The Future of Data Journalism and Storytelling

Betsy Slade, a seasoned editor, shared her insights on the ever-evolving world of data journalism and storytelling. When asked about her writing process, Betsy revealed that she always writes over her word count, and editors have to cut down the content to fit the requirements. This is just part of the process, as making tough decisions about what to include and what to leave out is essential in creating engaging stories.

Betsy also touched on the importance of making data more accessible to readers. She believes that this has been a significant theme in her work and will continue to be a focus in the future. With advancements in tools and platforms, journalists can now create interactive visualizations that make complex data more digestible for audiences. Betsy mentioned specific tools like Flourish and Data Wrapper, which have made it easier for reporters to get started with data visualization.

The Future of Data Journalism

As we look ahead to the future of data journalism and storytelling, several trends are likely to shape the industry. One major development is the increasing accessibility of data visualization tools. Betsy noted that these platforms are becoming more user-friendly, making it possible for anyone to create charts and visualizations with minimal technical expertise. This democratization of data visualization will enable a wider range of journalists to engage with data and tell stories in new and innovative ways.

Another area of growth is the potential for 3D data visualizations and other immersive formats. Betsy expressed excitement about the possibility of exploring these new forms of engagement, such as data sonification, which uses sound to convey information. She mentioned a visualization expert who is working on this type of project and plans to share more information in future episodes.

New Platforms and Social Media

The rise of new platforms like TikTok will also play a significant role in the future of data journalism. As Twitter continues to evolve, journalists are looking for alternative ways to reach their audiences. Betsy mentioned that she has recently joined TikTok, which allows creators to produce visual explainers with more space than traditional tweets. While there are challenges associated with using these platforms, Betsy believes that they offer new opportunities for engaging with data and telling stories in innovative ways.

The Impact of AI-Generated Tools

Finally, the integration of AI-generated tools into data journalism will likely have a profound impact on the industry. As these technologies mature over the next two years, we can expect to see increased efficiency and accuracy in data analysis. Betsy mentioned that her workplace, MuckRock, has already begun exploring the use of AI for analyzing documents. This technology can help journalists process large amounts of information more quickly and efficiently, freeing up time for more in-depth storytelling.

However, it's essential to acknowledge potential challenges associated with these tools. As AI takes over some tasks, there is a risk that journalists may become too reliant on automation, losing their ability to critically evaluate data and identify biases. Betsy emphasized the importance of educating readers about data and trusting them to handle more complexity than they think they can.

A Call to Action

As we wrap up our conversation with Betsy, she offered some final advice for aspiring data journalists: educate others about data and trust your audience. By being reliable and answering questions, you can build trust with your readers and create engaging stories that showcase the power of data journalism.

Betsy's insights offer a glimpse into the exciting future of data journalism and storytelling. As we continue to navigate the complexities of data analysis and visualization, it's essential to stay adaptable and open to new technologies and platforms. By embracing these changes, we can create a more accessible and engaging world for audiences, and continue to push the boundaries of what is possible with data-driven storytelling.

"WEBVTTKind: captionsLanguage: enyou're listening to data framed a podcast by datacamp in this show you'll hear all the latest trends and insights in data science whether you're just getting started in your data career or you're a data leader looking to scale data-driven decisions in your organization join us for in-depth discussions with data and analytics leaders at the Forefront of the data Revolution Let's Dive Right In hello everyone this is Adele data science educator and evangelist at datacam one thing we covered a couple of months ago during the illiteracy 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 lady zets Betsy ladiesettes is a science health and data journalist focus 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 a public records and investigative project house the mark Rock and the Brown Institute media Innovation her work has appeared in science news 538 MIT tech review the Covey 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 and what the future of data journalism looks like and more now on to 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 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 yeah so I'm a data journalist who is on the science and health beat mostly writing about covid at the moment I have a hybrid job so I write the Cova data dispatch which is a Blog and newsletter about Copa data mostly focused on the US I work part-time at muck 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 538 so I'd love to set this stage for today's chat back discussing the state of data journalism as an industry as a whole the past two years have definitely been a bone 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 expanding it slightly how have you seen the appetite of different audiences you serve change as the field has evolved yeah I think the pandemic really created a gigantic appetite for data journalism I mean I think back to early 2020 when everybody in the world was just starting to learn about the scale of Crisis that covet would become and we started to see these gigantic dashboards I think John Hopkins is one of the first and then a lot of news outlets like the New York Times Washington Post so far I've 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 I'd like all the data points all the time and at that time I volunteered for the Copa tracking project which also was a major source for these kinds of metrics and the covet 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 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 in looking for what audiences actually need on a day-to-day basis like 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 these days that's really great and adjacent to the rising interests and understanding how covid is spreading or covet data have you seen also the rising interests generally in data journalism covering all sorts of topics from Health Data to like election data have you seen the appetite also evolve on that front I think so I mean I am still relatively new to this field I graduated college about 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 expertise and I really want to discuss the ins and outs of having covered covid-19 but before I want to focus on the skill set of the data journalists today starting off maybe with the skill set I'd love to know from your perspective what are the different skills that needed to break into Data journalism today and how does it differ from traditional roles such as data scientists or data analysts yeah I think it can differ a lot based on what you want to do like people often see data journalism as a kind of a 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 whiz in Python and they can do really complex analysis there are other folks who more specialize in visualization like building those dashboards or making really unique visualizations in like D3 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 communicating data to the public like if you're working as a data scientist say in a corporate role you would probably be focused more on communicating within your company because that's what the needs are and what your role is but as a data journalist I'm really thinking about is my work going to be accessible to a really broad audience and and our people with kind of a limited data background or a limited Science Background going to understand what I'm doing I find the 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 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 going to share with your audience that's really great and you mentioned here one thing is the distinction between different roles is that communication happens to the wider public as opposed to within an internal setting what are the nuances associated with delivering data stories or community educating data to a wider public that you know may be lost on folks who work in internal data roles 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 going to be useful in their day-to-day lives or is going to answer like a burning question that they have although I think people can handle complexity and maybe we can talk more about that later 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 establish a nascent in a lot of different ways the blueprint becoming a data scientist and data analysis is relatively established you think about you know learning whether in University or online courses doing cargo then portfolio project projects getting your name out there similarly what does a blueprint look like for data journalism roles today I 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 know a lot of folks who are in similar positions or maybe they start off in like a general recording role and then become more interested in data or maybe they take advantage of like journalism courses or one Association that folks can get involved with is the ire 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 great 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 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 walk us through what are the different types of portfolio projects you can find in a data journalism career path versus other traditional data roles 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 mukrock 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 U.S 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 coveted treatment in the U.S 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 WNYC 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 and sort of providing 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 kova dashboards or like an election tracker things that can kind of be like a bigger project that gets updated over time I love these types of projects and if you know what definitely jumps out at me from listing all 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 a solid data journalism portfolio project yeah definitely I would agree focusing on public data or sort of questions of public interest because I think that's how you're going to get readers and I love how you focus here especially on like your own relevant experiences and I think this segue is well to discussing you know General 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 covet 19. maybe walk us through the keeplands principles that you've learned in delivering stories of The Wider public 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 that readers really can't 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 going to 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 complexities 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 yeah I completely agree maybe like as an advice for aspiring data journalists would you advise them to ER on the side of complexity or accessibility when making a lot of these different decisions 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 act 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 in definitely rely on the editors now another key component here of the learning 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 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 want to give people all the data points at once you want to 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 covidmap it can be appropriate to have like really icon touching 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 or 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 time stamp 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 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 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 journalist questions of like 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 want to know like oh these are researchers from XYZ institution and you know they have expertise in this topic and then you want to 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 time stamp who might be represented by the data and is anything missing are there any kind of major caveats that you need to provide yeah I feel it's really like journalists also 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 answering a kind of a related question 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 connecting a 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 yeah one thing I find really important is to let the data shape the story not the other way around 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 covet or something and say well I have this argument I want to make and now I want to 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 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'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 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 the 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 we're looking at the data made you update the initial hypothesis that you've had and what was that process like 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 covet cases and experts have tied that to colder weather like people are gathering indoors more soon we're going to have the holidays which is going to be in travel and all of that stuff but we're we're not really seeing like huge spikes in numbers yet right now as of the end of October when we're recording this 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 panda endemic and not just say okay there's going to 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 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 and you're reporting yeah I mean in my in my covet newsletter one of the sections that I do every week is a national update which is just like a short couple hundred words that's like here are the covet 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 false urge 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 that's definitely great to hear and they think this marks a great segue to discuss your overall work and experience covering a complex topic such as covid-19 and 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 kovid 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 probably the biggest challenge is just how many unknowns there are like I think the covet pandemic has been really interesting 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 covet 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 ever 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 treatments leading to the development of mRNA vaccines and all of this stuff 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 that 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 going to happen and covet is over when we actually we have no definitive information like that 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 we've 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 hit and miss right I think mainly due to the lacks use of data visualization lacks 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 our fails is that we can think of to ensure that this doesn't necessarily happen yeah this is such a good question and this is definitely something I think about a lot especially as I consider the fact that 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 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 non-profit that does this kind of work in the climate environmental space stacker which is a company I used to work at 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 communities 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 uscdc at one point if they had a plan for coveted misinformation and the CDC saying not really we're going to rely on journalists 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 I think that's 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 data literacy or data skills upselling program for the industry what would the essential principles be that you would teach 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 source that must be questioned rather than oh I see numbers so I'm just going to assume the numbers are right I think that's kind of a key mindset shift that might need to happen 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 website yeah yeah and that's actually not the case at all yeah exactly so 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 when I get feedback like this I definitely try to separate out or identify what is in good faith and what is in bad faith 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 you who has like good faith feedback or if they are just spreading misinformation and if it's the letter then I'm probably not going to engage with it because I have better things to do with my time but I always try to answer questions when they are like honest questions 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 in 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 interviews not like entirely full but like share a sort of transcribed edited versions of interviews that I do with sort 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 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 the questions it have 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 yeah 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 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 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 are 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 now 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 going to get easier and they're 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 going to say the metaphors because I don't I don't I don't know how how much I I I'm excited about the metaverse 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 listen to it that's the first thing yeah I just think that I'd love to check that out yeah 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 I'm also thinking about like newer platforms like I don't know I'm on tick tocks as of a few weeks ago because Twitter seems to be not not in a good place so as like I need to expand my social media footprint a little bit and I'm still getting used to it but I like how Tick Tock 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 not watch the whole video so I think that comes with his 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 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 it's from Dali to like GT3 even like codecs and coding assistance and these are going to be probably relatively mature to use within the next two years or so how do you anticipate these Technologies as well to impact data journalism yeah I don't have a ton of experience with them myself but I know muck 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 macrock has been working on an AI tool that can help journalists do that more quickly and more efficiently so 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 work day or in one work week I think is probably going to change a lot although there are of course AI kind of analysis can come with its own caveats and stuff too definitely that's something to cover for a future episode now Betsy as we wrap up is there any final a call to action you have before we end today's episode 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 okay that is awesome thank you so much Betsy for coming on data frame yeah thanks for having me you've been listening to data framed a podcast by datacamp keep connected with us by subscribing to the show in your favorite podcast player please give us a rating leave a comment and share episodes you love that helps us keep delivering insights into all things data thanks for listening until next timeyou're listening to data framed a podcast by datacamp in this show you'll hear all the latest trends and insights in data science whether you're just getting started in your data career or you're a data leader looking to scale data-driven decisions in your organization join us for in-depth discussions with data and analytics leaders at the Forefront of the data Revolution Let's Dive Right In hello everyone this is Adele data science educator and evangelist at datacam one thing we covered a couple of months ago during the illiteracy 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 lady zets Betsy ladiesettes is a science health and data journalist focus 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 a public records and investigative project house the mark Rock and the Brown Institute media Innovation her work has appeared in science news 538 MIT tech review the Covey 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 and what the future of data journalism looks like and more now on to 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 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 yeah so I'm a data journalist who is on the science and health beat mostly writing about covid at the moment I have a hybrid job so I write the Cova data dispatch which is a Blog and newsletter about Copa data mostly focused on the US I work part-time at muck 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 538 so I'd love to set this stage for today's chat back discussing the state of data journalism as an industry as a whole the past two years have definitely been a bone 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 expanding it slightly how have you seen the appetite of different audiences you serve change as the field has evolved yeah I think the pandemic really created a gigantic appetite for data journalism I mean I think back to early 2020 when everybody in the world was just starting to learn about the scale of Crisis that covet would become and we started to see these gigantic dashboards I think John Hopkins is one of the first and then a lot of news outlets like the New York Times Washington Post so far I've 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 I'd like all the data points all the time and at that time I volunteered for the Copa tracking project which also was a major source for these kinds of metrics and the covet 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 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 in looking for what audiences actually need on a day-to-day basis like 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 these days that's really great and adjacent to the rising interests and understanding how covid is spreading or covet data have you seen also the rising interests generally in data journalism covering all sorts of topics from Health Data to like election data have you seen the appetite also evolve on that front I think so I mean I am still relatively new to this field I graduated college about 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 expertise and I really want to discuss the ins and outs of having covered covid-19 but before I want to focus on the skill set of the data journalists today starting off maybe with the skill set I'd love to know from your perspective what are the different skills that needed to break into Data journalism today and how does it differ from traditional roles such as data scientists or data analysts yeah I think it can differ a lot based on what you want to do like people often see data journalism as a kind of a 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 whiz in Python and they can do really complex analysis there are other folks who more specialize in visualization like building those dashboards or making really unique visualizations in like D3 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 communicating data to the public like if you're working as a data scientist say in a corporate role you would probably be focused more on communicating within your company because that's what the needs are and what your role is but as a data journalist I'm really thinking about is my work going to be accessible to a really broad audience and and our people with kind of a limited data background or a limited Science Background going to understand what I'm doing I find the 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 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 going to share with your audience that's really great and you mentioned here one thing is the distinction between different roles is that communication happens to the wider public as opposed to within an internal setting what are the nuances associated with delivering data stories or community educating data to a wider public that you know may be lost on folks who work in internal data roles 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 going to be useful in their day-to-day lives or is going to answer like a burning question that they have although I think people can handle complexity and maybe we can talk more about that later 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 establish a nascent in a lot of different ways the blueprint becoming a data scientist and data analysis is relatively established you think about you know learning whether in University or online courses doing cargo then portfolio project projects getting your name out there similarly what does a blueprint look like for data journalism roles today I 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 know a lot of folks who are in similar positions or maybe they start off in like a general recording role and then become more interested in data or maybe they take advantage of like journalism courses or one Association that folks can get involved with is the ire 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 great 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 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 walk us through what are the different types of portfolio projects you can find in a data journalism career path versus other traditional data roles 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 mukrock 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 U.S 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 coveted treatment in the U.S 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 WNYC 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 and sort of providing 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 kova dashboards or like an election tracker things that can kind of be like a bigger project that gets updated over time I love these types of projects and if you know what definitely jumps out at me from listing all 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 a solid data journalism portfolio project yeah definitely I would agree focusing on public data or sort of questions of public interest because I think that's how you're going to get readers and I love how you focus here especially on like your own relevant experiences and I think this segue is well to discussing you know General 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 covet 19. maybe walk us through the keeplands principles that you've learned in delivering stories of The Wider public 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 that readers really can't 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 going to 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 complexities 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 yeah I completely agree maybe like as an advice for aspiring data journalists would you advise them to ER on the side of complexity or accessibility when making a lot of these different decisions 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 act 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 in definitely rely on the editors now another key component here of the learning 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 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 want to give people all the data points at once you want to 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 covidmap it can be appropriate to have like really icon touching 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 or 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 time stamp 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 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 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 journalist questions of like 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 want to know like oh these are researchers from XYZ institution and you know they have expertise in this topic and then you want to 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 time stamp who might be represented by the data and is anything missing are there any kind of major caveats that you need to provide yeah I feel it's really like journalists also 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 answering a kind of a related question 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 connecting a 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 yeah one thing I find really important is to let the data shape the story not the other way around 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 covet or something and say well I have this argument I want to make and now I want to 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 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'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 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 the 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 we're looking at the data made you update the initial hypothesis that you've had and what was that process like 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 covet cases and experts have tied that to colder weather like people are gathering indoors more soon we're going to have the holidays which is going to be in travel and all of that stuff but we're we're not really seeing like huge spikes in numbers yet right now as of the end of October when we're recording this 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 panda endemic and not just say okay there's going to 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 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 and you're reporting yeah I mean in my in my covet newsletter one of the sections that I do every week is a national update which is just like a short couple hundred words that's like here are the covet 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 false urge 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 that's definitely great to hear and they think this marks a great segue to discuss your overall work and experience covering a complex topic such as covid-19 and 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 kovid 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 probably the biggest challenge is just how many unknowns there are like I think the covet pandemic has been really interesting 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 covet 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 ever 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 treatments leading to the development of mRNA vaccines and all of this stuff 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 that 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 going to happen and covet is over when we actually we have no definitive information like that 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 we've 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 hit and miss right I think mainly due to the lacks use of data visualization lacks 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 our fails is that we can think of to ensure that this doesn't necessarily happen yeah this is such a good question and this is definitely something I think about a lot especially as I consider the fact that 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 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 non-profit that does this kind of work in the climate environmental space stacker which is a company I used to work at 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 communities 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 uscdc at one point if they had a plan for coveted misinformation and the CDC saying not really we're going to rely on journalists 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 I think that's 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 data literacy or data skills upselling program for the industry what would the essential principles be that you would teach 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 source that must be questioned rather than oh I see numbers so I'm just going to assume the numbers are right I think that's kind of a key mindset shift that might need to happen 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 website yeah yeah and that's actually not the case at all yeah exactly so 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 when I get feedback like this I definitely try to separate out or identify what is in good faith and what is in bad faith 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 you who has like good faith feedback or if they are just spreading misinformation and if it's the letter then I'm probably not going to engage with it because I have better things to do with my time but I always try to answer questions when they are like honest questions 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 in 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 interviews not like entirely full but like share a sort of transcribed edited versions of interviews that I do with sort 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 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 the questions it have 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 yeah 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 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 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 are 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 now 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 going to get easier and they're 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 going to say the metaphors because I don't I don't I don't know how how much I I I'm excited about the metaverse 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 listen to it that's the first thing yeah I just think that I'd love to check that out yeah 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 I'm also thinking about like newer platforms like I don't know I'm on tick tocks as of a few weeks ago because Twitter seems to be not not in a good place so as like I need to expand my social media footprint a little bit and I'm still getting used to it but I like how Tick Tock 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 not watch the whole video so I think that comes with his 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 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 it's from Dali to like GT3 even like codecs and coding assistance and these are going to be probably relatively mature to use within the next two years or so how do you anticipate these Technologies as well to impact data journalism yeah I don't have a ton of experience with them myself but I know muck 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 macrock has been working on an AI tool that can help journalists do that more quickly and more efficiently so 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 work day or in one work week I think is probably going to change a lot although there are of course AI kind of analysis can come with its own caveats and stuff too definitely that's something to cover for a future episode now Betsy as we wrap up is there any final a call to action you have before we end today's episode 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 okay that is awesome thank you so much Betsy for coming on data frame yeah thanks for having me you've been listening to data framed a podcast by datacamp keep connected with us by subscribing to the show in your favorite podcast player please give us a rating leave a comment and share episodes you love that helps us keep delivering insights into all things data thanks for listening until next time\n"