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Episode #70: Simon Rogers
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Welcome back to the PolicyViz Podcast! On this week’s episode, I’m excited to welcome Simon Rogers to the show. Simon is a data journalist, writer, and speaker and has worked at the Guardian in the UK, Twitter, and is now a...
The post Episode #70: Simon Rogers appeared first on PolicyViz.
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Introduction and Sponsorship
00:00:00
Speaker
This episode of the PolicyViz podcast is brought to you by JMP, Statistical Discovery Software from SAS. JMP, spelled J-M-P, is an easy to use tool that connects powerful analytics with interactive graphics. The drag and drop interface of JMP enables quick exploration of data to identify patterns, interactions, and outliers.
00:00:19
Speaker
JUMP has a scripting language for reproducibility and interfacing with R. Click on this episode's sponsored link to receive a free info kit that includes an interview with DataVis experts Kaiser Fung and Alberto Cairo. In the interview, they discuss information gathering, analysis, and communicating results.
Meet Simon Rogers
00:00:49
Speaker
Welcome back to the Policy Vis podcast. I'm your host, John Schwabisch. Happy holidays, everyone. I hope everyone is warm and cozy inside. One guy who may be a little cozy on the West Coast and I am here on the East Coast is Simon Rogers, who's the dated editor at Google, formerly at Twitter and The Guardian and maybe a couple other places. Simon, welcome to the show. How are you doing?
00:01:13
Speaker
Hi John, I'm happy thanksgiving to you. Thank you very much. You're released from the oppression of British yoke. I'm here to celebrate it with you.
00:01:23
Speaker
That's right. We're just bringing it all back together. It's just a historical thing all wrapped back together. How are you there? How's Google treating you? Google's treated
Google News Lab's Mission
00:01:32
Speaker
me very well. We've been allowed some really cool stuff and been given a lot of freedom to experiment and innovate, which is great. That's great. And you have a pretty good-sized team there now, right? Yeah, so we have... I'm basically part of the Google's News Lab, and Google's News Lab is really about
00:01:51
Speaker
What can Google do to help the news industry think about its future with all our expertise and knowledge and resources and all the stuff that Google has, how can it help news industry think about what's coming next? And the team that I work with most closely is around data, Google data, and helping to open that data up to the world and make it easier to use, provide inspiration about cool things to do with it, but also just make it easy for people to get a hold of and tell stories with.
00:02:21
Speaker
And it's kind of amazing. I mean, I think there are like three billion searches every day. That's an incredible kind of like view outside the echo chamber and the filter bubble that we all live in. And if we can use that data to tell interesting stories, I think that's a really powerful mission to have.
00:02:39
Speaker
So let's talk about stories because this was a big topic. And it was a big conversation about a couple years ago and then it sort of faded and I feel like at least I've been thinking about a lot more. So when you think about telling stories with data, what comes to your mind and how do you guys approach that storytelling aspect of Google data?
Evolving Storytelling with Data
00:03:03
Speaker
It could mean anything from just a line chart to a full-on interactive. I think we need to find another word. I don't know what that word is yet, but I think we're moving beyond that because there was a time when really all you had to do was make the data easy to interrogate so people could come up with their own tales of trying to find the words, their stories, their own way to analyze it and see what it means for them. I think we've moved away a little bit from that into these
00:03:33
Speaker
experiences where we guide people, we take people's hand and guide them through. And partly that's because, you know, everything is about kind of instant, right? We want instant, we don't want to spend, maybe we don't have time to spend like half an hour exploring something that's that intricate. And yeah, I think that that is a bit of a loss for us, because I think the idea of one of the great things to me about data opening up and data journalism opening up was the idea that we will be in control of what we saw. So for instance,
00:04:03
Speaker
You know, say, I mean, one of the things was back in the day, back in the day, there was just some map of from WikiLeaks, SAP, WikiLeaks before the election, for the Iraq attacks, where it was basically a map of every single attack in Iraq.
00:04:18
Speaker
and that was that was that did really well it did really well but i think because it allowed people to explore the data for themselves in a way that they could understand that feels to me still a really important part i just think that we're in this weird place now where people are much more literate about database and just doing a map or something isn't enough to have an impact you've got to do something really special to have an impact at the same time i'm not convinced that people
00:04:45
Speaker
once spent hours and hours and hours playing around with databases in the way that they did. People want stuff to be delivered straight away, so it's really difficult thin line to water down.
Global Data Journalism Trends
00:04:55
Speaker
In terms of storytelling, I think it's really about a lot of content now that's based around data. Data journalism is just simple, more important now than ever before, I would argue. It's like it's moving on, it's changing and evolving. It's not new. It's something that's well-established in lots of newsrooms now.
00:05:14
Speaker
around the world and there's some really interesting stuff coming up in the developing world. I'm director of the David Jones Awards and every year we get amazing entries from Africa and from the Middle East and so on.
00:05:26
Speaker
But at the same time, how do we kind of maintain that excitement that people feel about data journalism within the executive branch of the news industry? If you're working data journalism, there's no career path for you. One thing we've discussed with Scott Klein quite a lot is if the data editor leaves in newsroom, what happens? If the metro ads are left, they'd be replaced easily. Data editor leaves like the whole team can fall apart.
00:05:55
Speaker
So those kind of things suggest to me that it's become mainstream without becoming mainstream. If that makes sense. Yeah, yeah. They tell this was everywhere. Yeah. At the same time, it's like it's still really often just like one or two people in a room scrapping away with like, whatever tools they can get hold of. Right, that they're they're not a real part of the newsroom yet. And yeah, yeah, they're part of the process. I just think that it's not part of
00:06:23
Speaker
I think one of the weird things about working in the news industry is there's quite a clear structure to what people do and what their career path might look like. Within that, you get a lot of variations. But for a day's journalist in the newsroom, I don't think that structure is there in that way. I think it's quite difficult. You're not a developer, particularly. You're a generalist like me. It's not as simple as it is.
00:06:48
Speaker
But the people I've talked to, essentially because the people I've talked to, the Times and the Post and the Guardian and ProPublica, I feel like those groups, they bring the developers into the fold as really part of the whole news team. And that's why they're so successful is because they're bringing them in. And yet you probably have too many groups or have them just the way you talk about them sort of isolated off to the side. And certainly, I can tell you from personal experience of different
00:07:13
Speaker
non-data journalism places, but research places and federal agencies that they are definitely siloed out. I've got a team now of about seven people, and they're based around the world. I've got people in London and in Berlin and Paris, as well as here on the West Coast and somebody on the East Coast.
00:07:36
Speaker
And, you know, when we've been recruiting before that, I haven't actually been going for developers and analysts, I've been much more given people have had day to day journalism experience, because I want people to know what a news story is. But news in front of the word story there, people to understand, you know, this is this is what the news story is, this is what, what's interesting around that. Because, you know, that's hard to teach, I think.
00:08:03
Speaker
Yeah, I think people can learn skills and I know people are technically literate.
00:08:08
Speaker
understanding what a new story is feels to be really powerful and important. Do you feel in some ways that the part of the richness of having Google data is that everyone has that experience?
Google Data as a Social Signal
00:08:20
Speaker
It's sort of a shared experience. So while you could show a graph of Google Trends, and maybe the graph isn't the most striking thing in the world, everybody does a Google search. So everybody has that that feeling of doing that shared experience.
00:08:35
Speaker
There's three things about Google Data that I think are really interesting, one of which is, obviously, it's huge, so that three billion of the pages that gives it that sense of ubiquity you're talking about, that we all do it. The second thing about it is it's incredibly honest. I've said this before, but we're never as honest as we are with our search engines. You're not making a public pronouncement by doing a search in the way you would be by posting something on social media. There's an honesty to it, which I think is really powerful.
00:09:02
Speaker
And the third thing is this immediacy that's so important that basically, as soon as something happens, it's reflected in the way that we search. So we can explore that. I mean, we're just scraping the surface of what's possible with that data. We've only had real time search data for just over a year now, coinciding with the election campaign. So this is the first kind of Google Trends election.
00:09:25
Speaker
And it's not just things like spikes and searches for move to Canada, you know, you can see completely the things that people really care about, the kind of issues often which weren't talked about during the campaign that are big issues around the country. Abortion is still a huge issue around the states that wasn't really talked about that much during the campaign. But it will be a big issue, I'm sure, in this presidency.
00:09:46
Speaker
you know, gun control, are still huge issues out there in the country. But you can see that through the way that people search. And to me, that's really fascinating, because we we get a sense of what we think people care about, what we think people know about that actually, well, now we know what people care about and know about. But having said that, telling that using that data to tell, so as interestingly, is is is new things. So it's like we're kind of some ways I feel like we're a kind of archaeologists uncovering stuff.
00:10:15
Speaker
But at the same time, people have done some really interesting things. CNN just did this critical counties project where we go to county-level search data, and that very local-level granularity, I think is really interesting. In particular, there were a number of counties there where Clinton was ahead in the polls, but Trump was ahead in search interests, and Trump won those counties, which to me is really interesting.
00:10:38
Speaker
I mean, what's your take on the polling? I mean, it is a data rich area that we've just spent the last six months sort of like thinking about to one degree or another. I mean, do you think Google has process going forward to think about polling in different ways? So we don't do predictive stuff with trends, but other people do do, but we don't.
Search Data in Polling
00:10:57
Speaker
um partly because I don't know how yeah this stuff is new yeah but um also I think uh and yeah and people do too you know there's a there's a google consumer surveys within google which does kind of you know surveys online um you know which and and stuff on the island people I think sort of really enjoy those so that is there I think there is something
00:11:18
Speaker
I feel like I see this every election. We just had this with Brexit in the UK as well, where before every vote, polling is the best it's ever been. And then the vote happens and totally throws it all up. People are like, well, the model wasn't right, or this wasn't right, or we're reading the wrong stuff into the polls, I think. And we know more about, we have more people devoting more time to polling reporting in this election poll than ever before. Really? You couldn't move for analysis of the slightest
00:11:47
Speaker
I sent a shift in the polls and yet the margin of error was exactly that, right? The margin of error was what? The margin of error was essentially plus or minus infinity. We find out at the end. Exactly. And polling is expensive, right?
00:12:03
Speaker
I think there is other data I think is powerful. The idea that a search is just a search, just in inverted commas a search, I think is completely wrong. It's a really powerful social signal, but we just don't understand how to interpret that properly yet. That would be my take on that. I think that it's probably, I would argue it's the most powerful social signal because it's unconscious.
00:12:27
Speaker
you're not telling somebody something, you're not broadcasting it to the world, you're searching for something you care about and are interested in. And do you think people are going to respond to that in different ways? I kind of feel like a few years ago,
00:12:40
Speaker
started becoming clear, or people were sort of realizing that their search, what they were searching for is being fed back to them through ads and through marketing. This is what was me, we can have our own feedback loop. When we spotted the move to Canada trend, we just tweeted it out and it was like just a small thing. But then that started affecting people searching for move to Canada.
00:13:04
Speaker
So you can say, I think I feel like it's very temporal. So one of the things we worked with recently is we worked with Alberto Caro. And Alberto is working with us now as a kind of consultant art director. And so we're working with some really great designers and one of who was Georgia Lupe and Gabriel Rossi at Accurate.
00:13:24
Speaker
And what they made for us was a visualization which showed how people around the world were searching for the candidates in the run of the election. It's frozen on November the 8th. You can still check it out. It's at worldpotos.com.
00:13:38
Speaker
And one of the interesting things about that was they use this couple of techniques that everything's very fluid because the data itself is very fluid, right? It changes all the time. So it works in the moment, but it's changed by the time that we've tweaked it or pushed it out there. It's totally dynamic and it moves all the time. And I guess we want the visualizations that we do to reflect that. So you can really see that with something like, for instance, on Brexit night, one of the spikes we noticed from people in the UK searching for
00:14:05
Speaker
on how to get an Irish passport. There might not be very many people, but it was a noticeable spike or an increase, and the next day the Irish government's website went down, the passport website, because they weren't used to the traffic. So there is something there which is really interesting and tells us, and reflects, and tells us about interesting things that are happening out there in the world. But it's like it's a new science. It's like we're the first people to ever have done this.
00:14:30
Speaker
But it's also interesting from the Google perspective that it's, you are both the scientists, but also can affect the outcome, right? And like a traditional researcher, they collect the data, they do something, they can't impact anything. They can't impact people's behavior in the same way that a Google can where, you know, different algorithms or different things that the company can say, they can sort of impact the way things go, right? So I guess we don't really deal with that.
00:14:59
Speaker
Sorry. So, you know, the search algorithms are sort of a way outside my area of knowledge. I think there is something really interesting about when, you know, in my life, I guess I've had a few things that have gone viral, and not just at Google, you know, before I was Twitter as well. And when you see something go viral and then that data point then impacts future data points,
00:15:24
Speaker
Just because it's gone viral. It's so interesting to me how that happens and how that affects what people do. Yeah, I had a source when I was on Twitter, I did something that Charlie had to attacks and Jon Stewart used it, which is like, obviously, you know, still. Yeah, I know, right. And I mean, that was obviously an awful thing.
00:15:47
Speaker
But what happened there was that then that became this thing that influenced the tweets afterwards. It became part of the feedback loop. And the same thing, I guess, if we do something with search data that takes off and becomes picked up by people, does that have any impact on the data we're working with? So we're conscious of that. We're increasingly trying to do things, especially with visualizations, do things that dynamically change all the time to show the changing nature of the data.
00:16:14
Speaker
You know, people take snap. I mean, think about all data, like whether it's GDP figures or inflation or unemployment, they're all snapshots in time. Although there's snapshots in time of data that doesn't change very fast, whereas Google data, snapshot in time of data that changes really fast every few seconds. So, you know, inflation is
00:16:35
Speaker
is X percent on Tuesday is probably not going to change that much by Wednesday, although, you know, stuff happens, right? Sure. But with search dates, you know, it's going to change within a minute of you pulling within seconds of you pulling the figures. No, that's right. But some of those things, like what's interesting about the unemployment rate, I think is a good example, right? The unemployment rate from month to month may not move that much. But there's certainly within that month, there's a lot of churn.
00:17:00
Speaker
Some people are losing jobs, some people are gaining jobs. So you look at the average, I guess. You look at the average, but behind it is a lot of stuff going on, which we don't ever see. And if there were a Google that could track all of that, you would have a different picture, right?
00:17:18
Speaker
Yeah, no, I think it's really interesting. We're looking, we do a lot of averaging like that. So we're looking now, right now, the election, the election has dominated, like, please dominate our lives for last year. And you can still see at google.com slash elections, you can see a lot of the stuff that we worked on for that.
00:17:34
Speaker
But right now, we think we're moving ahead to think about year in search, which is a big kind of annual review.
Google's Annual Review Project
00:17:40
Speaker
And, you know, Google was like back in the day was the first company to do these kind of zeitgeisty reports. And they become the whole thing that everybody doesn't know everybody's a lucky annual round up. So what we've tried to do, we did this last year for the first time the news lab was running it.
00:17:56
Speaker
where with the data, with the website, it's very much designed to be a kind of data journalism exercise. Let's take the big events of the year and show what happened in search. This year we went to the same kind of thing but with an interactive visualization where you see these big moments through the lens of search over the year and that is an aggregated thing where we're taking 2016 as a whole.
00:18:19
Speaker
At the same time, I think there is something about the dynamism of search. We have the attention span of goldfish as human beings, right? But when we care about something, we really, really care about it. So something happened like the election results happen. We really, really care about it when it's happened. And then they say we've moved back on to Kim Kardashian or whatever it might be. I mean, we're very fickle and eclectic people.
00:18:42
Speaker
Moving on to the next thing right away. When you are thinking about telling stories and pulling all this data together, because you have so much data and there's so much that you could provide to people for them to dive into, are you immediately thinking about interactivity? Is that your first instinct as it go interactive or are you going back and forth? Not always. I guess there's somebody running a data transfer team.
00:19:10
Speaker
So the toolkit, you know, the data toolkit, what's in the WordPress? So sometimes it's working with a partner, like working, you know, we just did this election land project with Popapuco and, you know, we were part of this big coalition of news organizations where we were reporting in real time on voting issues around the country on election day.
Balancing Detailed Visualizations and Impactful Stories
00:19:31
Speaker
It was amazing. It's literally one of the best date journalism projects I've ever been involved with. And it's powerful because it was very, yeah, everybody working together on this big thing. And
00:19:41
Speaker
But sometimes the output such as it is, in our case, was a couple of things. It was, you know, we had a real time map, you could see, you know, voting problems in real time. But we also had, you know, that we were part of this bigger system where people were sending tips and people were using the system called chat, which is kind of amazing to send tips through to local reporters. And so your output becomes one of a number of things. And I think increasingly, so I think that with in terms of data visualization, you know, bigger projects take more time.
00:20:10
Speaker
And we've had a few projects and we work with amazing people from pitch and track to Wes Grubbs' team pitch and track to them. Accurate, I mentioned before, and then we've got a big project coming out in a week's time with Morris Tafana, which is going to be beautiful and amazing, and it's taking
00:20:32
Speaker
searches for food on Google, or back to 2004, which is kind of an amazing thing to see how people's search for food have changed over time. Things like that, I feel you need to give them the love that they need and do them properly. And so they take longer, they take a few months, and that's fine, I'm fine with that.
00:20:50
Speaker
I think that's really, I think that's really important. But I also think there is some way for instant data shellers to be out there when a big event happens. So we have this little toolkit of quick instant tools as well. We can use Visualize something and get it out there. But often we find, often just like the right spike at the right time is what gets picked up and that's what. That's what people will pick up. Those are the things that go viral.
00:21:17
Speaker
Yeah. Very good. Well, I think we're out of time, but Simon, thanks so much for coming on the show. It's been great to see you again. Thanks for having me, John. Sweet to see you soon. Yes. Thanks to everyone for tuning into this week's episode. Have a great holiday season. So until next time, this has been the Policy Vis podcast. Thanks for listening.
00:21:44
Speaker
This episode of the PolicyViz podcast is brought to you by JMP, Statistical Discovery Software from SAS. JMP, spelled J-M-P, is an easy to use tool that connects powerful analytics with interactive graphics. The drag and drop interface of JMP enables quick exploration of data to identify patterns, interactions, and outliers.
00:22:04
Speaker
JUMP has a scripting language for reproducibility and interfacing with R. Click on this episode's sponsored link to receive a free info kit that includes an interview with DataVis experts Kaiser Fung and Alberto Cairo. In the interview, they discuss information gathering, analysis, and communicating results.