This is what my wife calls my Bible salesman photo.
Yeah.
Accurate, yes?
Yeah.
No lies detected.
So I've got a little presentation here I want to walk you through
that I think I'm able to click my way to.
No, wait.
I'm clicking the wrong slides.
My friend's in the back.
Can we flip?
Anyone else take the Acela up this morning?
I was on the 5 o'clock from Penn Station.
So if I get a little delirious, that's why.
All right.
We're waiting for the deck.
We're going to take questions at the end.
And you can ask me about my presentation, really,
anything you want to know about Reuters News.
I work in Times Square every single day, five days a week.
And there you find the New York newsroom of Reuters News,
which I think you probably have some familiarity with,
one of the world's largest news organizations with people all around it.
And I'm here to introduce you to a new project that we are,
I think, publicly speaking about for the first time today at this event.
And we'll launch in the coming weeks.
This is kind of a sneak peek.
And we call it the Reuters Climate Monitor.
So what is that?
I'm going to talk you through who I am,
kind of what my team does,
and then this new approach we're taking towards climate data as we go.
Please put your questions in.
All right.
So I'm Ben Welsh.
I'm from Iowa.
Any other Hawkeyes here?
All right.
Any other Caitlin Clark fans here?
Can I go for that?
Yeah, okay.
And I've been working at Reuters in New York for three years now,
and I'm one of these nerds in the newsroom.
I'm a reporter, an editor, and a computer programmer.
And I lead a specialized team that works on automating data
to cover breaking news for our service.
What does that mean?
Well, I'll give you an example.
So our goal is really to build off these types of data applications
that have become part of news,
but you might not always think of as software.
These are the live election results we look at on election night, right?
I bet people in this room spent a lot of time
refreshing their local coronavirus tracker, you know,
when we were in the middle of the pandemic.
And those data apps have become kind of almost by accident
some of the most successful things that news organizations publish
on their dot-com websites and in their apps.
And what teams like mine at all the different news organizations
really around the world have tried to do in the last few years
is kind of retool and create miniature assembly lines
inside of our information factories
that kind of manufacture these types of products, right?
And they're really just automated software systems,
data pipelines that go to your local election office, right?
Gather the results, bring them back, clean them up,
and then put them up on a beautiful map
before your eyes, right?
You're familiar with this.
And that approach works not just for elections,
but really for all kinds of different stuff.
And so my group is this new little assembly line inside Reuters
trying to manufacture as many of those widgets as we can.
And so like an example that is on our homepage
like five or six times a week
is we have all these automations
that go get the latest economics and financial data
from like a kind of business terminal type situation
or a government website.
And it doesn't just bring them down
and allow us to analyze them.
It goes all the way through out into live charts
that go up onto our website, right?
So we've got hundreds and hundreds
of these breaking news automations
that help us cover markets, economics, public opinion,
other things.
And that's been a success for us.
And so we're trying to,
and so we try to aim for the biggest stories
so that when we invest in these automations,
we can get continual use of them
and really cover the top stuff.
So the last few weeks, a lot of oil prices charts, right?
I've been sitting on our homepage
coming out of these automations.
And we're looking to expand that approach
beyond just stuff having to do with money
and markets and that kind of thing
into other major breaking news events,
which of course extreme weather is a great example of, right?
And so last year we rolled out a new hurricane tracking system
that allows us to watch cyclones
not just around Florida in the United States
but really in Asia and elsewhere in the world
so that we're able to cover those events
kind of as they happen in this same style.
And our next goal and what we've been working on
is bringing that approach to climate
and to the stuff this conference is about.
So this guy right here, this is my boss.
This is Simon Robinson.
Don't tell him I'm doing this.
And he's the executive editor of Reuters in London.
And he came to me with a challenge
which was could our newsroom use data
and this approach I've been telling you about
to cover the day-to-day kind of impact of climate change
because he really saw a kind of gap in our coverage.
If you think about you have a major heat wave
in the Mediterranean last summer
and for us at Reuters that's a big story
that's going to dominate the top of the file,
the homepage every day for maybe even a week, right?
But in that coverage we only have
the kind of traditional weather and temperature data
and it just seemed to him that there's this kind of gap
where the context of climate
is kind of missing from those major storylines.
And so his challenge was how could we try to address that
or could we try to address that?
And it was the first time I really thought about it
and, you know, I definitely want feedback from you all
on how we're doing as we go through this
because you know more about it than me.
But it seemed to me that one of the problems was
is that unlike the stock market data
or the oil prices data,
which updates every second, right,
almost around the clock, right,
a lot of climate data is a little slower moving, right,
where it might tell you,
well, the Earth's temperature was this last month
or last year, not today or tomorrow, right?
And a lot of climate data,
for very good scientific reasons,
is measured in these kind of arcane statistics
that aren't how the average person thinks about the weather.
The average temperature is a great example, right,
where the most climate goals, as I understand it,
are set using the average temperature,
but if I asked any of you,
what's the average temperature in Boston today,
you probably never even thought about it, right?
People think in highs and lows.
And so my feeling was that if we're going to address this,
we had to take those things on.
And so I went out to kind of,
on a little reporting trip to see,
could we get faster climate data
that is in more relatable statistics
and that isn't just entirely global either,
but maybe can relate to people like where they live,
like the weather report does.
And so we surveyed what everybody else did,
I interviewed a bunch of nerdy nerds,
and we found the answer is yes,
it actually might be possible to pull off such a thing.
And if you guys want to get into all the details
of the era five data set and all that stuff,
I'm happy to go down the road,
but we kind of came up with a rough methodology
that we think will allow us just getting started
in our first steps into this coverage
to create a daily dashboard
that automatically compares today's temperature,
tomorrow's temperature,
against the historic record,
against what used to be normal
before widespread global climate change
for Boston, for Swisher, Iowa,
where I grew up,
for anywhere on earth, right?
And then deliver a comprehensive view
of today's, how weird today's temperature is, right?
The thing we all ask ourselves
when we're close to one of these events
by measuring what people call an anomaly,
just the difference between today
and what used to be normal.
And we've created and we're preparing to launch
a color-coded world map
that will update each day and highlight right now
where the most significant temperature anomalies are on earth,
the interactive controls will allow you
to search your hometown, where your mom lives,
wherever you'd like to go,
whatever you're interested in,
and find out the differences there
in the same way that the weather report can.
We're calling it the Reuters Climate Monitor,
and we have a prototype
that this is the screenshot of
that we're preparing to launch.
And we were going to try maybe
to open up the webpage in the back,
and this is just a quick look at the demo page,
you know, where you're able to see
today's kind of forecast,
the red areas are higher,
the blue are lower,
and you'd be able to search anywhere you wanted
and zoom in and get a report, right?
We can jump back.
And that's just the beginning, right?
So after we get this up and out,
we have immediate plans to follow on
with stuff that we hope will make it more newsy.
We're going to try to calculate the number of people
living in extreme conditions every day,
today, tomorrow, the day after,
so that we're able to roll up
those summaries of how many million people, etc.,
into our coverage.
And that, to me, is delivering that type of context
that Simon thought was missing from those reports.
So on the very day that we're writing about,
an unusual temperature event somewhere,
we're able to get that context automatically in, right?
We're going to try to forecast it out
further into the future,
and we're going to use this analysis,
we actually currently are using this analysis
to kind of fling correspondence around the world
to areas where we've seen significant climate change
already bubbling up out of this data
over the last 30, 40 years,
and report on the impacts of that.
And so it's going to serve as a reporting tool
for enterprise as well.
I want to hear from you.
So this is just the very beginning.
It might seem kind of basic to you guys.
I don't know.
I'm curious to learn.
But in this room with experts,
I would love to know answers to questions like these
and how you think a news organization like Reuters
could and should or ought to better use data
to cover climate change.
Thank you.
Thank you.
Thank you.
Thank you.
Thank you.
Thank you.