Giorgia Lupi: The New Aesthetic of Data Narrative

Giorgia Lupi: The New Aesthetic of Data Narrative

August 14, 2019 0 By Stanley Isaacs


– What I’m gonna do today
is trying to explore with you what we can do with data visualization aesthetics
when we have to compose rich visual narrative
in static images mostly, but also the difference
regarding this topic when we have the possibility to build interactive data visualization. And, yeah, I’ll talk about the work that we’ve been doing in Accurat which is as Lisa was introducing, the company, the
information design company that I co-founded two years ago. And our main office is in Milan but now we opened an office in New York, still a smaller place mark but we are working on making it bigger. So basically just to give
you a bit of introduction if you guys don’t know, is we actually gather
data and analyze data and we design data visualization
and visual narratives but also we design and develop analytical and interactive tools like web or mobile
interfaces and applications to access big system
of information through. And in the company we have
very different backgrounds and we really rely on that in how we work in our projects. So far we are around 20 people, interaction designers, graphic designers, but also developers, in content curators. And as you might see from
this kind of composition, really the core is designing
visual storytelling with data. And starting to defining a little bit what we think of our work in a few words I would say that mostly we try to act as a visual bridge between the data and the people who cannot
really dig into the data. And we try to help them understand shaping the ways, the visual ways that people access this information. But today I will basically
focus on two projects that specifically uses data visualization to convey stories. The first one is a data
journalistic projects that we’ve been doing for
more than a year so far for the Sunday cultural
supplement of Corriere della Sera which is the main Italian newspaper. And we regularly publish on the newspaper. And as you can see from these previews they are quite rich in
compound visual narratives and I have to tell you that they’re done pretty much manually, so really passing from spreadsheet to Adobe Illustrator
in this kind of thing. And the second project
that I will show you is an interactive one and then we’ll see a little bit about the differences, it’s visual digital access
to the crunch based database which is a public database that collects information on the
startups around the world. And I’ll show you really the difference in designing this visual when it comes to static and interactive. But let’s get started with
the old fashion printed one. So La Lettura is how
the supplement is called and it can be translated in English like the very act of reading, so spending time reading. And what we do here is that we every time choose a topic that we wanna explore, sometimes it’s just a
fascination that we have or some other time it’s just like data sets that we find, we combine the data
sets and we try to find the most interesting
way to tell the stories. And purposely here we aim at composing rich visual narratives, maintaining the informative
richness of the data but still making this
richness more accessible through visualization. And we call what we do here, and then I’ll dig a little
bit in some of the projects. We call this kind of storytelling as a multi-layer storytelling with a first story that should be visually very clear at a first glance, but then that can also lead readers to get like in depth in finding
some tangential stories. And I think that for us here the very challenge is to think about the data as journalists
rather than analysts, and so really asking ourselves what’s interesting about this number and what if I match it
out with something else, and how can I fit several
stories in just one page? And visually speaking,
as you might have seen from this preview is also we really try to build customized
visual models for our pieces. So experimenting a little bit with non-common standards, visual metaphor for the data that we are analyzing. Okay, I’ll present two pieces
that we did for La Lettura. In this one the phenomena
of the so-called brain drain is explored through a visualization that is displaying the
main income and outcome of researchers floor for 16 countries, and discovering coloration
with article meters. And you have to imagine that these pieces are really meant to be printed that big, and so people can really
physically dig into the data. So I hope that you guys
can get the details, but in case they are online, just like follow me on how we build it and why we did some design choices. So to give you a bit of a context. Here the countries which
are these compound element that you see are positioned
within the main architecture or the visualization
according to two factors, so horizontally the GDP that each country dedicates to the research
and development field and vertically the number of researchers per million people. So the very first story, the very first point of
view on the data set, you have here with the
positioning of the countries. And you can really just like see how countries are positioned in different ways and so performing in different way. But then each country is really displaying by contrasting lots of
parameters and information that helps discover it’s to
all situation in terms of how many researchers go abroad which is the solid red
histogram going down, and how many entered the country which is the blue solid line going up; and also how many are coming
back after a period abroad which is the orange small line that is going up again
compared to the red one. But there is not enough
information to understand what’s happening in this country. So we also chose to include data about the regular population
emigration and immigration which are the hollowed
red and blue histogram very close to the researchers
emigration and immigration, and also the GDP per capital
which is the light bubble dimension behind the name of the country, and also the main countries where researchers come from and go into; the main university rankings
which is the greed dot up above and the unemployment rate in the country which is the yellow square dimension. And the last one that
we find interesting is the female employment rate
which is the pink line. So this is a very rich narrative, very fluid information. But we did include all this information to provide some possible correlations. And all of the things that you see, all of the data that
you see are percentages so then you can really
visually compare the trends and of the value per se. And coming back to our job as
a visual designer in this way of course this is not the definitive and ultimate point of view on the global brain drain phenomena. It’s just one possible
interpretation of it, it’s one possible view on the topic, and we hope that readers
can really find their interpretation on that. And visually speaking here
we don’t have big data. We cannot really talk about big data. But still we have lots of different kinds of information we correlated. And also to us that we analyze the data, really the patterns emerge
just once visualized. As an example we can
see from the piece that in general the researchers move around much more than the average person. In fact for almost each country the red and blue solid histogram which are the researchers are
longer than the hollowed ones. We see that we have some exceptions in leading countries like Spain and Italy. In fact it seems to be
important proportionally more regular worker so say with lower level of education
rather than researchers. And we cal also see that the countries that attract many researchers, so the filled blue bars are also normally the one that
attract more foreigners. And so now we start
looking at the situation and asking ourself why. It’s not be so much a question of GDP per capital which
still is the light bubble behind any of the country. The presence, I’m sorry, on top university which is the green dot is important but not essential. The best countries seems really to be the ones with a high
female employment rate and also if you see from the name of the country maybe the country where people normally speak English like Canada, Great Britain and the US. Another peculiarity is that the countries that drive out the researchers the most which are the ones I highlighted here with a long red solid histogram are not necessarily the one afflicted by economical crisis such as high unemployment or low incomes. As an example you can
see that Switzerland, the Netherlands and Belgium, they export many researchers even if they have a very
low unemployment rate which is the yellow square dimension in a very high average income. But here we’re back
with the overall again. And again of course this
is an incredible rich visual piece, full of information. And as a reader you are supposed to spend time getting that and finding your own stories, the stories that you want to understand. And the visual design choices that we made should help to find
potential correlations. And of course the aim of producing a visual piece of this kind is to convey effective way to understand correlation and phenomena and we have to be very straightforward in that when we are representing data. But we also, every know
how it is important also to try to produce beautiful pieces and expect that aesthetic to try to catch reader highs and say,
and make readers say, “Oh, this is beautiful. “I wanna understand that.” And this is true more
over today with the world very full of infographic
and data visualization. And so how can you make
sure that your pieces are in a way remembered? I don’t have a kind of universal rule, but we have our personal
method which is basically getting visual inspirations not from actual data visualization
but from whatever else. As an example the visual idea behind the brain drain piece
that you’ve seen before actually came to me after a visit to the MOMA Inventing
Abstraction exhibition which happened last year. It was very fascinating. And this visit happened
actually during the first day that we were analyzing the data on the researchers and the country. And during the visit at MOMA I was really, and I have to be honest, I was really wishing to come up with a data visualization
able to replicate the geometrical feeling
and the pleasing aesthetics and primary color that
I was passing through. And then each country
started immediately to appear to me as a kind of compound element with parameters displayed
as very geometrical shapes. I was sketching down during the visit. I also think that is interesting. When you see some images
that you really like to kind of start to redraw so that you can start to convey them your personal take on that. And so, yeah, these are
just like the first sketches exploring the parameters related to the country in
this kind of geometrical way. And just to show you also
from the very beginning to how we shape the data visualization, here in this stage I was
just trying to understand how this inspiration could help in deciding how to represent
the parameters of the country. Of course according to
the analysis that we did and what we wanted to put in evidence for this particular story. And, yeah, here is the stage where elements start to
have been done digitally and have their shape. And really I wanted to
keep this experiment with primary colors as
Mondrion was teaching a lot. And here we are with the final one. And I guess you can totally spot how this kind of modern painting, visual inspiration played
its role on the piece. This is another visualization
actually the one that Liz was talking about
featured in the magazine. And it explores Nobel Prizes and laureates from 1901 to 2012, so the last round of Nobel Prizes in 2013 is not included unfortunately. I’ll just spend a little bit on that. And then I’ll go to see
sites and inspiration even for this piece. So we visualize the prize
categories along these timelines. With this kind of parallel squares, with the six main rows
highlighted by colors; and we have red for chemistry, blue for economic science,
green for physics, yellow for literature,
purple for medicine, and orange for peace if you cannot read it on the back of the room. And so each dot that you see here represent a Nobel Laureate, and each guy is positioned
according to the year the prize was awarded of
course in the timeline, and also his or her age
at the time of the award which are the vertical
positioning within each category. So in a way highlighting is a first story how old should you be to win the prize. And also for each category we draw a line indicating the average
age for the category which is the colored one, and also the average age for
the total of the laureate which is always the black dashed one, so that you can really
compare the categories among them all but still one to the other and really understand how they perform within the total of the Nobel Laureates. And then we use the main
axis of the visualization that will be already
provided with the first story to provide some aggregated information. And the arcs that you can see here represent the principal
university of affiliation of Nobel per each category, and it’s interesting that
chemistry, physics and medicine, which are red, green and purple, are spread among the
seven major university while literature and peace Nobel Prizes which are the yellow and orange quite didn’t attend the top university. And you can really see it once visualized. Imagine to have all of
this data in a spreadsheet, it’s really, really impractical
to find patterns on that. And also the bar charts here
within the two parenthesis represent the average
grade level per category, so if PhD or not even degreed. And you can learn that, if you want a Nobel in
medicine, economics or physics well you should seriously think about PhD, but you can try without that
for literature and peace which are the yellow and orange one, which you can see that
had lots of laureates that are not even degreed. And the double rounded
dot that you see here represent the women out of the total which are not so many. And I think that design
wise this is important. So we didn’t really differentiate the men and the women with two different symbols. We gave for granted that all the guys without a double symbol they are men and we only highlighted the women, which if you have to
present lots of information you have to be able to understand which ones could be in a way visually cut out and which
one still makes sense. And so this part below represent the most frequent
hometowns out of the total. Of course still using the timeline of the main visualization
and just aggregating 30 years of information. And so it’s interesting that lately if you’re born in the US, you are more likely to catch the prize, while at the beginning of the century the most of the scene was European. And we also have some, let’s
say, curiosities highlighted, so some primates in particular, relative information that we found. So some very personal story
that we wanted to highlight with the text like Marie Curie that she won two Nobel prizes, or the oldest winner that was 90 and the youngest that was 24 and so on. And of course we recalled the numbers within the visualization
to really point out where and when this happened. And this is the how-to read it part. When we use, again, the
visualization itself to the visualization may shape as a base to give readers the information about how to pay around. And this is very important like we had a key or religion when you are
trying to convey information in novel visual ways. And so it is the
visualization itself even here customized for the data that we analyze that can help spot patterns, some of them we’ve already seen before. But in general we can also say that lately Nobel prizes are older if
compared to the decks before. Or also we have concentration
of women in medicine and peace particularly which are the two rows at the bottom. And also this is something
that always surprise me. There is maybe expected gap of prizes during the second World War. And this is pretty visible. And, you know, we could imagine that. But when you realize it visually isn’t it stronger? In coming to visual inspiration again, even here it wasn’t
really data visualization that inspired us and then inspired me. Just like by myself always very visually fascinated about from the musical scores and
their elegant aesthetics. And many, many time I
would just like find myself drawing and replicating this kind of musical like
shapes with no purpose at all. And also I really visually
love the so called graphic music notation
which is the contemporary music notation that uses
non-traditional symbols and colors to convey
information about a performance of a piece of music and express what and how it should be played. And John Cage is a famous
contemporary composer, and his visual exploration
on contemporary score are really fascinating. And I think that I don’t
need to have anything more. Can you spot similarities
with the visualization that I was showing before in this kind of visual music notation? And again just to go from
the inspiration to the piece, these were the first sketches when I was trying to
simply follow the idea of building those parallel scores, helping highlighting some differences that we noticed within the data. And the visualization
was pretty clear to me, the idea of really using the main timeline of the visualization to then provide aggregated information
per category and per time. And these are intermediate stages when we started doing it digitally. And actually for this piece lots of people asked us why
we rotated the visualizations. So why it is turned a
little bit like on a slope. And to be honest the lack of a space of the R board at the very beginning played its role on the choice when we were trying to fit
all of the data into a page. But then when we tried
this kind of rotation while fitting everything to the art board, so isn’t it just like
more incredibly elegant? I could never know, I
couldn’t even tell why. But it’s more elegant, it’s more like giving the sense of evolution, and so we were like, “That’s
okay, let’s keep it like that.” and here we are back again with the whole structure
of the piece finalized. Let’s try to learn something from that. As I was introducing before we call on the pieces
that you have seen here, multi-layered storytelling. And of course to build
effective pieces of this kind everything has to fold around
the concept of layering, establishing hierarchies and making sure that these hierarchies are clear. And this is the case for us for both the data analysis and
the visual composition. And I’ve tried to explain
how we normally do for this paper I’ve been asked to write for the Parsons Journal
for Information Mapping. In this article called
Non-linear Storytelling: Journalism Through
info-spatial Composition. I didn’t choose the title
but it’s nice anyway. And it is basically the section of our pieces from how they start to how they evolve in terms
of visual composition. And we just try to
derive general principles and some intermediate
stages that we could name. And I won’t really go in depth on that. You can find the article online. But if you wanna somehow
group the whole process of ours in point we
could explain them like composing the main architecture
of the visualization. So we chart the main
principle of the spatial organization of the story. And then positioning
each singular elements within the main framework. So each singular data point find its location within the piece. And then now each element assumes a shape, a dimension and a feature according to the characteristic
that we wanna highlight. If we happen to find
interesting relationship or groups within the data, once we visualize the basic data point we might need to elucidate
these internal relationships. And then we start labeling
and identifying elements, see if we need to have some text. And then very important, supplementing the greater story through the addition of secondary stories, the other correlation
that we want to include, the added background information that we wanna have to the story. And of course all of these
are just like schematization. There are no data behind it. Everything is fake. It’s just like abstract the process of building of the piece in the slides. And then we of course
provide visual explanation such as legend or a key. And at the very end we
fine tune and stylize element shapes, color and weight to really make sure that
the hierarchies can pop up. And little things can
really make a difference. So just like labeling a legend or fine-tuning of strokes,
lines, opacity of elements, something that may be people
don’t even notice really. But it’s very fundamental with
so many information layers. Of course the process is not that linear as it could seem from this point. It’s a constant iteration of exploration. And we again, we really make no claim to have settled any universal method. It’s just ours derived from
our different backgrounds and the attitudes. And in a way it’s our
method to try to reach interactivity even in static pieces. But what if we have the possibility to really work with
interaction, how things changes. As I was introducing, this one is a visual access to the
crunch-based database which is a public database that collects information on startups
all around the world. This tool that we designed is online and available so all of
you can like play with it. And we design it with
the aim of highlighting relationships in terms of time and money among venture capitalist
startups and founders. Here is a video of the navigation, yeah. Actually you can either enter a query on the upper part of the visualization if you already know what
you are looking for, or you can browse startups from the menu. And then you jump to the main
scene of the visualization. When you can see how
much money each startup earn per each round which is the size of the internal circle, and the total of the money which is the size of the whole circles, and then jump in to seeing
which venture capitalist put money on that in understanding which other startups the
venture capitalist founded. And when you find some guys that have really founded lots of startups, you can also like act on the timeline and really, like,
narrowing down the period in order to be able to bare and compare what’s happening in this kind of period. And so the idea is to
have this kind of view when you can easily, easily jump from venture capitalist
startups and founders and explore the relationship. But also as you can see you always have the information on the
crunch-based database and the links to access their website and their information on the bottom. And so it is really a visual
access to the information, the more like quantity
information that you can have on the website. And so this was very quick but of course static pieces and
interactive visualization are very different in terms of how not only in terms of how many information, and so the amount of information that you can display at once, but also because of the fact that with interactive pieces we as designers we could really try to let people explore and find their
own path through data without really having
to pick a point of view, I mean without really
having to act as a curator. And so wrapping up what
I went through before and starting to conclude
this kind of exploration, I really like this definition
that Moritz Stefaner, which is a very brilliant data visualizer, have about data visualization. So good visualization not
only answer questions, they generate new questions. They make you think and concern. Good visualization should tell stories, thousand stories able to
provide, if done good, multiple layers of exploration. And I know that I showed some compound and complex pieces today, but talking about complexity which I think it’s an important point to make. Whenever the main purpose of the visualization is
to open reader’s eyes to new knowledge to reveal
something new about the world, or also to engage the audience, entertain the audience about the topic, it’s really kind of impractical to avoid certain levels of visual complexity because actually the world
is complex, it’s compound. We every day run into lots of information that we can combine in endless way. And therefore catching new points of view, in discovering something
that you didn’t know before often cannot really happen at a glance. And this kind of process
of knowing and revelation and all of the aspect of our lives require an in depth
investigation of the context. So we like to think data visualization like visual way to convey the richness and involvement and feelings being like engagement and concerns or
whatever kind of information that we experience in our everyday life on the story that we run into rather than a simplification of the words. And, yeah, talking a little bit more about how we design,
how we shape these ways. These are just pieces
starting from the sketches and becoming actual data visualization so that you just like
enjoy some comparison what I’m trying to conclude in my talk. So through this visual exploration we really constantly try to create harmonious composition, maintaining and respecting the complexity of the
data that we’re analyzing and those building
customized visual models as I call them customized. And possibly also different
experimental of the time. And I think that we do know that there is a science
through data visualization or at least that
recognize principles about how to represent information exist, and I’d say our work will be
pursued in many, many times. So really many times regular bar charts, counter plots, timelines and maps are the best way to convey information. But to us it simply doesn’t mean that this is an end that everything is already settled and concluded and there is no room for experimenting. And this is really the exploratory goal that we have behind all of these projects. In a way we believe that keeping on exploring the
realm of possibility, even passing through failures and mistakes and weird composition can also lead up to refining and perfecting the core of this kind of field in science. We are just like testing and exploring how to push forward and create new visual language. And also I know that is a very high aim but we also hope that we
can educate reader’s eyes, educate is a bad word in English I think but make reader’s eyes more comfortable with new aesthetics in visual languages and visual conventions,
new visual conventions. And so in a way successful
aesthetic design should on data visualization
should balance conventions in so familiar forms that people are already comfortable with and novelty. Novelty like imaginative visual, able to attract readers
into the exploration because they see something that
I haven’t seen before. And when you try to have something new or picking inspiration
from different field I think that we as designers, you as designer you have to remember how our brain works in terms
of like finding meaning, towards finding meaning
they already know in things, in this case visual meanings
and visual conventions. And everything is about metaphor I think. And if we really want to
explore and experiment and risk we have to build and
frame in a way our pieces around something and relying on something that has been experienced before, being something like, okay, I can experiment but I can provide basically out of data visualization with regular timelines with kind of grid that people
are already familiar with, something able to transform the strange into the known. I think that basically the
question here right now and this nowadays the world
is full of data visualization, the question to (mumbles)
is how far can we go. And I don’t know but we try to find ways to get there in a way. And I think that’s it. I don’t know if it was too
short or was too quick. This was the basic idea of today. I also have here my partner at Accurat which is Gabriel right there. So if you wanna ask questions to us. Thanks. Thank you. (applauding) – [Participant] What software do you use to create the visual designs? – Actually the first one that you saw are really created manually passing from spreadsheet like Excel to Adobe Illustrator,
very, very manually because I mean we have developers in our company, and I’m not a technological person. I cannot code at all so
I’m just like the designer. For the startup universe, and so the second piece that I show, the second pose of course we use these three and we
like coded and programmed. But all of the pieces that you saw at the beginning are done very manually, and this is basically
because we really want to customize the visual models, and so not starting from the standards, the tools or already codified languages can provide us with. But in this experimental project we really wanna do, yeah, explore. And also I think that I didn’t mention it but the visualization
for Corriere della Sera we do it in a very short time span, like imagine that from the
very beginning to the end, like from the data gathering it passes something like
six days and not more. I mean sometimes we don’t
even have the time to program. We just have to say, that’s okay. We want a visual like this, like that, and we do in Illustrator. So, yeah. – [Participant] I guess I’m curious how you might think of your process
versus a magazine article whereas an article you have a journalist who’s doing research and
writing up a first draft, but then an editor comes in and helps her shape the
story that’s being told. Do you have an editor who’s coming in and sort of looking at
your graphic and saying that data point doesn’t
really help tell the story or helping you understand what
the original data should be? – Sure, this is an interesting question. Actually the other partner
that we have in the company he’s a sociologist. So in a way he has a
kind of more humanistic, he’s not a journalist but he has this kind of background
in picking the right data, picking the right criteria through which display the data. But we have a publisher which sometimes act like an editor. I think that the main, let’s say, tricky part with them is to find data set that they can recognize
as institutionalized. So for our publisher, as an example, even if we find very interesting to add a layer of information
say coming from Wikipedia or from popular choices
like blog and forums we cannot do that because they are the main Italian newspaper and they cannot rely on this data. So I think that mostly our
publisher act as sensory in a way like, you know, and not really helping us in saying, oh but you could also have
this kind of data set. We do it by ourself and we know that we are not journalists and so we are not pretending to have, as I was saying before, the ultimate point of view on a phenomena. Actually the visualization that you see, this kind of visualization
are always like, when you see the
visualization on the newspaper you’ll always have an article written by a journalist
interpreting the visual. So basically it’s not just
the visualization itself but it is a visualization plus an article. So maybe if we miss something as just a visual designer, sometimes the article could provide, yeah, help on that. Basically it’s just finding out which are the parameters that we wanna add to the story and how to compare them. And this is how we work. That’s okay. Another question? – [Participant] I was
wondering how you come up with your story in the first place. You mentioned in your process you lay out the main framework and you decide where the
elements go inside that story and then you start laying
on the secondary ones. But do you just start asking questions and seeing what comes out of the data? – It really depends. Sometimes the bigger part is
really to analyze the data. And so we have some guys in the office, I’m not analyzing the data, we have our data analyst and someone who is a sociologist that start in combining information to the spreadsheet and really manually
looking if we could spot some interesting patterns
or something that are worth to tell the main stories. And then sometimes it happen like that, and then we build the visual models according to the thing that
we already know in data. Some other times, and this is interesting, we can have a fascination
about how to visualize a topic even without having the data yet. So really something like why don’t we visualize this like this? And it’s just like imagining
that we can find some data, imagining that we can
find a story in the data but sometimes the visual highlighting come even before the actual data analysis. And of course I think that this is usually a jumping point to the story. And then when we find the data we can also come up
finding that the partners are not the one that we expected, and then we change the visual models. But I think that it
really, really depends. And sometimes the main story is something that is really in the data, so you say that’s okay. This is something that I have to represent like in the Nobel prizes. Of course we have picked whatever first visual information and first story. But we decided to choose
the age of the laureates as the main jumping point. So, yeah, sometimes the
story is in the data. Sometimes the visualization is a representation of a phenomena where people can then look for patterns. And it really, really varies for us. And I think that it also, it’s so variable and also because we really have a very short time span. So every time we have to figure out how to make this in six days. So if we have a visual
idea, we pursue this idea. If we have a data set we try
to start from the data set. So the short time span I think is an interesting constrain because you have to get something
delivered for that day. I know that I’m going
also a little bit further from the original question, but also I think that the constraints like the graphic constraints, and you can see this very pink background and the fixed artwork, and just the fact that
you can use one font. But it’s still very interesting
because you can then just focus on how to
compose the visual piece and you’re not distracted
about how to compose the layout, which kind of fonts to use. And I think that also gives the overall of the visualization
this kind of aesthetic of being a series in a way. I don’t know if I answered the question in the first place. – [Participant] Hi. In my job we tend to struggle a bit with the balance between explanation and exploration. I’m wondering what your
thoughts are on this and how do you approach it, especially for the more
in depth interactive work? – Sure. Yeah, you’re totally right. I think that my point today, I don’t know if it was so
clear at the beginning, but was really to show
you how we can explore and experiment. So this was the reason why I chose to really show this exploratory project. As a company we do lots of other more straightforward visuals that, it depends from the aim. I mean, when you have to produce a visual that has to be useful for
decision making purposes or something that has to be
in a way very scientific, I think that the flexibility and the possibility to
explore is a little bit less. In this project that is, like, a column called visual data on a Sunday cultural
supplement of a newspaper that is meant to be a
long read for people, it’s mandatory like for
us, from our publisher, to really explore. I think that the balance between exploration and explanation depends on the goal of the project, and the goal of the project at hand, the goal of the client, and the kind of of
information that the readers or the users are expecting to see, and the kind of story that
they are expecting to see. I think that the there
is no single answer. It really depends on your goals, or the goals of a specific project. What do you think? – [Participant] Our goals are
different for every target. – Likewise. Exactly. – [Participant] But
sometimes there is maybe an implied pressure for
people to get like that, which is, I know you were
talking about that earlier. – Yeah. – [Participant] If it’s
too much exploration you might feel overwhelmed. But if you explain too
much you might actually– – I know. I know and this is why we try to layer our visual so that eventually hopefully there’s something
that you can get at a glance like the topic or something
that you as a reader are particularly interested to explore. And then if you have
time you can go in depth. And I think that it’s interesting here sentence that sometimes
I use from Sean Carter which is the designer
of the New York Times, and it’s very interesting because he talks about designing for Bart Simpson and Lisa Simpson of the world. So when the Bart Simpson is the guy that just wanna have a
quick look, a quick fix and then walk away and flip away. But then on the same piece
you may also encounter a Lisa Simpson that wanna stay there and understand everything
and get into details. And so I think that this
is an interesting way of thinking about your audience because with a singular
piece you can really give this kind of piece
to people that have different willingness to
understand and to explore. I think there is no, a single answer to your question. I don’t know, Gabriel, if
you wanna add anything? Not really? Okay. – [Participant] What is
your mixture of clients? Is it really just for the
more magazine journalists or you have like just
regular corporate clients– – Yeah, we have very different clients. Like we work with the financial sector, and so really people that have
to visualize financial data. We work with lots of banks, both from their communication and so like visualizing
data for their clients or their public maybe to show their philosophy of saving and something, but also for their internal scopes. Like we also build analytical
visual tools for them to get better understanding of
internal dynamics of people, internal dynamics of data. And so basically for the financial sector we are doing this kind of job. Then we work for lots of foundation, and also libraries like the
New York City Public Library as an example, so an institution that have more qualitative data,
so data based on text. For them we are now
visualizing a very compelling data set on the correspondences between the founding fathers of the US. And also this kind of more
cultural institutions. We’ve been working for the
health care system in Italy, also designing visual interfaces that was able in a more visual way to convey, not really to convey
information but to make patients, relatives and different kind of doctors talk about
the same medical record. And so our work there was to start from the already existing
digital medical record which are basically very
complicated right now in their interfaces, and just making the
interfaces more intuitive for different kind of users. And so the interface for the patient would be different rather than the one for the client, for the doctor
and the relative of course. And, yeah, I think that we work with big
consultancies to help them, consultancy company helping them shaping the way they communicate with their clients about the clients data. It really varies. And I think it’s
interesting because we can really crosswise the field and work with expert in the field which is really very interesting to us because every time we learn from them. And we just like act as
the information designer which is our role. – [Participant] And you
opened the New York office ’cause you were moving
into more American clients or North American clients? – Yeah, actually it was
the other way around. So basically we wanted to explore if it was possible for us to get, let me be honest. We really wanted to try to see how was the scene in New York because we felt from Italy that, I mean in Italy it’s a very small market and we are kind of like little number of people
doing this kind of job. So from a certain point of view we have whole of the market in Italy. So we are like the top Italian company doing that just because we are basically the only ones. And so we are very happy with Italy because we can do whatever. We can publish with the
main Italian newspaper, imagine that is like the
New York Times like that. But then we really felt that the scene was somewhere else. And so we tried to stay
here for three, six months and really like exploratory mode with no real business strategies or marketing strategies. But then clients and
publication came by themselves. So we didn’t really even
have to look for them. Just the fact of being here and just kind of very
connected environment, and I think the New
York is also particular and very unique. I’ve not been traveling
that much in the US but I think the energy and the, you know, possibility to meet people
and to run into people that are interesting for
the job are super high here. And so then we decided
that we had, of course, to try to open an office here and now we are here. Thank you all. (applauding)