[00:00] [MUSIC PLAYING]
[00:21] Hello, and welcome to the Data Journalism Podcast.
[00:25] My name is Simon Rogers.
[00:26] I am a Data Journalist, speaker, and teacher, and data
[00:29] editor at Google.
[00:31] And my name is Alberto Cairo.
[00:32] I am a professor of Visualization
[00:34] at the University of Miami, an Infographics designer,
[00:37] and journalist, and also a book author.
[00:40] We love using data to tell stories.
[00:42] And the music you can hear is the sound of data,
[00:44] made with two-tone, an app that turns numbers into tunes.
[00:48] There's also the sound of football, American football,
[00:50] that is, with a changing football school since 1920,
[00:54] care of one of our guests today.
[00:57] And this is the Data Journalism Podcast
[01:00] that dissects the latest trends in data journalism
[01:02] around the world.
[01:03] In each episode, we will explore the latest in data journalism,
[01:07] and we will chat with some of the world's top data journalists.
[01:11] You'll get to find out how they do what they do.
[01:14] So subscribe at datajournalismpodcast.com
[01:17] to see how data is changing the world of journalism forever.
[01:21] Hi, Simon.
[01:22] Hey, Alberto.
[01:24] How are you doing?
[01:25] So good.
[01:26] I'm really thrilled to have the chat that we had this week.
[01:29] It was a lot of fun.
[01:30] It was a very interesting one, right?
[01:32] Can you tell us a little bit about our guests?
[01:34] Yeah, so we have two doyen of local data journalism.
[01:39] We've got Ben Welsh from the LA Times, who
[01:43] has been just one of the first people out there
[01:47] to really see the power of using data to tell stories.
[01:51] The Los Angeles Times have one of the first great data
[01:54] journalism sections, and he has forgotten more than we will ever
[01:58] learn about how data can just improve local reporting.
[02:03] And we also are lucky enough to have Mary Jo Webster, who
[02:06] is a great local reporter who's just taught herself
[02:10] how to work in this area and really created
[02:13] some interesting work.
[02:15] It was a very interesting conversation with both of them,
[02:18] and we got into sort of how they got started,
[02:22] how they discovered the tools that they use nowadays,
[02:25] beginning with Excel.
[02:27] They tell a couple of funny stories
[02:29] about how they discovered Excel and then later more
[02:32] sophisticated data analysis tools.
[02:34] I think that it was a very enlightening conversation,
[02:37] and listeners will enjoy it a lot.
[02:39] We should do an episode one day about Excel
[02:42] and the power of this tool in getting people
[02:44] into data journalism.
[02:45] Love it or hate it.
[02:46] It's just become this kind of like entry-level drug
[02:49] for people who are getting into data and reporting.
[02:54] I mean, it's super useful.
[02:55] You can do amazing things in Excel, right?
[02:57] I mean, both Ben and Mary Jo, they
[02:59] mentioned AR, the programming language, and Python,
[03:03] and many other tools.
[03:04] But both of them say, you know, if you
[03:05] can get away with doing this very simple thing with Excel,
[03:09] why don't do it with Excel?
[03:10] Or if you are getting started in this field,
[03:14] it's better to begin with the tools that you already know
[03:16] and many people already know that it's Excel, right?
[03:19] Yeah, and one of the things we wanted to do with this episode
[03:21] was really getting some of the nitty-gritty.
[03:23] So we have a rapid-fire series of questions at the end,
[03:28] really about the kind of people's top tools,
[03:30] some of their foibles, some of the stories they care about.
[03:33] It's a lot of fun.
[03:34] And I would really advise sticking around to that,
[03:36] because you'll learn a lot about how each of them works.
[03:39] It was an excellent episode, I think.
[03:40] So let's get into it.
[03:42] Let's get into it.
[03:42] [MUSIC PLAYING]
[03:49] [MUSIC PLAYING]
[03:57] Hi, I'm Mary Jo Webster.
[03:59] I'm the data editor at the Star Tribune in Minneapolis.
[04:02] And hello, I'm Ben Welsh.
[04:04] I live in Los Angeles, where I work on a project
[04:07] at Stanford University called Big Local News
[04:09] and at the Los Angeles Times.
[04:12] Thank you so much for joining us, Mary Jo and Ben.
[04:15] We're really happy to have you both here today to talk really
[04:18] about local data journalism and how it's
[04:20] growing around the country, and I think around the world
[04:23] as well.
[04:24] But I would love you to talk a little bit first about how
[04:26] you got into this world.
[04:28] Now, Ben, when I was at The Guardian back in the day,
[04:31] when we were setting up the data blog,
[04:33] we were definitely looking at the work
[04:34] that you guys were doing.
[04:35] And I feel like you were actually really
[04:37] one of the innovators in this field.
[04:39] So talk to me a little bit about how
[04:41] you got into data journalism and the kind of work that you do.
[04:44] Well, it definitely wasn't the plan.
[04:46] It wasn't anything that happened on purpose,
[04:48] but I'm really glad it did.
[04:50] I was a pretty listless undergraduate in Chicago,
[04:53] Illinois at DePaul University and kind of unclear
[04:56] of where I was going with my life and career,
[04:58] like perhaps many undergraduates that Alberto
[05:01] encounters from time to time, and maybe himself as well.
[05:06] And I won't bore you with the whole long story,
[05:09] but through sort of an unusual series of events,
[05:12] I became the assistant to two local television journalists
[05:15] in Chicago, who were both excellent practitioners.
[05:18] And I became what was known at that time
[05:20] as the foot man, which was a very glorified term
[05:23] for the intern or the helper around the office.
[05:25] And so as they prepare a weekly newspaper column
[05:28] and television piece, I would do research and scheduling
[05:31] and a little bit of television editing too.
[05:34] And in that, I kind of caught the journalism bug.
[05:36] And one of the early stories we worked on
[05:38] was about corruption in a Chicago suburb.
[05:41] Locals could probably guess which ones.
[05:44] And we did my first public records request
[05:46] for legal fees from a sort of local heavy
[05:49] who was milking the city out of money.
[05:51] And it came as a really, really long printout.
[05:55] Remember when printouts used to have the perforated tears
[05:58] along each side and a single print over 20, 30 pages
[06:03] will be connected through these long strips in the side
[06:05] that you have to rip off, right?
[06:07] And we got that back, this epic printout
[06:10] of all the legal fees.
[06:11] And we thought, well, what are we gonna do with this?
[06:12] How are we gonna turn this into a story?
[06:14] And I don't even know from where,
[06:16] but I got the idea of just typing it into a spreadsheet.
[06:19] I think it was probably the first spreadsheet
[06:21] of my entire life.
[06:23] And I spent having nothing better to do.
[06:25] I spent a good chunk of time just hammering all that in
[06:28] and we added it up.
[06:29] And at the end, that number got read out on the local news.
[06:33] The anchor even climbed up a step ladder
[06:35] and dropped the sort of accordion of paper live on camera,
[06:38] which was kind of a funny little gag.
[06:40] And I kind of caught the bug.
[06:41] You know what I mean?
[06:42] And within that, I saw that through technology and data,
[06:46] it was kind of like a way I could help.
[06:48] It's something I could chip in and do
[06:50] as a place for me to contribute.
[06:52] And it also was a gateway to doing more ambitious journalism
[06:55] than just the average run of the mill story.
[06:57] We did a story nobody else was doing
[06:59] that was more ambitious and analytical,
[07:02] and that was exciting.
[07:03] And so I kind of just got into it there.
[07:05] This is now 20 years ago, I guess,
[07:07] or more than that actually.
[07:09] And I just followed that and followed that.
[07:12] And without boring you with my whole life story,
[07:14] I mean, I kind of ended up on this podcast today.
[07:17] - Mary Jo, tell us about how you caught the bug.
[07:22] - Mine is very much a local news-based situation.
[07:26] I was, I don't know, three or four years into my career
[07:28] working as a reporter in Oshkosh, Wisconsin,
[07:32] a very small daily paper.
[07:34] I was tasked with covering the police and sheriff's department
[07:37] both on a kind of a policy standpoint,
[07:39] but also, you know, I went out to the crime scenes
[07:42] and the fires and whatnot.
[07:45] But I was covering the city budget
[07:48] and it looked like the police department
[07:50] was growing its budget dramatically one year to the next.
[07:52] And I was sitting there with a calculator
[07:54] trying to calculate the percent change
[07:56] between the two years for all of the departments.
[07:59] And a colleague of mine came over and said,
[08:02] do you know what Excel is?
[08:03] And I was like, no, no idea.
[08:06] He opened it up.
[08:07] He showed me how to do a simple percent change formula.
[08:10] My eyes were bugging out.
[08:11] I was like, wow, this is so cool.
[08:14] Where did you learn this?
[08:16] You see, he told me I went to this IRE&NICAR bootcamp
[08:19] and they teach you all this stuff called,
[08:21] back then it was called computer-assisted reporting.
[08:24] And I was immediately hooked
[08:26] and I decided I had to keep going.
[08:29] And sometime later that year,
[08:32] I was able to attend a training that Sarah Cohen did.
[08:35] She was the IRE trainer at the time.
[08:38] And I remember sitting in this room.
[08:40] I don't think it was a hands-on training.
[08:42] My memory serves.
[08:44] But I just remember being enthralled
[08:46] by everything she was saying
[08:48] and mind being blown by this entirely different way
[08:53] of reporting that I had not learned in college
[08:56] that my editors had not taught me in my early years.
[08:59] And it was a complete turning point for me.
[09:02] - That's amazing.
[09:03] Both historical, welcome to the podcast in the first place.
[09:06] So thank you for being here.
[09:08] But the stories that you are telling
[09:10] are similar to some of the stories
[09:11] that I tell my students that
[09:13] when you see data journalism or my own field,
[09:16] data visualization from the outside,
[09:18] it looks like these very shiny, super advanced,
[09:22] high technology kind of endeavor.
[09:25] But when you see how this software is made,
[09:27] it's actually sometimes pretty pedestrian, right?
[09:30] It's like you essentially type the data
[09:32] into an Excel spreadsheet
[09:34] or you don't use machine learning algorithm.
[09:37] No, you just use a simple Excel pivot table, right?
[09:40] So I find all that absolutely fascinating.
[09:43] I mean, we are going to go back and forth
[09:45] between your professional careers,
[09:47] but also other sites of your professional endeavors.
[09:54] I'm really curious about your educational endeavors
[09:57] because both of you have been involved
[09:59] in educational initiatives, right?
[10:01] Can you talk a little bit about that?
[10:02] Both of you?
[10:05] - You mean what training we have taken or the teaching?
[10:10] - The training that you offer,
[10:11] because for example, Mary Jo,
[10:13] you have a data journalism academy, right?
[10:16] So both of you have been involved
[10:17] in these types of initiatives.
[10:19] Not only, I mean, you didn't take advantage
[10:21] as of things such as like IRE NYCAR,
[10:23] but you also offered to the community.
[10:26] - Yes, I am a huge believer in teaching.
[10:29] I think it has helped me
[10:33] as much as it helps the other people
[10:34] because I believe very much in the learn, do, teach approach.
[10:39] I found all the holes in my knowledge
[10:41] by having to turn around and teach it to somebody else.
[10:44] So I've also gotten so much back from other people.
[10:49] That's how I learned.
[10:50] So I feel the need to turn around
[10:52] and teach it to other people.
[10:55] I have taught for about 10 years as an adjunct
[10:58] at the University of Minnesota,
[11:00] a data journalism class that was fully dedicated
[11:04] just to data journalism.
[11:07] I'm currently a senior fellow
[11:08] with the Center for Health Journalism's
[11:10] Data Journalism Program,
[11:12] in that we do a week-long training
[11:14] and then we do six months of mentoring
[11:16] and additional training throughout that time.
[11:19] I'm particularly enjoying the mentoring part of that
[11:23] because it's another level of teaching.
[11:25] You're helping them get a project off the ground.
[11:28] You didn't just teach them how to run things in Excel
[11:30] and then walk away.
[11:32] You keep it going and help them get a story out the door.
[11:35] And it's quite fulfilling, actually.
[11:38] And then I taught at IRE and Nicar a lot
[11:42] and I built up so many materials
[11:44] that I've gradually decided I should share those materials.
[11:48] And at the start of the pandemic
[11:50] when I had a bunch of free time, I thought,
[11:52] you know what, it's time to turn some of these materials
[11:54] into videos.
[11:56] So I made a new website and posted a lot of stuff there,
[11:59] mainly Excel and other teaching things.
[12:03] I am doing a new thing right now
[12:05] that I'm actually gonna present about this
[12:07] at the Nicar conference this year.
[12:10] I've created a little data for editors training module
[12:14] for the assignment editors in our room.
[12:16] And so these are the people who work directly
[12:18] with the reporters, mainly in our local news section.
[12:22] And I've broken it down.
[12:23] I'm not teaching them how to use Excel
[12:26] or how to run data or any of that.
[12:29] I've broken it down into little sections
[12:32] of the different points in a data-driven story
[12:35] that the editor needs to weigh in and look at
[12:40] and what they should be doing at each point
[12:42] and what they should be looking for.
[12:43] So like the key ones are that brainstorming process
[12:48] with their reporters when they're talking
[12:50] about what story should I do?
[12:51] What should I tackle?
[12:53] Looking, having, I'm giving them some clues
[12:56] that they should be watching for
[12:57] that maybe there's a data opportunity here.
[13:01] What questions they should be asking, things like that.
[13:04] And then the bulletproofing process.
[13:06] Okay, the data has come in.
[13:07] We've got findings.
[13:08] We're showing you a bunch of charts and tables.
[13:11] What do you do with it?
[13:12] Too often the editors just look at it and go,
[13:15] oh, that's pretty.
[13:16] Oh, that's kind of interesting.
[13:17] And I'm trying to teach them like,
[13:19] how about we take it to another level
[13:21] and you really help bulletproof it.
[13:23] And then the third level is the editing the written story.
[13:28] One of the things I've seen a lot as editors
[13:30] see a paragraph full of numbers
[13:33] and they just glaze right over it.
[13:36] And they don't think about like,
[13:37] what are we really saying?
[13:39] What are the key points we're getting across?
[13:41] So I'm trying to teach them some editing things around,
[13:45] you know, what's the key finding?
[13:48] You know, not lumping too many numbers in a paragraph.
[13:50] All these kinds of things to try to help
[13:52] make the story more powerful.
[13:54] And then we're also gonna do a graphic session.
[13:57] My colleague at the head of our graphics department
[13:59] is gonna help me with that.
[14:00] And that'll be more about, okay, when should we use graphics?
[14:03] What kinds of graphics should we use?
[14:05] What should you as an editor be doing
[14:07] when we send you those proofs?
[14:09] Instead of just saying, oh, that looks beautiful.
[14:12] What else should you be doing?
[14:13] And I'm really anxious to see what kind of outcome that gets.
[14:19] If it leads to more data-driven stories on a beat basis
[14:25] and more daily basis or a weekly basis,
[14:27] not just the big enterprise kind of stories.
[14:31] - What about you, Ben?
[14:32] Can I talk a little bit about your educational efforts?
[14:35] - Well, I think Mary Jo said something
[14:36] very sort of succinct and wise there
[14:38] with the learn D2, learn, do, teach formulation.
[14:41] You know what I mean?
[14:42] It's a great way.
[14:43] It definitely benefits yourself.
[14:44] But I also think there's another great thing
[14:45] about that kind of approach or model
[14:47] is there's really a very sort of like stimulating
[14:50] and actually rewarding creative process
[14:53] of converting something that you've learned how to do
[14:55] into like a lesson or something
[14:56] that you can teach someone else.
[14:58] And it actually is like, it kind of gets the neurons firing
[15:01] the same way when you're like kind of innovating the code
[15:04] in the first place for the idea.
[15:05] And so for me along those lines, you know,
[15:08] one like sort of approach to teaching that I've enjoyed
[15:13] or found fruitful is taking emerging new techniques
[15:17] that we're developing in the newsroom
[15:18] that I feel like are kind of like a big deal for me
[15:20] or a big deal for our team,
[15:22] but that I don't see like being taught
[15:24] and then trying to convert that
[15:25] into like a night-card bootcamp or class
[15:28] to try to spread it to other people.
[15:29] And so, you know, examples I've done this with not alone.
[15:32] I always try to seek out other folks
[15:34] to help me like really strengthen the idea.
[15:36] But with others, we've created some of the earliest
[15:39] night-card bootcamps on how to use Jupyter Notebooks,
[15:42] how to use the static site frameworks
[15:44] that so many newsrooms use to make standalone visual stories
[15:48] but like nobody ever talks about, right?
[15:50] And this year at night-card, we're gonna have,
[15:52] I think the first kind of three hour bootcamp
[15:54] on how to automate a scraper
[15:56] within the GitHub actions framework.
[15:58] And so to me, like those three things, Jupyter Notebooks,
[16:02] static site page building, Git scraping
[16:04] are three really important like technologies
[16:07] that have taken off in our world
[16:08] in the last five or 10 years.
[16:10] And it's just been fun to try to like think about
[16:13] and pin down, well, how exactly do we use these?
[16:15] What are the fundamental skills?
[16:17] How can I pass that on to someone else?
[16:20] - It's really interesting to me
[16:21] how you're both so involved in teaching.
[16:26] And I wonder, does that reflect towards,
[16:29] I wonder if this is particularly applies to people
[16:32] working with data on a local level,
[16:34] is often people are kind of isolated.
[16:37] Often it's like one or two people on their own,
[16:39] they've got no support, there's no network.
[16:42] But the colleagues from other organizations
[16:45] are kind of struggling in the same way.
[16:47] So there's a kind of a need there, is that fair?
[16:49] - I love the psychoanalytical take here.
[16:51] I mean, I did grow up as a lonely, nerdy kid
[16:54] on a grand full road side,
[16:56] who didn't have a lot of friends.
[16:58] And so maybe this is me still trying to connect,
[17:01] you know what I mean, and get over.
[17:02] I was not crazy.
[17:06] - Well, I think you're right.
[17:07] There's so many that, you know,
[17:08] these days they refer to themselves as lonely coders.
[17:12] The majority of my 20 years in data journalism
[17:15] was spent in a newsroom where I didn't have support
[17:18] from anybody else.
[17:19] And so I sought it out from other people
[17:21] and learned from other people.
[17:23] And then, you know, I turned around and taught.
[17:24] But I think another factor is the Nykar community
[17:29] has always been a sharing community.
[17:32] If you even look at how Nykar was formed in the first place,
[17:36] it was really a group of investigative reporters
[17:39] who realized they needed to learn how to work with data
[17:44] and they didn't know.
[17:46] And a few of them learned somehow, I'm not sure how,
[17:48] and then they decided to share it with the others
[17:51] to try to pass it along.
[17:52] And so it was really, that's how Nykar got started.
[17:55] And that has been infused throughout all the years.
[17:59] I remember so many boot camps,
[18:01] and I think Ben is guilty of this,
[18:03] of people sitting at the bar at the end of the conference
[18:06] helping somebody with their data, you know, hovering over
[18:10] a laptop, helping someone with their data
[18:12] when they should be, you know, having fun
[18:14] and enjoying themselves.
[18:15] But that's how much I think our community likes to share
[18:19] and teach each other and it just, it's wonderful.
[18:22] - Yeah, and just to add to that,
[18:23] I mean, I think if you follow the origins back
[18:26] like Mary Jo was doing, I mean, data journalism's roots
[18:29] go a lot further than I think is commonly recognized.
[18:31] You know what I mean?
[18:32] It's really a practice that stretches back decades
[18:34] into the invention of the computer in the '60s and '70s.
[18:37] And I think is that culture of those early days
[18:40] is probably in like an anthropological sense,
[18:43] like indistinguishable from the hacker culture of MIT
[18:47] that's so celebrated.
[18:48] You know what I mean?
[18:49] It really was a very similar people
[18:52] with very similar sort of ethos of sharing
[18:56] that is the wellspring for a lot of what we do today.
[19:00] And I think that, you know, as an inheritor of that,
[19:03] I'm continuing to be inspired by it
[19:05] and I feel some obligation to uphold it, you know?
[19:08] - I think that we all do.
[19:09] We all have the sort of like feel the need
[19:11] to contribute back to the community that helped us
[19:15] at the beginning of our career, right?
[19:17] I have a question related to what you were both saying
[19:20] about, you know, growing up as a nerdy kid, right?
[19:23] I sort of sympathize with that because, I mean,
[19:25] people don't see where I'm sitting right now,
[19:27] but I'm sitting in a room with my kid
[19:30] and then surrounded by comic books, science fiction novels,
[19:34] you know, books about history and psychology
[19:37] and then some tabletop games.
[19:38] So the nerdery comes naturally to me.
[19:41] So has that improved or has that changed?
[19:44] It's like, are we still seeing or are you still seeing
[19:47] as sort of like the nerds in the newsroom
[19:49] or has that situation changed
[19:51] and data journalism, data visualization,
[19:54] computer assistant reporting,
[19:55] has it has become more widespread and more accepted
[20:00] or more popular within local newsrooms?
[20:02] What do you think?
[20:04] - I think it is more widely accepted
[20:07] as a tool that every reporter should have in their toolbox.
[20:12] I like to talk about data analysis
[20:14] as really another form of interviewing a source.
[20:17] That's all we're doing really.
[20:19] And it just requires kind of a little bit
[20:21] of a foreign language to do it.
[20:22] I also see a data journalists increasingly
[20:25] as the innovators in the room.
[20:28] In all the newsrooms I've been in
[20:30] for quite a few years now,
[20:32] the data journalists have been at the forefront
[20:34] of whatever new digital innovation we're gonna do.
[20:38] And they're the ones pushing for it and seeking it out.
[20:41] And in my newsroom right now,
[20:44] my team is the one often called upon
[20:47] for like, what do we do next?
[20:49] Where do we go?
[20:50] And I think that's wonderful.
[20:51] And I think that takes us out of the nerdy idea
[20:55] into kind of more of the leaders.
[20:59] - Yeah, and you see this in the product realm too,
[21:01] where a lot of people out of the data journalism world
[21:03] have become leaders in the next generation
[21:06] of digital business products
[21:07] their companies are putting together,
[21:09] which is a whole other level beyond
[21:12] just like what's happening in the newsroom.
[21:15] To be frank with you,
[21:16] I think in the last two or three years,
[21:17] we've seen a quantum leap with data journalism in newsrooms.
[21:21] The takeover has happened.
[21:24] You see it at the prestige level with those metrics.
[21:27] You look at the Pulitzer Prizes last year,
[21:29] five of the Pulitzer Prizes
[21:31] went to data journalism projects,
[21:33] including the public service highest award,
[21:37] which went to the New York Times COVID tracking effort, right?
[21:40] Which on its own was a gigantic public service
[21:42] and an incredible feat of like data gathering and analysis.
[21:46] But it also was the most popular
[21:50] and I'll say it most lucrative thing
[21:53] the New York Times has ever published.
[21:55] And those COVID tracking things
[21:56] I bet at the Minneapolis paper
[21:58] was the most popular thing ever published.
[22:00] I'll just throw that out there.
[22:01] Mary Jo can correct me.
[22:02] It's the most popular thing
[22:03] the Washington Post ever published.
[22:04] It's the most popular thing
[22:05] the Los Angeles Times published.
[22:07] And I don't mean to demean the news by saying that,
[22:11] but once the product you're creating
[22:13] is both the high prestige thing
[22:16] and more importantly, the economic and economic engine.
[22:21] You know what I mean?
[22:22] That's growing the digital audience
[22:23] of which there's a desperate need to grow.
[22:26] The entire way the organization looks at you changes
[22:28] just fundamentally.
[22:30] And that's happening.
[22:32] - Yes, the tracker we'd put up was,
[22:34] is hands down the historically most read page
[22:38] in the history of the Star Tribune.
[22:40] And the numbers on it are so astronomical
[22:42] that I don't envision it being that record being broken.
[22:47] So it would take something pretty massive,
[22:49] I think to break it.
[22:50] But the other interesting thing we saw
[22:53] is that it was also the most popular page
[22:56] among our digital readers who are not subscribers,
[23:01] but they come to us pretty regularly
[23:03] and we would like them to be subscribers.
[23:06] We call them in tenders.
[23:07] And it was the most popular page among them.
[23:10] And now we have conversations going around
[23:14] what else like that, maybe not data,
[23:17] but more in the service journalism realm,
[23:20] but data is one of the things you can do
[23:23] for service journalism.
[23:24] What can we do to help get more of those people
[23:27] to become subscribers?
[23:29] - Exactly.
[23:30] This all changed.
[23:31] And Steve Kornacki was named
[23:32] one of the sexiest men alive guys.
[23:34] Come on, we're on top.
[23:37] Yeah, the dog agrees with that assessment.
[23:40] So we have talked a lot about COVID on this podcast.
[23:44] And I wonder what you think the longer term effects
[23:47] are gonna be for your field
[23:49] of that very data intensive experience
[23:53] of working with COVID data in a way that readers
[23:56] and users actually really responded to.
[23:59] - I mean, the tracker thing is here to stay.
[24:01] And I think the creating dashboards for the general public
[24:05] on major topics they care about is not new.
[24:07] I mean, it's something that's been around with weather
[24:10] and sports for a long time.
[24:12] But I think that it's a type of product
[24:14] that clearly our audience wants
[24:16] that is in my opinion, like capital J journalism
[24:19] in most circumstances, but requires a new assembly line
[24:24] to be constructed inside the metaphorical factory
[24:27] of your newsroom to create that.
[24:29] You know what I mean?
[24:30] It requires a little bit more
[24:31] of a product development mindset,
[24:33] a longer development cycle, a more iterative approach,
[24:36] you know what I mean?
[24:37] Than juggling what's on Sunday's front page
[24:40] and the sort of like day-to-day heartbeat of the newsroom.
[24:43] And so I think there's a way in which
[24:46] to produce more of those kinds of widgets,
[24:49] data journals and teams are gonna have to find a way
[24:52] to create a kind of app workflow
[24:54] that's able to support that.
[24:56] And I think that's becoming more
[24:57] like a product development team in some ways.
[25:00] - From a different perspective,
[25:01] I think it's gonna have a long-term effect in newsrooms
[25:05] because I think it really woke up a lot of people
[25:08] to the need and benefits to being able
[25:11] to analyze your own data
[25:13] and not rely on a government official
[25:15] to hand you some numbers on a press release.
[25:18] Especially in those early days
[25:19] where we kind of had to pull stuff together ourselves.
[25:23] But then even more long-term is data literacy,
[25:28] increasing understanding among reporters and editors
[25:31] for the need to understand
[25:33] all the weird little caveats behind data
[25:36] and the problems we have with denominators and whatnot.
[25:42] And I think there's gonna be,
[25:45] I'm expecting to see greater appetite
[25:49] for data training in my newsroom.
[25:51] - We talked a lot about how data journalism is affecting
[25:56] the bottom line for news organizations
[25:57] and how they're all seeing the value now.
[25:59] If you're a reader or a user,
[26:02] you don't necessarily realize
[26:03] you're looking at data journalism, I guess.
[26:05] But what difference does it make to them?
[26:08] Do you think it helps people understand
[26:09] the local areas better
[26:11] in ways that we haven't quite kind of grasped
[26:14] the long-term impact of yet?
[26:17] - Well, I think the way we present data journalism,
[26:20] especially in charts and graphs and things like that,
[26:23] allow people who maybe need a visual aid like that
[26:27] to see something rather than reading
[26:30] long sentences of text,
[26:31] that some readers just do better
[26:34] with absorbing information that way than others.
[26:37] And I think also the readers maybe
[26:40] who aren't naturally inclined to do that
[26:43] are getting better at it because there is so much of it.
[26:47] - We all have, oh, sorry, Ben.
[26:50] Were you going to say something?
[26:51] We can make a pause.
[26:52] Let's set this up, okay, perfect.
[26:56] I was about to say, let's begin there,
[26:59] we all have projects in our past as professionals
[27:02] that we are very fond of,
[27:04] even if they are not perhaps the best projects
[27:06] that we have ever done,
[27:07] but there are certain projects that we hold dear, right?
[27:09] So if you had to choose just one of your local
[27:13] data journalism projects in the past, right,
[27:16] what would that be and why?
[27:18] What was the story about?
[27:20] What do you do to gather the data or visualize
[27:22] or display the data, explain the data,
[27:25] and what type of impact that story had?
[27:30] - In 2018, we did a series called Denied Justice
[27:34] that looked at how the criminal justice system
[27:37] in Minnesota was handling sex assault investigations.
[27:41] A reporter came to me and said he was hearing
[27:44] that very few cases that get reported to police
[27:49] actually result in a conviction, but nobody tracks that.
[27:53] It's not a number that someone calculates.
[27:56] So we put in requests for thousands of police
[28:00] investigative reports and we built our own database,
[28:05] tracking key bits of information off these cases
[28:07] and then hunting down, was this referred to the prosecutor?
[28:11] Were charges filed?
[28:13] Was somebody convicted?
[28:15] And a mountain of work in order to get a few numbers.
[28:19] And what we found is like 8% of reported cases
[28:23] result in a conviction.
[28:25] And we ended up doing a series,
[28:27] it ended up being a nine part series,
[28:28] which we did not expect,
[28:30] because it ended up being so powerful.
[28:33] The response was overwhelming from readers and policymakers.
[28:37] And we had hundreds of victims come forward
[28:41] to share their stories because they read our first story
[28:45] and were like, oh my gosh, I'm not the only one
[28:48] who had this bad experience with the police department.
[28:51] I'm not the only one.
[28:52] This is so cathartic to hear this
[28:56] and they wanted to share their stories.
[28:58] And as a result, Minnesota's sexual assault laws
[29:02] have been overhauled quite substantially.
[29:05] Several police departments have added additional staff
[29:10] or like victim advocates embedded into their department,
[29:14] prosecutors embedded into the department
[29:16] to help improve the quality of the investigations.
[29:19] And it was one of the most,
[29:24] probably hands down the most rewarding
[29:26] piece of work I've ever done.
[29:28] At the end of it, we gathered all these victims
[29:32] at the state Capitol and took a picture
[29:35] of all of them together.
[29:37] And these women came out in the bitter cold in Minnesota
[29:41] in December to do this photo.
[29:43] And they came up to me and the other reporters
[29:45] and they were so thankful.
[29:48] And I've never had that experience before
[29:51] that the people just thanking us for the work.
[29:54] And that story is a really big combination
[29:58] of data being the spine holding up the key findings
[30:02] and then these powerful emotional stories from victims.
[30:06] And policy makers told us that the two things combined
[30:09] were so powerful that they could not ignore it.
[30:16] - Yeah, when the data comes together
[30:17] with real life in a way, you know what I mean?
[30:19] Or when the data leads you to the doorstep
[30:21] you didn't know you needed to go to, right?
[30:24] That is just this sort of like unforgettable feeling.
[30:28] You know what I mean?
[30:29] It's kind of like one of the deeper work hits.
[30:32] It's better than any hack, you know what I mean?
[30:34] And so like for me, like an example of that was
[30:36] we had a circumstance where there was like
[30:39] a little political fight in LA government
[30:41] over the fire department that was really pretty superficial
[30:44] and was just a couple of people
[30:46] trying to score political points.
[30:47] But it raised some questions about the efficacy
[30:50] of the 911 system in the city of Los Angeles.
[30:53] And so my colleague and I, Robert Lopez,
[30:55] decided to get a copy of the entire 911 calls database
[30:59] and analyze what was happening with the 911 system.
[31:02] And you know, there's a lot of ins and outs
[31:05] to how it all worked out.
[31:06] But the reality was is by doing our own
[31:07] independent analysis of the data,
[31:09] we were able to identify several structural flaws
[31:13] in how the 911 center operates,
[31:15] how they handle calls near the city border
[31:17] with neighboring fire departments,
[31:19] and then go out into the real world tracking real calls,
[31:22] knock on doors and find out what happened.
[31:24] And in that we discovered these kind of deeper problems
[31:27] with the system that nobody was talking about
[31:29] in city politics.
[31:30] And we were able to really point to where the system
[31:33] needed to improve to like make a difference.
[31:36] And then a change actually did happen, you know.
[31:40] And to me, that's sort of like that feeling of
[31:44] where the statistics lead you to the store,
[31:46] you listen to the numbers,
[31:48] and then you follow them out into the real world.
[31:50] And then you connect that with what's really happening there.
[31:52] And then you bring that back to your audience.
[31:54] That's better than any chart.
[31:56] That's better.
[31:56] You know, I love charts, you know what I mean?
[31:57] But like, that's better than any little dinky thing.
[32:01] That's the deeper like thing
[32:03] that numbers and statistics can lead you to,
[32:06] which is the sort of hidden truth,
[32:07] but the deeper story that isn't being told.
[32:10] And that's the hit, you know,
[32:14] I'm still looking for it on every story to get back to.
[32:17] I'm still chasing.
[32:18] - Those are both examples of stories where a human
[32:21] is probably not gonna come forward and tell us
[32:23] that there's a story there.
[32:25] Maybe they'd have an inkling,
[32:26] but probably nobody did the data analysis
[32:30] to know for sure that that's what it is.
[32:32] And it takes us to do these kinds of lifts
[32:37] to heavy lifts on data to find those stories.
[32:40] And, you know, as I said, interview the source
[32:43] to get at the meat of that story that needs to be told.
[32:47] - And then sometimes I think this may have happened
[32:49] in Mary Jo's case too.
[32:50] Your finding, your synthesis of what's really going on
[32:53] actually gives a name to and some meaning to something
[32:57] really negative that happened to somebody.
[32:58] And then they decide, then your sources
[33:00] and the people you're writing about
[33:01] find this deeper reward in the whole thing.
[33:04] And it's very rare, you know what I mean?
[33:06] But to me, that's as good as it gets.
[33:10] - That combination of like traditional reporting
[33:14] with the data and that sweet spot event have impact as well
[33:18] is just everything you can ask for, isn't it?
[33:21] So we are in February, this is our first part of the year.
[33:24] If you two were to make predictions,
[33:27] I know everybody does make predictions,
[33:28] but you two make predictions
[33:30] about what your big exercise is gonna be this year.
[33:35] - Let's let Mary Jo off the hook here.
[33:36] I don't want you to make her pop up too much.
[33:38] So since I'm working on this new project at Stanford
[33:41] called Big Local News,
[33:42] which is really an open source collaborative effort
[33:45] to try to be a data backbone for all the local newsrooms
[33:49] who maybe need help or some consulting
[33:53] or some technology to really pull off big data projects,
[33:57] we're looking to take it up a notch, Simon.
[34:00] I mean, I think this year, this team I'm on
[34:02] with Cheryl and Surdar and everybody up on the farm
[34:05] in Palo Alto, I'll say it here,
[34:07] we are going to build the biggest web scrapers
[34:09] in the history of data journalism.
[34:11] We're gonna, we have a project called Agenda Watch
[34:14] where we're gonna scrape hundreds of city council agendas
[34:17] across the country, put them into one database,
[34:19] and we're gonna try to do the same thing
[34:21] with county courthouses.
[34:22] And you know, those types of projects,
[34:24] scraping your local county courthouse or city council,
[34:27] I mean, every local newspaper in America
[34:29] has wanted to do it.
[34:31] Some of them have tried to do it.
[34:34] A very few have succeeded in doing it
[34:37] for like a short period of time,
[34:38] but I just don't think that there's been a lasting
[34:41] and sort of broad solution to that challenge
[34:44] because it's a lot of work, there's competing priorities,
[34:49] you know, da, da, da, da, da.
[34:51] And we're really looking to solve that or try to
[34:54] by being the sort of database library
[34:56] that attacks those big things.
[34:58] And so by December 31st,
[35:00] we'll be the biggest scraper you've ever seen,
[35:03] outside of Google, of course.
[35:05] - I love the Local News Project,
[35:07] I'm really glad to hear that.
[35:08] (upbeat music)
[35:13] Okay, so we are gonna finish off with a new section,
[35:17] which I'm gonna just call no dumb questions
[35:19] because these are quite dumb questions.
[35:22] And they're one word answers,
[35:23] but I'm gonna go first.
[35:25] And my one to Mary Jo is,
[35:29] what book or class had an impact on you
[35:32] as a professional?
[35:35] - The Investigative Reporter's Handbook.
[35:38] - Very good, how about you, Ben?
[35:41] - Oh, just one?
[35:42] - Yeah, just one, I'm afraid, sorry.
[35:44] - Oh, man.
[35:46] - Be concise, we are all journalists,
[35:48] we are supposed to be concise.
[35:49] - Gotta be concise, all right, I'm gonna do it.
[35:51] I will say the one that had an effect on me,
[35:53] I will say The Autobiography of Malcolm X
[35:57] by Alex Haley, guys.
[35:58] How about that?
[35:59] It's like a young teenage me,
[36:01] you know what I mean, moving from the country to Chicago,
[36:03] getting a broader view of the world.
[36:05] It was nonfiction journalism
[36:07] that helped me like see what was up,
[36:09] you know, and its book is an alternative version
[36:11] of American history that I hadn't heard before.
[36:14] It's also a religious conversion story,
[36:16] not unlike one I've been taught in church,
[36:19] you know, as a kid who went to Sunday school.
[36:21] And it shows the power of stories
[36:23] that go against the grain
[36:25] that aren't otherwise being told.
[36:28] And it's a great piece of literature.
[36:29] So if you haven't read it, you know,
[36:32] you should check it out.
[36:35] I think sadly in some ways, you know,
[36:37] we've regressed since then.
[36:39] You know, that was a time when Alex Haley
[36:41] could have the most popular show on television
[36:43] and write a really provocative book.
[36:46] And as a reminder of the power,
[36:50] and I would say virtue of the mainstream media,
[36:53] when it's properly focused, you know.
[36:56] - Those were the times.
[36:58] Okay, next quick question for both of you, actually.
[37:01] So if you could keep just one of the tools
[37:05] that you commonly use in your daily job,
[37:07] what would that tool be?
[37:10] - R.
[37:12] - I like that answer.
[37:14] What about you, Ben?
[37:16] - Oh, a spreadsheet, guys.
[37:17] It all begins with a spreadsheet.
[37:19] You can't play baseball without a bat
[37:21] and you can't crush data without a spreadsheet.
[37:23] And--
[37:24] - Oh, you can, you can, R can do everything.
[37:28] There's so many things that R can do that Excel can't.
[37:31] - These R people, they really love R.
[37:33] Have you guys noticed this?
[37:34] Like, I think--
[37:36] - I have actually noticed that.
[37:38] - Yeah, I think that, you know,
[37:40] not unlike, that it's like R conversion stories.
[37:43] It has an evangelical fervor and flavor,
[37:45] not unlike the Nation of Islam.
[37:47] - You are only saying that because you're a Python person.
[37:50] That's the only reason why you're saying that.
[37:52] I thought you were gonna say Python for sure.
[37:55] - No, I think it all comes down to the fundamentals.
[37:57] You know, you can't dribble.
[37:58] You can't play basketball if you can't dribble.
[38:00] Can't play baseball without a bat.
[38:03] I'll give you the religious flavor,
[38:04] is I'll pick Libre Office's Calc,
[38:06] the open source free alternative spreadsheet,
[38:08] which is just as good as the ones from those companies
[38:12] you maybe have heard of.
[38:14] - Okay, if you rephrase the question to
[38:16] what should any journalist have a tool,
[38:19] I would say Excel.
[38:21] But what should I, as a professional data journalist,
[38:24] doing this full-time, crunching numbers like non-stopped,
[38:28] then it's R.
[38:30] - All right, okay.
[38:33] Next one, what is your favorite hack
[38:36] that you are too embarrassed to admit?
[38:38] I can tell you my one is,
[38:41] and I still do this occasionally,
[38:42] is the way you can use the finder and place
[38:46] in Microsoft Word as a shortcut to getting stuff done
[38:49] if you're in a hurry.
[38:50] I still use it.
[38:51] I'm slightly embarrassed about it.
[38:52] So there you go, that's mine.
[38:54] Ben, do you go first?
[38:55] - Well, I'm not easily embarrassed,
[38:56] which maybe is a bug, not a feature in my personality.
[39:02] But I mean, a hack,
[39:05] saying no to other people in the newsroom.
[39:09] You know what I mean?
[39:09] I think that the data journalists,
[39:11] especially in smaller newsrooms,
[39:12] can sometimes get consumed by helping things
[39:18] just go the way they've always been going
[39:19] or sort of chipping in around the edges.
[39:21] And I think that being a helper
[39:24] and assisting that is noble work and often really valuable.
[39:27] But I see it pulled back a lot of other data journalists
[39:31] from really reaching their potential from taking risks
[39:33] and from doing the more ambitious, powerful work
[39:36] that our skills are capable of.
[39:38] So never be afraid to say no
[39:40] and never be afraid to bet on yourself
[39:45] and to take a chance.
[39:46] - I guess I don't know if I'd call this a hack,
[39:48] but I am probably most embarrassed to admit
[39:50] that I have data import scripts
[39:54] that I have not automated, but I probably should.
[39:58] And I just still keep running them.
[40:01] It's not exactly by hand, but they're not automated.
[40:03] I feel like I should be moving into the 21st century,
[40:06] but, oh well.
[40:09] - Well, I still run out of patience
[40:11] whenever I need to style graphs or charts in R
[40:14] and I export them as PDFs and I style them in Illustrator
[40:17] just because I find it faster.
[40:19] So that would be my personal hack.
[40:20] And I'm not embarrassed at all in admitting it.
[40:22] So we all have a skeletons in our closet, I guess.
[40:27] - I had trouble making a graphic in R yesterday
[40:30] and I was like, ugh, okay, I'm just gonna put it in Excel
[40:33] and I will make it in Excel.
[40:35] - I do the same thing with Illustrator.
[40:36] It's like R is not working, fine.
[40:39] Let me go to the Illustrator's graphing tool,
[40:41] which is sort of like the clunkiest graphing tool ever.
[40:44] So anyway, so let's go to the next one.
[40:49] What would you have done
[40:51] if you hadn't gone into journalism?
[40:53] - Oh, MG, I have no idea.
[40:55] I've wanted to be a journalist since I was 16, no clue.
[41:01] - Well, I was not in a very motivated place
[41:03] before I found journalism as we covered already, Alberto.
[41:06] But one thing I did like to do at that time in my young life
[41:09] was read pretentious books and be like a pretentious student.
[41:14] And so I think at that time, if you had asked me,
[41:16] I probably would have told you
[41:18] I would have become a high school English teacher
[41:20] or something, you know what I mean?
[41:21] And I don't think that that's a crazy outcome for me.
[41:26] And maybe I would have been happy doing that.
[41:29] It's hard to say.
[41:29] I probably would have stayed in Chicago
[41:31] and now that I think about it, never met my wife,
[41:35] which is making me sad to even contemplate.
[41:38] Like, you know, that's probably it.
[41:40] - So glad you didn't do that, Ben.
[41:42] (laughing)
[41:44] - Oh my God.
[41:45] My monologue to like high school students
[41:47] about of mice and men, it would be just terrible.
[41:50] - I would hope I need to hear that.
[41:52] Okay, last one, pie charts or tree maps.
[41:55] And I'm going to go to Mary Jo for this one.
[41:58] - Tree maps.
[41:59] - Like anything with visualizations, it depends.
[42:02] You know what I mean?
[42:03] Like, so like, I would say like,
[42:05] well, at first I would say never pie charts
[42:07] in the circular form.
[42:09] However, if you have like a single,
[42:12] like a categorical thing,
[42:14] I think the like underrated pie chart alternative
[42:18] is a single stacked bar.
[42:20] You know what I mean?
[42:21] Just like one rectangle sliced up.
[42:23] You can do the nice little labels on the top and bottom.
[42:26] And I guess it's technically a stacked bar chart,
[42:28] but it's just a single bar.
[42:29] It's also a pie chart.
[42:30] I think those are great.
[42:31] I think those really work and you can stack them.
[42:34] It doesn't like Pew do that on their quizzes.
[42:35] I think that's good.
[42:36] Cause like, cause maybe that is a tree map.
[42:39] I don't know.
[42:40] You guys tell me in some way it is a tree map, I guess,
[42:42] but like to me the tree map works
[42:44] when there's a lot of slices,
[42:46] like there's like 50 slices
[42:49] and like one or two of them are huge, you know?
[42:52] And you want to be like, these ones are big
[42:53] and these ones are small.
[42:55] But like, if you only have like five things, you know,
[42:58] you don't need that vertical part of the tree map.
[43:00] If it's just like a fixed wide height, it works fine.
[43:05] Sorry, I just did that.
[43:07] But that's really how I feel about it.
[43:09] - It's a great, it's a great answer.
[43:10] I mean, tree maps were created to show a nested hierarchies,
[43:15] right?
[43:16] So you have a total
[43:17] and then you subdivide that total into large divisions,
[43:19] for example, population of the world.
[43:21] And then you start dividing them into continents
[43:23] and each one of the continent pieces
[43:26] gets further subdivided into the country.
[43:28] So that's what tree maps are for.
[43:30] So it's an excellent answer.
[43:31] I would advocate for the pie chart though.
[43:34] So just to be the contrarian,
[43:36] I would vote for pie charts if I could.
[43:38] Simon, can I do that?
[43:41] - You don't get advocate for pie charts.
[43:43] - I did not expect that, Alberto.
[43:46] - I'm getting old.
[43:48] Getting older, you know, on a simple pie chart.
[43:51] I tweeted the other day, if a graphic works, it's good.
[43:55] And pie charts unfortunately are fortunate, often work.
[43:59] It's like if there are two, three subdivisions,
[44:01] four subdivisions perhaps, readers like them,
[44:04] readers understand them, readers read them,
[44:07] they bring attention to the data.
[44:09] So, you know, I'm getting older, more flexible,
[44:12] more pragmatic in some sense.
[44:13] So go for the pie chart.
[44:15] - Even the three-dimensional pie chart?
[44:17] - No, three-dimensional.
[44:18] That's going to, maybe when I'm 80.
[44:20] - That's the limit so far.
[44:21] - Yeah, that's, no, maybe when I'm about to retire,
[44:24] maybe I will accept three pie charts.
[44:26] - Wow, now that Alberto has been switched with somebody else,
[44:29] I think that's a good point to end.
[44:31] Ben Welsh, MaryJo Webster, thank you so much.
[44:34] - Thank you.
[44:35] - Thank you for having us.
[44:39] (upbeat music)
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