and adjacent to the rising interest in understanding how Covid is spreading or. Data. Have you seen also the rising interest generally in data journalism covering all sorts of topics from health data to like collection data? Have you seen the appetite also evolve on that?

Betsy: I think so. I mean, I am still relatively new to this field. I graduated college. Three and a half years ago. So most of my career kind of has been the pandemic, but I do think when I look at like journalism, job postings, and stuff, it seems like all so many newsrooms want to have data people on staff now.

Although that's still more of a niche within journalism. But I think there's definitely an interest, and I know like my colleagues who are in the kind of science and health beat, a lot of those folks are interested in gaining more data e.

Adel Nehme: And I really wanna discuss the ins and outs of having covered Covid 19. But before I wanna focus on the skill of data journalists today. Starting off maybe with the skill set, I'd love. From your perspective, what are the different skills that are needed to break into data journalism today, and how does it differ from traditional roles such as data scientists or data?

Betsy: eah, I think it can differ a lot based on what you wanna do. Like people often see data journalism as. A kind of, niche or assume that all data journalists have the same skill set, but I think there are really like sub-niches within that, right? So there are people who really focus on coding. Maybe they're like a wiz and python and they can do really complex analysis.

There are other folks who specialize in visualization, like building those dashboards or making really unique visualizations in like D three or JavaScript. Um, and then there are folks like I would say I fall into this third category of focusing more on explaining data, writing about data, or maybe using data to kind of power investigative reporting.

I think those are other kinds of categories. So depending on which area, you know, one is interested in, you can sort of tailor. The kinds of projects that you take on or the kind of skills you focus on to that. And as for how data journalism differs from other data roles, I think in my view, the focus is really on communicating data to the public.

Like if you are working as a data scientist, said in a corporate role, you would probably be focused more on communicating within your company because that's what the needs are. But as a data journalist, I'm really thinking about is my work going to be accessible to a really broad audience and, and are people with kind of a limited data background or a limited science background going to understand what I'm doing.

I find that data journalists think about data as like a source. Right. We interview sources. Maybe we, we treat documents as sources. We treat like scientific papers, as sources, and the data. A data set can be a source that needs to be interviewed or needs to be. You need to ask it questions, and then you need to come up with some kind of insight that you're gonna share with your audience.

Adel Nehme: That's really great. And you mentioned here one thing is the distinction between different roles is that communication happens to the wider republic as opposed to within an internal setting. What are the nuances associated with delivering data stories or communicating data to a wider republic that you know may be lost on folks who work in internal data roles?

Betsy: I think really the consideration of making your work accessible is so key to me. Both when I'm dealing with data as a more complicated source. And then also dealing with topics like Covid that are themselves more complicated. Like you really want to think about what are the really big takeaways that you want your readers to get from something?

And in that case, maybe it's okay if you're not like delivering all of the super complex or niche or most interesting pieces of information, but really giving people a takeaway that's gonna be useful in their day to day. Or is going to answer like a burning question that they have. Although I think people can't handle complexity, and maybe we can talk more about that later.

Adel Nehme: Definitely, and given this conversation on skill set, is just how niche certain data journalism roles can be. And I think for listeners who may be interested in breaking into such a career path, the path is definitely not charted as you think about different data roles such as data scientists, data analysts, while still not establishing a nascent in a lot of different ways.

Uh, the blueprint to becoming a data scientist and data analysis is relatively established. You think about, you know, learning whether in university or online courses doing caggle, then portfolio, project projects, getting your name out there. Similarly, what does a blueprint look like for data journalism roles today?

Betsy: think it can be similar in terms of like taking courses, although I will say, At least in my experience, many of the data journalists I know did not necessarily go to school for it. That's certainly the case for me. I double majored in English and biology in college. I took one course that involved some R, but that was the extent of it.

I was definitely not like a computer science person and I knew a lot of folks who are in similar positions or maybe they start off in like a general reporting role and then become more interested in data. Maybe they take advantage of like journalism courses or one association that folks can get involved with is the i e Investigative reporters and editors.

They are like investigative and data focused, so they offer a lot of training courses, boot camps, which I have done a couple of times. Those are really. There's also societies like the Data Visualization Society, you know, other other kind of communities that you can find to help identify courses or even just get feedback from other kind of data journalists on projects around work.

Adel Nehme: And you mentioned here projects. I think portfolio projects is a huge aspect of breaking into data science and just in any data role really what. Through what are the different types of pro portfolio projects you can find in a data journalism career path versus other traditional data roles?

Betsy: Yeah, so I can talk about a few of my own projects. Although I mentioned I focus on like explanatory and investigative stuff. I have done some of those other kinds of projects that are more analysis focused or more visualization focused. So I mentioned I work for M Rock, which is a kind of public records investigative outlet.

And so a lot of the projects I've done there are using data as a tool to interrogate a broader question. And then I've also done projects for like science News is a science specific news outlet in the US where I've done some like visualization based stories for them. One recent example of a piece I think is coming out soon is I produced a map of clinics offering long covid treatment in the us.

So that story was really like trying to. Compile a list from a couple of different sources and then just making a giant interactive map for people. So that's one type of story. You can also do things that are more like building a novel analysis. I did a story over the summer for Gothamist, W N Y C, which is a local outlet in New York, basically looking at how PCR testing access had declined in the city in early 2022.

So that was kind of me taking a public website and analyzing it. providing a novel, novel data set that had not existed before. And then of course there are also like giant dashboards or trackers that folks can work on. So like the Covid dashboards or like an election tracker, things that can kind of be like a bigger project that gets updated over time.

Adel Nehme: I love these types of projects and you know what definitely jumps out at me from listing all of these projects is definitely, there's needs to be some form of novel analysis there. It needs to be a data. Set that is publicly available that people need to dig into. The public has burning questions around.

It needs to be some form data visualization focused. Would you agree? These are three main principles for our solid data journalism portfolio project.

Betsy: Yeah, definitely. I would agree. Focusing on public data or sort of questions of public interest, because I think that's how you're gonna get readers.

Adel Nehme: And I love how you focus here, especially on like your own relevant experiences. And I think this segues well to discussing, you know, journal best practices for delivering data stories. As a data journalist, a key component of being a successful data journalist is delivering data stories, and that requires balancing the sophistication of a data.

Story, but also the accessibility, which is something that you mentioned earlier when discussing best practices for delivering stories for a wider public. So as someone who's working on the front line of really complex topics such as Covid 19, maybe walk us through the key links principles that you've learned in delivering stories to the wider public.

Betsy: Yeah, so definitely there is that balance between wanting to have a simple accessible takeaway, but then also not dumbing it down because I find in writing about covid. Readers really can handle complex topics. Like if I want to go in depth on. Say like how hospitalization data works in the United States.

I can give an in depth explanation and people are going to read it and engage with it, but also some folks are going to stop at the headline or are gonna stop at the first few lines of the story. So it's thinking about how you structure your work using those classic journalistic principles of like an engaging lead, a clear nut graph like that all applies for a data story as well.

And then for those readers who are interested in the complex, Or are interested in knowing how you got to the conclusions that you did, sharing your methodology, sharing your source information, like acknowledging the caveats of the data or the caveats of the analysis. All of that is super important.

Adel Nehme: Yeah, I completely agree. And maybe like as an advice for aspiring data journalists, would you advise them to err on the side of complexity or accessibility when making a lot of these different decisions?

Betsy: I don't think I can say one thing. I think it depends on the project. I think that you can always, like in a data story as in really any kind of writing, like you have to get another person to read it and give you their feedback. Like this is why editors are great. They can tell you if you are being too confusing or if you're failing to provide the general takeaway.

So that can be kind of a helpful way to figure out like which direction to go.

Adel Nehme: Definitely rely on the editors. Now, another quick component here of delivering data story is also the data visualization side of things, right? Walk us through some of the visual best practices that you've learned across the. Years to delivering like high quality data stories.

Betsy: Yeah, so this is definitely one area where I consider myself less of an expert compared to many other data journalists, but I do a lot with those simpler tools like Flourish and Data Wrapper. And stuff I always think about is trying to keep it as simple as it can be. You don't want to, as I said, you don't wanna give people all the data points at once.

You wanna make sure they know what they're looking at. Thinking about colors, what's the sort of the mood or the emotion that you're trying to create, like. Sometimes with Covid map, it can be appropriate to have like really eye catching reds because you want somebody to think, Oh, it's bad in this area.

Like, Oh, this is not a good situation in the red zone. But other times, like if I'm making a map of vaccine data, for example, I would probably use like cooler colors or something that evokes like, The places that are more, are more vaccinated, maybe that's a positive for those communities. And then also thinking about like clear titles, clear labels, clear annotations, making sure that your source is in there and is linked.

If it's like an online visualization so that people can go look up the, the source data if they want to. Making sure that it has like a timestamp if you're working with a data set that is updated frequently, so readers know maybe what they're looking at might no longer be the most recent data. That kind of thing.

That's all important.

Adel Nehme: Yeah. That's really great. And you mentioned here a lot of the times, like making sure that the sources, the methodology is always mentioned, ensuring that readers have ability to go deep dive into that particular aspect of the data story. What do you think is a great checklist maybe for ensuring that the audience.

It has all the necessary knowledge, but also a great checklist from a data quality perspective to ensure that the methodology is sound as a data journal.

Betsy: Yeah, so I think you wanna kind of answer the basic questions of. What is the source data? And then to any extent you can talk about it, where is the source data coming from? Is it from like a government agency? Is it from a scientific paper? Is it from like a survey? And then what gives the data authority?

Like if it's coming from government or something, then that's a given. But if it's coming from a scientific paper, then maybe you wanna know like, Oh, these are researchers from XYZ institution and you know, they have expertise in this. And then you wanna talk about like what did you do with the data? Did you do an analysis or are you just presenting what exists in the data set?

Did you like select a specific column or a specific field for some reason to present? And can you maybe give that reason as to why that field seems most important or why it might be most relevant to your story? When is the data from, like what's the timestamp? Who might be represented by the data, and is anything missing?

Are there any kind of major caveats that you need to. Yeah, I feel, and it's really like journalists also talk, often talk about, like answering all those who, what, when, where, why questions. And I think you can think about a similar set of questions with your methodology and like if the reader wants to do their own analysis, like what's the information that they would need to either replicate what you did or do something similar maybe for their community or in a more, in a, answering a, a kind of a related question.

Adel Nehme: Okay, that's awesome. You really put a lot of emphasis on making sure that it connects a lot to like journalistic best practices as well and connecting it. Traditional journalism, which I find great for audiences now. I think this a lot connects as well to your ability to shape the story of a particular data story.

Right. So as a data journalist, what are the additional nuances that you need to take into considerations when shaping the story and the narrative that the data is telling you?

Betsy: Yeah. One thing I find really important is to let the data shape the story, not the other way. Like I've run into this before where you know, you come up with an idea or maybe you, you come up with like an argument and then you say, Well, let's go out and see if we can find data that supports this argument, as opposed to finding the data and seeing what argument comes out of it.

Or like seeing where your evidence leads. You. People can have the same problem in reporting, like talking to sources where I might go into a story about Covid or something and say, Well, I have this argument I wanna make. And now I wanna find experts who are going to give me evidence that will support my argument.

It can be hard to not fall into that. So you always have to give yourself room for finding something you don't expect and adjusting your story accordingly. Another thing I find really important is explaining and leading into uncertainty. I think this is particularly true for Covid, but you find this in many other data sets as well, where if you're looking at say, like results of a, an election poll, you don't want to just write the story as though.

These data are really definitive and like definitely reflect the entire country, right? Like you, you're probably dealing with a sample and the sample might not be as representative as you want it to be, so you have to explain that, or you have to talk about what's not being included or what's not being represented in the data.

Adel Nehme: Okay. That's really great, especially on the last point. I think 2016 proved a lot that polling can be misleading as something to like look at. Focusing on that particular aspect that you mentioned here of making sure the data tells. Story or shapes the narrative and not vice versa. Can you walk us through maybe an example of a story that you were working on where looking at the data made you update the initial hypothesis that you've had, and what was that process?

Betsy:I think one example that comes to mind is just covering the pandemic right now in the United States. We've been in this moment for the last kind of two months or so, I would say, where everybody is anticipating a fall surge. Um, just because both in winter of 2020 and winter of 2021, we had a big surge in covid cases and experts have tied that to colder weather, like people are gathering indoors.

More soon we're gonna have the holidays, which is gonna be in travel and all of that stuff, but we are, we are not really seeing like huge spikes in numbers yet. Right now, as of the end of October when we're recording. And even in sources like wastewater data, which are a bit more reliable than cases right now, we're not seeing a massive jump yet nationwide.

So for me, that kind of requires adjusting my assumptions to say like, I think we're still probably going to expect outbreaks around the holidays, but, I have to adjust how I write about this current moment in the pandemic and not just say, Okay, there's gonna be a surge. Like, we definitely know that because we, we never know that we can make hypotheses and we can prepare for that to happen, but that doesn't mean it's, it's definitive until we, we see the data in the next few weeks.

Adel Nehme: Okay. That's really awesome perspective, especially how it ties into like preemptively trying to, making sure that the narrative has enough caveats to a certain extent that you bake in, in your own reporting.

Betsy: I mean, in my, in my Covid newsletter, one of the sections that I do ev every week is a national update, which is just like a short couple hundred words that's like, here are the covid patterns right now. And literally anybody who reads my newsletter could probably tell you that for the last month and a half it's been like fall surge, maybe we're not sure yet.

Here are some signs why. And also why not? You know? And that's just in the situation. And I'll continue to caveat it as best I can until we have a clearer pattern.

Adel Nehme: definitely great to hear, and I think this marks a great segue to discuss your overall work and experience covering a complex topic such as Covid 19. In a lot of ways, there's a lot of weight on one's shoulder when discussing, visualizing and writing about like complex health stories such as Covid.

Maybe walk us through, first the challenges of covering Covid 19. What have you had? What would you consider are the main learnings from having covered it?

Betsy: Probably the biggest challenge is just how many unknowns there are. Like I think the Covid Pandemic has been really interest. From a data perspective, Also from a science and health perspective, wherein we have more information about the coronavirus than we have had about probably any other disease. I'm not sure that I can say that really definitively, but I know that, for example, I just did a piece for my newsletter about comparing Covid tracking to the flu and rsv, which are both kind of having large outbreaks in the US right now.

And I was reflecting on how. We have never tried to count every flu case. We have never tried to count every case of these other common viruses that we're used to dealing with, but Covid was such a huge crisis that there was an impetus to try and track it really precisely and track it through novel methods and try out all these new things for treat.

Leading to the development of mRNA vaccines and all of this stuff. You, you would think we would be able to answer like any question, but that's actually not true. Like all case numbers are underestimates, all official sources have gaps. We don't have like basic demographic data in the United States for a lot of things.

I, I could go on about this all day, but the, the basic point is, We still have a lot of unknowns and it can be hard to explain what those are. When people think, Oh, surely we've answered all the questions and we know exactly what's gonna happen, and covid is over when we actually, we have no definitive information like that.

Adel Nehme: So let's start maybe talking about, I think how in a lot of ways the inconsistencies and the gaps and the challenges that you've talked about here has trickled down into also inconsistent reporting and coverage. When it comes to Covid 19 data. One thing that. Have seen during the COVID 19 surge, especially in the first year and a half of the pandemic, is an explosion of data visualization, showcasing different angles and flavors of how COVID 19 is spreading.

Right? Could have been on a local level where local municipalities or local government is like reporting on how COVID 19 is spreading in their local area. Or it could be national or international news outlets covering the spread of like, like the virus globally. However, in a lot of ways this has been hidden.

Right. I think mainly due to the lax use of data visualization, lax use of narrative or employment of narrative when it comes to shaping the story, how do you think we can avoid this in the future? And what are fail says that we can think of to ensure that this doesn't necessarily happen?

Betsy: Yeah, this is such a good question and this is definitely something I think about a lot, especially as I consider the fact. I work in two niches within journalism that I really wish weren't niches like I wish that every general assignment reporter at a local outlet was able to make charts and was able to read scientific papers and was able to like closely follow every update from the CDC or from their local public health agency.

And I think local journalism in the us from what I've seen and from like talking to friends who are in those roles is just at a huge capacity problem where there are not enough people to deliver the information that needs to be delivered. And so I'm really thinking about like, how can we improve education on data literacy, on science and health literacy and kind of.

Help your average reporter like do the things that I do without it being a super specialized skill set. I would love for my role to, to not be as unusual or whatever as it is, and I think it would also be great to have more resources. For those local outlets, whether that's like, Oh, here's an organization that made chart that has made charts for every state, and you can just use the one for your state if you want.

There are some groups that start to do this. Climate Central is one example of a a non-profit that does this kind of work in the climate environmental space, Stacker, which is a company I used to work. Did some of this stuff, creating like a local news wire with data driven stories. And I think this goes to not just local news, but also local public health agencies and other kinds of local agencies that are tackling these crises.

Like they also need to have infrastructure to communicate to their audiences or communicate to their communi. And also address misinformation, which we know has been such a huge problem. During the pandemic, a friend of mine gave her a presentation at a conference recently talking about how misinformation has been so rampant.

And she mentioned asking the US CDC at one point if they had a plan for Covid misinformation and the CDC saying, Not really. We're gonna rely on journalists. And it's like, Well, , you maybe shouldn't, like this is a huge problem and you should have your own kind of infrastructure. So that's another thing to think about.

Adel Nehme: I really great. I love, I love the holistic answer. Maybe focusing on the skills component of it. What do you think, if you were to design like a basic day literacy or data skills upskilling program for the industry, what would be central principles be that you would teach?

Betsy: I think I would probably have to do more research myself to make sure that I'm like creating something comprehensive, but going off of what I would, I know now, I think that being able to critically interpret statistics, Whether that's from like a survey or a scientific paper or from a health agency, that that is super critical.

And then thinking about like how to make charts, how to interpret charts, how to explain where data are coming from, like what is a methodology, what goes into a methodology. And then maybe after that, you know, one could get into the basics of doing your own analysis. But I think those sorts of just getting used to treating data as a.

That must be questioned rather than, Oh, I see numbers, so I'm just gonna assume the numbers are right. I think that's kind of a, a key mindset shift that might need to happen.

Adel Nehme: Yeah, it's kind of a data determinism that people fall into whenever they see a chart, they're like, Okay, this is a higher level of truth. Just because it's like visualized on a chart on like some

Betsy: Yeah. Yeah. And that's actually not the case at all. Yeah.

Adel Nehme: exactly. So, uh, I'm sure another challenge of Covid 19, and I think even though it shouldn't be necessarily, is objection handling criticism, right?

This is a highly controversial topic. It's highly politicized. As a data journalist, how have you approached this, especially when there's a lot of feedback that must be bad, faith feedback and criticism.

Betsy: When I get feedback like this, I definitely try to separate out or identify what is in good faith and what is in bad. For example, if I have like a story that's getting popular on Twitter and I'm getting a lot of replies, I can usually tell pretty quickly by just checking somebody's profile, whether they are.

Like a concerned reader who has a question or even like, like somebody with some data expertise or somebody with some science expertise who has like good faith feedback or if they are just spreading misinformation. And if it's the latter, then I'm probably not gonna gauge with it because I have better things to do with my time

But I always try to answer questions when they are like honest question. And I try to explain complexity, especially if, and this happens all the time in journalism, but it can be especially challenging when you have like a data story or sort of a story and a complicated niche when you have pieces of complexity that get cut out in the editing process.

And then somebody asks a question and you're like, Oh, I wrote that, but that paragraph was cut so, So sometimes you can kind of, you can kind of use a bit of your reporting notes, so your material that didn't make it into the story to answer a question, and this is why. Also, one thing I like to do with my newsletter is to share full interview.

Not like entirely full, but like share sort of transcribed, edited versions of interviews that I do with sources and tell people like, Here's the finished story and here you can read this 20, 25 minute long conversation that I had with a scientist. And you can see all of these complexities that didn't make it into the piece for kind of a more general audience.

And I think that's like a nice thing to do to share a little bit of the reporting process for people.

Adel Nehme: That's really great. I love this insight and I love especially, and it must be very frustrating to have a piece cut out. Indeed. That makes it back into

Betsy: I mean, it hap it's, it happens. I am definitely one of those people, like any editor I've worked with can tell you. I always write over my word count and I have to cut stuff back, whether that's me doing it or an editor doing it, it's just part of the process.

Adel Nehme: Yeah, indeed. As we wrap up this conversation, Betsy, which I really enjoyed, I'd love to end on a more future looking note. I'd love if you can outline maybe in your own words what the future of data journalism and storytelling looks like.

Betsy: Definitely like making stuff more accessible I think has been a big theme of our conversation, and that's something I. Anticipate seeing more of going forward. I know right now we have tools for visualization like flourish and data wrapper or two I use pretty frequently that are so much easier to get into than if you were somebody starting out in data journalism like 10 or 20 years ago.

I know like some of the older reporters in ire, they came from an era of what they call computer assisted reporting, which just feels so much more technical than what we were able to do. So really anybody who's interested in getting into data journalism can make an account and start making charts, and I think those platforms are gonna get easier and there are going to be more platforms like that.

I'm also kind of interested to see what happens with newer formats. Like are we going to see 3D data visualizations that are incorporated into, I'm not gonna say the metaphor, because I don't, I don't, I don't know how much I, I, I'm excited about the metaphor, but you know, platforms like that. Or even exploring other kinds of ways to engage with data.

Like I have, there's one visualization expert I follow who is big into data sonification, which I think is so cool, like making a visualization, but it's through sound, so you, you listen to it. Yeah. I just think that's,

Adel Nehme: love to check that.

Betsy: Yeah, I'll, I'll, I'll, I'll, I'll send a link. Maybe you can put it in the show notes or something, but that, I find that stuff so cool.

But I'm also thinking about like newer platforms. Like, I don't know, I'm on TikTok as of a few weeks ago because Twitter seems to be not, not in a good place. So I like, I need to expand my social media footprint a little bit and I'm still getting used to it, but I like. TikTok allows you to do like visual explainers.

You can put a chart behind you and then like point stuff out, explain the trends, and you get a lot more space than you do in like a tweet. Obviously people might, might not watch the whole video, so I think that comes with its own challenges, but I am interested to see how more journalists or more database people getting onto those platforms changes how we think about data journalism.

Adel Nehme: Yeah, that's really great. I'm very excited to see what's in store for the field. One additional thing that I wanted to ask you about is with the rise of AI generating tools right from Dali to like GPT three, even like. Coded and coding assistance, and these are gonna be probably relatively mature to use within the next two years or so.

How do you anticipate these technologies as well to impact data Journal?

Betsy: Yeah, I don't have a ton of experience with them myself, but I know M Rock where I work has done some work with AI for. Analyzing documents. If investigative reporters know, sometimes you get a trove of documents back from a public information request, and it can be like a thousand pages that you have to sort through.

And so M Rock has been working on an AI tool that can help journalists do that more quickly and more efficiently. So I, I, I think there's obviously folks also work on like machine learning for data analysis and yeah, this is not something that I have a ton of experience with myself, but definitely I think that will similarly help.

If not improving access, then improving the efficiency of analysis. Like how much are you able to do in one workday or in one work week I think is probably gonna change a lot. Although there, of course, AI kind of analysis can come with its own caveats and stuff.

Adel Nehme: Definitely that's something to cover for a future episode. Now, Betsy, as we wrap up, is there any final call to action you have before we end today's?

Betsy: I think just educate other people about data or about your sort of chosen niche. People can handle more complexity than you think they can. You just have to trust your readers and be reliable and answer questions, and that can go a long way.

Adel Nehme: Okay. That is awesome. Thank you so much, Betsy, for coming on data.

Betsy: Yeah. Thanks for having me.



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