ABOUT THE SPEAKER
Giorgia Lupi - Information designer
Giorgia Lupi sees beauty in data. She challenges the impersonality that data communicate, designing engaging visual narratives that re-connect numbers to what they stand for: stories, people, ideas.

Why you should listen

What sets Giorgia Lupi apart is her humanistic approach to the world of data.

Her work frequently crosses the divide between digital, print and handcrafted representations of information: primarily, she draws with data. She has a passion for and obsession with data, the material she uses to tell stories, and the lens through which she sees the world.

Data are often considered to be very impersonal, boring and clinical, but Lupi's work proves the opposite. She makes sense of data with a curious mind and a heterogeneous arsenal, which ranges from digital technology to exhausting and repetitive manual labor. She believes we will ultimately unlock the full potential of data only when we embrace their nature, and make them part of our lives, which will inevitably make data more human in the process.

Trained as an architect, Lupi has always been driven by opposing forces: analysis and intuition, logic and beauty, numbers and images. True to these dichotomies, in 2011 she started both her own company and studying for a PhD. She earned her ddoctorate in design at Politecnico di Milano, where she focused on information mapping, and she is now the design director and co-founder of Accurat, a global, data-driven research, design and innovation firm with offices in Milan and New York. She relocated from Italy to New York City, where she now lives.

Thanks to her work and research, Giorgia is a prominent voice in the world of data. She has spoken at numerous events, universities and institutions around the world, including the Museum of Modern Art, the Guggenheim Museum, PopTech Conference, Eyeo Festival, Fast Company Innovation by Design, New York University, Columbia University and the New York Public Library. She has been featured in major international outlets such as the New York Times, The Guardian, the Washington Post, NPR, BBC, TIME magazine, National Geographic, Scientific American, Popular Science, Wired, Vogue, Vanity Fair, Monocle and more. Her work has been exhibited at the Design Museum, the Science Museum, and Somerset House in London; the New York Hall of Science and the Storefront for Art and Architecture in New York; at the Triennale Design Museum and the Design Week in Milan, among others.

With her company, Accurat, she has worked with major international clients including IBM, Google, Microsoft, the United Nations, the World Health Organization, the World Economic Forum, the European Union, the Louis Vuitton-Moet-Hennessy Group, Fiat Chrysler Automobiles, J.P. Morgan Asset Management, Unicredit Group and KPMG Advisory.

Giorgia is the co-author of Dear Data, an aspirational hand-drawn data visualization book that explores the more slippery details of daily life through data, revealing the patterns that inform our decisions and affect our relationships.

Her work is part of the permanent collection of the Museum of Modern Art.

More profile about the speaker
Giorgia Lupi | Speaker | TED.com
TEDNYC

Giorgia Lupi: How we can find ourselves in data

乔治亚·卢皮: 如何在数据中找到自己

Filmed:
1,279,894 views

乔治亚·卢皮使用数据来讲述人文故事,为数字增添细枝末节。在这个魅力四射的演讲中,她分享了如何能够将个性融入数据中,如何对我们日常生活中甚至包括琐碎细节的内容进行可视化,如何让抽象和无法量化的信息变得能够被看到、被感知,并直接重新与我们的生活建立联系。
- Information designer
Giorgia Lupi sees beauty in data. She challenges the impersonality that data communicate, designing engaging visual narratives that re-connect numbers to what they stand for: stories, people, ideas. Full bio

Double-click the English transcript below to play the video.

00:12
This is what my last week looked看着 like.
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上周我的生活是这样的。
00:16
What I did,
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我做了什么,
我跟谁在一起,
00:18
who I was with,
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清醒时的每个小时我的
主要感受,等等。
00:20
the main主要 sensations感觉 I had
for every一切 waking醒来 hour小时 ...
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00:24
If the feeling感觉 came来了 as I thought of my dad
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我是否在想起刚去世的爸爸时
产生了这感觉,
00:26
who recently最近 passed通过 away,
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或者我能否绝对避免担忧和焦虑。
00:28
or if I could have just definitely无疑
avoided避免 the worries and anxieties焦虑.
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如果你认为我有点着魔,
00:32
And if you think I'm a little obsessive强迫症,
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你应该是对的。
00:34
you're probably大概 right.
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但是很明显,从这个画面中,
00:36
But clearly明确地, from this visualization可视化,
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你对我的了解会比
从另外的途径了解得多很多,
00:38
you can learn学习 much more about me
than from this other one,
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你可能对这些图片更熟悉,
00:41
which哪一个 are images图片 you're
probably大概 more familiar with
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甚至你的手机里现在就有。
00:44
and which哪一个 you possibly或者 even have
on your phone电话 right now.
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你走路步数的条形图,
00:47
Bar酒吧 charts图表 for the steps脚步 you walked,
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你睡眠质量的饼图,
00:50
pie馅饼 charts图表 for the quality质量
of your sleep睡觉 --
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你晨跑的路线。
00:52
the path路径 of your morning早上 runs运行.
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00:55
In my day job工作, I work with data数据.
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我的工作是与数据打交道。
我运营一家数据可视化设计公司,
00:57
I run a data数据 visualization可视化 design设计 company公司,
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我们设计和开发一些方法,
意图通过视觉表现
01:00
and we design设计 and develop发展 ways方法
to make information信息 accessible无障碍
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01:03
through通过 visual视觉 representations交涉.
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使信息容易理解。
多年来我的工作教给我的是,
01:05
What my job工作 has taught me over the years年份
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要真正了解数据及其真实的潜力,
01:08
is that to really understand理解 data数据
and their true真正 potential潜在,
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实际上有时我们必须忘掉它们,
01:12
sometimes有时 we actually其实
have to forget忘记 about them
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反而才能识破它们。
01:16
and see through通过 them instead代替.
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01:18
Because data数据 are always
just a tool工具 we use to represent代表 reality现实.
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因为数据永远只是我们
用来表达现实的工具。
数据总被用作其他东西的占位符,
01:22
They're always used
as a placeholder占位符 for something else其他,
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但它们永远不是真实的事物。
01:24
but they are never the real真实 thing.
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但是让我先回溯一下,
01:27
But let me step back for a moment时刻
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回到我个人第一次
明白这道理的时候。
01:29
to when I first understood了解
this personally亲自.
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01:32
In 1994, I was 13 years年份 old.
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1994年,我13岁,
生活在意大利。
01:35
I was a teenager青少年 in Italy意大利.
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我太年轻了,对政治不感兴趣,
01:37
I was too young年轻
to be interested有兴趣 in politics政治,
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但是我知道有个商人,
西尔维奥·贝卢斯科尼,
01:40
but I knew知道 that a businessman商人,
Silvio西尔维奥 Berlusconi贝卢斯科尼,
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正在代表右翼温和派竞选总统。
01:42
was running赛跑 for president主席
for the moderate中等 right.
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01:46
We lived生活 in a very liberal自由主义的 town,
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我们的镇上非常偏向自由党,
而且我父亲是民主党的政客。
01:48
and my father父亲 was a politician政治家
for the Democratic民主的 Party派对.
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我记得没有人认为
贝卢斯科尼可以当选——
01:51
And I remember记得 that no one thought
that Berlusconi贝卢斯科尼 could get elected当选 --
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他完全不可能当选。
01:55
that was totally完全 not an option选项.
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01:58
But it happened发生.
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然而事实相反,他当选了。
我非常清晰地记得那种感觉。
01:59
And I remember记得 the feeling感觉 very vividly生动地.
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那是个巨大的意外,
02:02
It was a complete完成 surprise,
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因为我爸爸信誓旦旦地说,
他知道我们镇上没人投票给他。
02:04
as my dad promised许诺 that in my town
he knew知道 nobody没有人 who voted for him.
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02:10
This was the first time
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这是第一次
我手里的数据反映出的现实
是完全错误的。
02:12
when the data数据 I had gave me
a completely全然 distorted扭曲 image图片 of reality现实.
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我的数据样本实际上
很有限且有偏向性,
02:17
My data数据 sample样品 was actually其实
pretty漂亮 limited有限 and skewed偏斜,
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可能是由于我以为
自己生活在一个气泡里,
02:20
so probably大概 it was because of that,
I thought, I lived生活 in a bubble泡沫,
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没有足够的机会看到外面的世界。
02:24
and I didn't have enough足够 chances机会
to see outside of it.
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02:28
Now, fast-forward快进 to November十一月 8, 2016
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现在,快进到2016年11月8日,
在美国。
02:31
in the United联合的 States状态.
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02:33
The internet互联网 polls民意调查,
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互联网民意调查、
统计模型、
02:35
statistical统计 models楷模,
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所有专家对总统选举的
预测结果意见一致。
02:36
all the pundits专家 agreeing同意 on a possible可能
outcome结果 for the presidential总统 election选举.
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好像这次我们的信息很充足,
02:41
It looked看着 like we had
enough足够 information信息 this time,
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而且有更多机会看到
我们所在的封闭圈以外的世界,
02:44
and many许多 more chances机会 to see outside
the closed关闭 circle we lived生活 in --
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但是很显然,事实并非如此。
02:48
but we clearly明确地 didn't.
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那感觉太熟悉了。
02:50
The feeling感觉 felt very familiar.
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我亲身经历过。
02:52
I had been there before.
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02:54
I think it's fair公平 to say
the data数据 failed失败 us this time --
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我认为可以说这次是
数据让我们失望了,
而且非常严重。
02:57
and pretty漂亮 spectacularly壮观.
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我们相信了数据,
02:59
We believed相信 in data数据,
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但真正发生的是,
03:00
but what happened发生,
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即便是最受尊敬的报纸也只是
03:02
even with the most respected尊敬 newspaper报纸,
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痴迷于将所有事情缩减成
两个简单的百分比数字,
03:05
is that the obsession困扰 to reduce减少 everything
to two simple简单 percentage百分比 numbers数字
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用来制作震撼的标题,
03:09
to make a powerful强大 headline标题
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让我们聚焦在这两个数字上,
03:11
made制作 us focus焦点 on these two digits数字
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并且只看到这两个数字。
03:13
and them alone单独.
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为了简化信息,
03:15
In an effort功夫 to simplify简化 the message信息
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画出漂亮的、无法抵御的红蓝地图,
03:17
and draw a beautiful美丽,
inevitable必然 red and blue蓝色 map地图,
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我们完全失去了重点。
03:21
we lost丢失 the point completely全然.
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我们莫名其妙地忘记了还有故事——
03:23
We somehow不知何故 forgot忘记
that there were stories故事 --
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这些数字背后的人类的故事。
03:25
stories故事 of human人的 beings众生
behind背后 these numbers数字.
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03:29
In a different不同 context上下文,
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还有一件背景不同
但情况很相似的事件,
03:30
but to a very similar类似 point,
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这位女士给我的团队
提出了一个特殊的挑战。
03:32
a peculiar奇特 challenge挑战 was presented呈现
to my team球队 by this woman女人.
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她带着很多数据来找我们,
03:36
She came来了 to us with a lot of data数据,
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但最终她想要的是
讲一个可能最有人性的故事。
03:38
but ultimately最终 she wanted to tell
one of the most humane人道 stories故事 possible可能.
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她是萨曼莎·克里斯托维蒂,
03:43
She's Samantha萨曼莎 CristoforettiCristoforetti.
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意大利第一位女性宇航员,
03:45
She has been the first
Italian意大利 woman女人 astronaut宇航员,
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在出发去国际空间站进行
03:47
and she contacted联系 us before being存在 launched推出
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03:50
on a six-month-long六个月之久 expedition远征
to the International国际 Space空间 Station.
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为期六个月的远征之前,
她联系到我们。
她说:“我要去太空了,
03:54
She told us, "I'm going to space空间,
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我想用我的任务数据
做些有意义的事,
03:56
and I want to do something meaningful富有意义的
with the data数据 of my mission任务
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去联络人们。”
03:59
to reach达到 out to people."
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04:01
A mission任务 to the
International国际 Space空间 Station
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去国际空间站的任务
带着兆兆字节(太字节)的数据,
04:04
comes with terabytes兆兆字节 of data数据
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04:06
about anything you can possibly或者 imagine想像 --
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涉及你能想到的任何事——
绕地轨道,
04:08
the orbits轨道 around Earth地球,
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ISS的速度和位置,
04:10
the speed速度 and position位置 of the ISSISS
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还有另外数千个
来自其传感器的直播流。
04:12
and all of the other thousands数千
of live生活 streams from its sensors传感器.
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我们拥有所有可以想到的硬数据——
04:16
We had all of the hard data数据
we could think of --
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就像那次选举前的专家一样——
04:19
just like the pundits专家
before the election选举 --
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但是这些数字是什么意思?
04:22
but what is the point
of all these numbers数字?
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人们对数据本身不感兴趣,
04:25
People are not interested有兴趣
in data数据 for the sake清酒 of it,
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因为数字永远不是重点。
04:27
because numbers数字 are never the point.
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它们总是用来结束的手段。
04:29
They're always the means手段 to an end结束.
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04:32
The story故事 we needed需要 to tell
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我们要讲的故事是,
小箱子里有一个人
04:34
is that there is a human人的 being存在
in a teeny蝇头 box
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正在你头上的太空中飞行
04:37
flying飞行 in space空间 above以上 your head,
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你在晴朗的夜晚能用肉眼看到她。
04:39
and that you can actually其实 see her
with your naked eye on a clear明确 night.
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所以我们决定用数据在萨曼莎和
04:43
So we decided决定 to use data数据
to create创建 a connection连接
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正从地面看着她的
所有人之间建立一个连接。
04:46
between之间 Samantha萨曼莎 and all of the people
looking at her from below下面.
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我们设计和开发了“太空中的朋友”,
04:50
We designed设计 and developed发达
what we called "Friends in Space空间,"
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这是一个网络应用程序,
简单地让你从你的位置
04:53
a web卷筒纸 application应用 that simply只是
lets让我们 you say "hello你好" to Samantha萨曼莎
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对萨曼莎说“你好”,
04:58
from where you are,
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对世界各地的所有同时在线的人
04:59
and "hello你好" to all the people
who are online线上 at the same相同 time
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说“你好”。
05:03
from all over the world世界.
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所有这些“你好”都能在
萨曼莎飞过的地图上
05:05
And all of these "hellos打着招呼"
left visible可见 marks分数 on the map地图
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05:09
as Samantha萨曼莎 was flying飞行 by
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留下可见的痕迹,
而且她每天都在使用推特
05:11
and as she was actually其实
waving挥手 back every一切 day at us
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从国际空间站向我们问候。
05:14
using运用 Twitter推特 from the ISSISS.
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这使人们看待任务数据的
角度大有不同。
05:16
This made制作 people see the mission's访问团 data数据
from a very different不同 perspective透视.
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这一切突然变得
更加关乎人性和好奇心,
05:21
It all suddenly突然 became成为 much more
about our human人的 nature性质 and our curiosity好奇心,
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而不是技术。
05:26
rather than technology技术.
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所以虽然数据丰富了经历,
05:28
So data数据 powered动力 the experience经验,
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但人类的故事才是背后的驱动力。
05:30
but stories故事 of human人的 beings众生
were the drive驾驶.
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05:34
The very positive response响应
of its thousands数千 of users用户
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数千用户的积极回应
教给我非常重要的一点——
05:38
taught me a very important重要 lesson --
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处理数据意味着设计各种方法,
05:40
that working加工 with data数据
means手段 designing设计 ways方法
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将抽象和无法量化的信息转化成
05:43
to transform转变 the abstract抽象
and the uncountable不可数的
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可以看到、感觉到并
与我们的生活和行为
05:45
into something that can be seen看到,
felt and directly reconnected重新连接
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直接重新连接的东西,
05:49
to our lives生活 and to our behaviors行为,
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而如果我们让对数字的痴迷和
05:52
something that is hard to achieve实现
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05:54
if we let the obsession困扰 for the numbers数字
and the technology技术 around them
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围绕数字的技术
在这个过程中引领我们,
则很难实现这一点。
05:57
lead us in the process处理.
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但是,我们还能更进一步
将数据与它们所代表的故事连接起来。
06:00
But we can do even more to connect data数据
to the stories故事 they represent代表.
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我们可以完全去掉技术。
06:05
We can remove去掉 technology技术 completely全然.
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几年前,我遇到另一位女士,
06:08
A few少数 years年份 ago, I met会见 this other woman女人,
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斯蒂芬妮·波萨维奇——
06:10
Stefanie孙燕姿 PosavecPosavec --
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一位伦敦的设计师,
与我一样对数据热爱和痴迷。
06:11
a London-based总部设在伦敦 designer设计师 who shares分享 with me
the passion and obsession困扰 about data数据.
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我们不认识对方,
06:17
We didn't know each other,
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但我们决定进行
一个非常激进的实验,
06:19
but we decided决定 to run
a very radical激进 experiment实验,
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开始一场只使用数据的交流,
06:22
starting开始 a communication通讯 using运用 only data数据,
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不使用任何其他语言,
06:24
no other language语言,
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06:26
and we opted选择 for using运用 no technology技术
whatsoever任何 to share分享 our data数据.
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我们选择不使用任何科技
来分享我们的数据。
事实上,我们唯一的沟通方式
06:30
In fact事实, our only means手段 of communication通讯
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是通过老式邮局。
06:33
would be through通过
the old-fashioned过时 post岗位 office办公室.
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为了“亲爱的数据”,
一年中的每个星期,
06:36
For "Dear Data数据," every一切 week for one year,
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我们用自己的
个人数据来了解彼此——
06:39
we used our personal个人 data数据
to get to know each other --
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个人信息包括每周分享的平凡话题,
06:42
personal个人 data数据 around weekly每周
shared共享 mundane平凡 topics主题,
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从我们自己的感受
06:46
from our feelings情怀
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到我们与爱人之间的互动,
06:47
to the interactions互动 with our partners伙伴,
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从我们收到的赞美到周围的声音。
06:49
from the compliments赞美 we received收到
to the sounds声音 of our surroundings环境.
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06:53
Personal个人 information信息
that we would then manually手动 hand draw
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然后我们把这些个人信息
手绘在一张明信片大小的纸上,
06:57
on a postcard-size明信片尺寸 sheet of paper
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每周从伦敦寄到我所在的
06:59
that we would every一切 week
send发送 from London伦敦 to New York纽约,
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07:02
where I live生活,
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纽约,
以及从纽约寄到她所在的伦敦。
07:03
and from New York纽约 to London伦敦,
where she lives生活.
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明信片的正面是数据图,
07:06
The front面前 of the postcard明信片
is the data数据 drawing画画,
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卡片背面当然包括
07:10
and the back of the card
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对方的地址,
07:11
contains包含 the address地址
of the other person, of course课程,
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还有如何破译数据图的方法。
07:13
and the legend传说 for how
to interpret our drawing画画.
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07:17
The very first week into the project项目,
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在开始的第一个星期,
我们实际上选择了一个
相当冷门和非私人化的话题。
07:19
we actually其实 chose选择
a pretty漂亮 cold and impersonal非人的 topic话题.
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一周内看了多少次时间?
07:22
How many许多 times do we
check the time in a week?
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07:26
So here is the front面前 of my card,
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这里是我的卡片的正面,
你可以看到,每一个小符号
07:28
and you can see that every一切 little symbol符号
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代表着我每次看时间,
07:30
represents代表 all of the times
that I checked检查 the time,
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位置按顺序代表日期和小时——
07:34
positioned定位的 for days
and different不同 hours小时 chronologically按时间顺序 --
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没有什么复杂的。
07:37
nothing really complicated复杂 here.
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07:40
But then you see in the legend传说
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但是再看看这破译说明,
我是如何把这些时刻的
各种细节加进去的。
07:41
how I added添加 anecdotal传闻 details细节
about these moments瞬间.
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实际上,不同类型的符号
代表着我为什么要看时间——
07:45
In fact事实, the different不同 types类型 of symbols符号
indicate表明 why I was checking检查 the time --
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当时我在做什么?
07:49
what was I doing?
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我无聊吗?我饿吗?
07:51
Was I bored无聊? Was I hungry饥饿?
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我迟到了吗?
07:52
Was I late晚了?
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我是有意看表
还是随意瞥一眼时钟?
07:54
Did I check it on purpose目的
or just casually胡乱 glance一瞥 at the clock时钟?
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但关键是——
07:57
And this is the key part部分 --
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我的数据收集代表了我的
07:59
representing代表 the details细节
of my days and my personality个性
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08:03
through通过 my data数据 collection采集.
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生活细节和个性。
用数据作为镜头或过滤器
来发现和揭示,例如
08:05
Using运用 data数据 as a lens镜片 or a filter过滤
to discover发现 and reveal揭示, for example,
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我对迟到无休止的焦虑,
08:10
my never-ending没完没了 anxiety焦虑 for being存在 late晚了,
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即使我绝对每次都准时。
08:12
even though虽然 I'm absolutely绝对 always on time.
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08:16
Stefanie孙燕姿 and I spent花费 one year
collecting搜集 our data数据 manually手动
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斯蒂芬妮和我花了一年时间
手动收集我们的数据,
迫使我们专注于
计算机无法收集——
08:20
to force us to focus焦点 on the nuances细微之处
that computers电脑 cannot不能 gather收集 --
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至少现在还无法收集的细节,
08:24
or at least最小 not yet然而 --
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08:26
using运用 data数据 also to explore探索 our minds头脑
and the words we use,
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使用数据来探索我们的思想
和我们使用的词语,
而不仅是我们的活动。
08:29
and not only our activities活动.
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比如在第三周,
08:31
Like at week number three,
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我们记录了我们所说的
以及收到的“感谢”,
08:33
where we tracked追踪 the "thank yous你的"
we said and were received收到,
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它让我意识到,我多数时间
在感谢我不认识的人。
08:37
and when I realized实现 that I thank
mostly大多 people that I don't know.
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显然我对感谢男女服务生有强迫症,
08:41
Apparently显然地 I'm a compulsive强迫 thankerthanker
to waitresses女服务员 and waiters服务员,
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但绝对没有对身边的人
表达足够的感谢。
08:46
but I definitely无疑 don't thank enough足够
the people who are close to me.
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08:51
Over one year,
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在这一年多里,
对这些类型的行为
积极留意和计数的过程
08:52
the process处理 of actively积极地 noticing注意到
and counting数数 these types类型 of actions行动
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成为了一种仪式。
08:56
became成为 a ritual仪式.
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它真的改变了我们自己。
08:58
It actually其实 changed ourselves我们自己.
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我们变得更加贴近真实的自己,
09:00
We became成为 much more
in tune with ourselves我们自己,
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更加了解我们的行为和周围环境。
09:02
much more aware知道的 of our behaviors行为
and our surroundings环境.
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一年多的时间,斯蒂芬妮和我
通过共享数据日记
09:06
Over one year, Stefanie孙燕姿 and I
connected连接的 at a very deep level水平
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09:09
through通过 our shared共享 data数据 diary日记,
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2016
建立了非常深层的联系,
但是我们能做到这样,
只因为我们用这些数字表达了自己,
09:11
but we could do this only because
we put ourselves我们自己 in these numbers数字,
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并加入了我们的个人故事背景。
09:16
adding加入 the contexts上下文
of our very personal个人 stories故事 to them.
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这是使它们真正有意义
09:20
It was the only way
to make them truly meaningful富有意义的
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09:22
and representative代表 of ourselves我们自己.
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并代表了我们自己的唯一途径。
09:26
I am not asking you
to start开始 drawing画画 your personal个人 data数据,
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我不是要你开始画你的个人数据,
也不是要你找个跨洋笔友。
09:29
or to find a pen钢笔 pal朋友 across横过 the ocean海洋.
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但是我请你把数据——
09:32
But I'm asking you to consider考虑 data数据 --
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各种数据——
09:35
all kind of data数据 --
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看成交谈的开始,
09:36
as the beginning开始 of the conversation会话
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而不是终止。
09:38
and not the end结束.
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09:40
Because data数据 alone单独
will never give us a solution.
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因为数据本身永远不会给我们答案。
这就是为什么数据
让我们败得这么惨——
09:43
And this is why data数据 failed失败 us so badly --
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因为我们没有考虑到
要用适量的背景信息
09:46
because we failed失败 to include包括
the right amount of context上下文
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来展示现实——
09:49
to represent代表 reality现实 --
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微妙的、错综复杂的现实。
09:50
a nuanced细致入微, complicated复杂
and intricate错综复杂 reality现实.
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09:54
We kept不停 looking at these two numbers数字,
205
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我们一直盯着这两个数字,
痴迷于这两个数字,
09:57
obsessing沉迷 with them
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假装我们的世界
09:58
and pretending假装 that our world世界
could be reduced减少
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可以缩减成两个数字和一场赛马,
10:01
to a couple一对 digits数字 and a horse race种族,
208
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而真实的故事,
10:03
while the real真实 stories故事,
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真正重要的故事
10:05
the ones那些 that really mattered要紧,
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在别处。
10:06
were somewhere某处 else其他.
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如果只用模型和算法
来看待这些故事,我们错过的是
10:08
What we missed错过 looking at these stories故事
only through通过 models楷模 and algorithms算法
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10:12
is what I call "data数据 humanism人道主义."
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我所说的“数据人文主义”。
在文艺复兴时期的人文主义中,
10:15
In the Renaissance再生 humanism人道主义,
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欧洲的智者们
10:17
European欧洲的 intellectuals知识分子
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在他们世界观的中心位置
摆放的是人类本性,而不是上帝。
10:19
placed放置 the human人的 nature性质 instead代替 of God
at the center中央 of their view视图 of the world世界.
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我相信在数据的世界,
10:24
I believe something similar类似
needs需求 to happen发生
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10:27
with the universe宇宙 of data数据.
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也需要类似的事情。
现在我们显然把数据
当成了一个神——
10:28
Now data数据 are apparently显然地
treated治疗 like a God --
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我们现在和未来的永恒真理持有者。
10:31
keeper管理人 of infallible万无一失 truth真相
for our present当下 and our future未来.
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10:35
The experiences经验
that I shared共享 with you today今天
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我今天分享的经验告诉我,
为了使数据忠实地代表我们的人性,
10:38
taught me that to make data数据 faithfully忠实
representative代表 of our human人的 nature性质
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并确保数据不再误导我们,
10:43
and to make sure they will not
mislead误导 us anymore,
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我们需要开始设计方法,
在收集、处理、分析和
10:47
we need to start开始 designing设计 ways方法
to include包括 empathy同情, imperfection缺陷
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10:50
and human人的 qualities气质
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演示数据时,
纳入同情、不完美和人文素质。
10:52
in how we collect搜集, process处理,
analyze分析 and display显示 them.
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10:57
I do see a place地点 where, ultimately最终,
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我能预见,终将有个地方,
数据不会被单纯用来提高效率,
11:00
instead代替 of using运用 data数据
only to become成为 more efficient高效,
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我们都会用数据来变得更人性化。
11:03
we will all use data数据
to become成为 more humane人道.
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谢谢。
11:06
Thank you.
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(掌声)
11:08
(Applause掌声)
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Translated by Yan Gao
Reviewed by Ellen Tung

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ABOUT THE SPEAKER
Giorgia Lupi - Information designer
Giorgia Lupi sees beauty in data. She challenges the impersonality that data communicate, designing engaging visual narratives that re-connect numbers to what they stand for: stories, people, ideas.

Why you should listen

What sets Giorgia Lupi apart is her humanistic approach to the world of data.

Her work frequently crosses the divide between digital, print and handcrafted representations of information: primarily, she draws with data. She has a passion for and obsession with data, the material she uses to tell stories, and the lens through which she sees the world.

Data are often considered to be very impersonal, boring and clinical, but Lupi's work proves the opposite. She makes sense of data with a curious mind and a heterogeneous arsenal, which ranges from digital technology to exhausting and repetitive manual labor. She believes we will ultimately unlock the full potential of data only when we embrace their nature, and make them part of our lives, which will inevitably make data more human in the process.

Trained as an architect, Lupi has always been driven by opposing forces: analysis and intuition, logic and beauty, numbers and images. True to these dichotomies, in 2011 she started both her own company and studying for a PhD. She earned her ddoctorate in design at Politecnico di Milano, where she focused on information mapping, and she is now the design director and co-founder of Accurat, a global, data-driven research, design and innovation firm with offices in Milan and New York. She relocated from Italy to New York City, where she now lives.

Thanks to her work and research, Giorgia is a prominent voice in the world of data. She has spoken at numerous events, universities and institutions around the world, including the Museum of Modern Art, the Guggenheim Museum, PopTech Conference, Eyeo Festival, Fast Company Innovation by Design, New York University, Columbia University and the New York Public Library. She has been featured in major international outlets such as the New York Times, The Guardian, the Washington Post, NPR, BBC, TIME magazine, National Geographic, Scientific American, Popular Science, Wired, Vogue, Vanity Fair, Monocle and more. Her work has been exhibited at the Design Museum, the Science Museum, and Somerset House in London; the New York Hall of Science and the Storefront for Art and Architecture in New York; at the Triennale Design Museum and the Design Week in Milan, among others.

With her company, Accurat, she has worked with major international clients including IBM, Google, Microsoft, the United Nations, the World Health Organization, the World Economic Forum, the European Union, the Louis Vuitton-Moet-Hennessy Group, Fiat Chrysler Automobiles, J.P. Morgan Asset Management, Unicredit Group and KPMG Advisory.

Giorgia is the co-author of Dear Data, an aspirational hand-drawn data visualization book that explores the more slippery details of daily life through data, revealing the patterns that inform our decisions and affect our relationships.

Her work is part of the permanent collection of the Museum of Modern Art.

More profile about the speaker
Giorgia Lupi | Speaker | TED.com