TEDNYC
Giorgia Lupi: How we can find ourselves in data
喬姬雅‧露琵: 在數據中找到自己
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喬姬雅‧露琵說出數據背後的人情味，為死板的數據添上生動的細節。在這場迷人的演說中，她分享了我們如何為數據加上個性，將平凡無奇的日常生活變成一目瞭然的圖表，讓抽象、不可數的內容可以被看見、被感受，並與我們的生活重新連結。
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. Full bio
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 like.
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我上週的生活長這樣。
00:16
What I did,
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我做了什麼、
00:18
who I was with,
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和誰在一起、
00:20
the main sensations I had
for every waking hour ...
for every waking hour ...
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清醒時，每個小時的感受......
00:24
If the feeling came as I thought of my dad
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是否我思念起了
00:26
who recently passed away,
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剛過世的父親。
00:28
or if I could have just definitely
avoided the worries and anxieties.
avoided the worries and anxieties.
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或有些無法避免的煩惱和焦慮。
00:32
And if you think I'm a little obsessive,
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若你覺得我有點走火入魔，
00:34
you're probably right.
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你可能是對的。
00:36
But clearly, from this visualization,
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但顯然這些視覺化的圖表，
00:38
you can learn much more about me
than from this other one,
than from this other one,
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比起其它方式，讓你更了解我，
00:41
which are images you're
probably more familiar with
probably more familiar with
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像是一些大家都很熟悉的圖表，
00:44
and which you possibly even have
on your phone right now.
on your phone right now.
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可能你手機裡現在就有了。
00:47
Bar charts for the steps you walked,
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比如記錄走路步數的長條圖、
00:50
pie charts for the quality
of your sleep --
of your sleep --
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表示睡眠品質的圓餅圖、
00:52
the path of your morning runs.
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晨跑的路徑圖......
00:55
In my day job, I work with data.
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我的工作就是與數據打交道。
00:57
I run a data visualization design company,
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我有一間數據視覺化設計公司，
01:00
and we design and develop ways
to make information accessible
to make information accessible
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負責設計和開發
01:03
through visual representations.
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視覺化呈現資訊的方式。
01:05
What my job has taught me over the years
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過去幾年的工作經驗告訴我，
01:08
is that to really understand data
and their true potential,
and their true potential,
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想要真正了解數據和它的潛力，
01:12
sometimes we actually
have to forget about them
have to forget about them
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有時不能只看表象，
01:16
and see through them instead.
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而是要深入核心。
01:18
Because data are always
just a tool we use to represent reality.
just a tool we use to represent reality.
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因為數據只是表達現實的工具。
01:22
They're always used
as a placeholder for something else,
as a placeholder for something else,
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它們只是一些代碼，
01:24
but they are never the real thing.
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不是實際的狀況。
01:27
But let me step back for a moment
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讓我退一步說明，
01:29
to when I first understood
this personally.
this personally.
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回到我第一次有所體會的那年。
01:32
In 1994, I was 13 years old.
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1994 年，我 13 歲，
01:35
I was a teenager in Italy.
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一名生活在義大利的年輕人。
01:37
I was too young
to be interested in politics,
to be interested in politics,
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當時還小，對政治沒興趣，
01:40
but I knew that a businessman,
Silvio Berlusconi,
Silvio Berlusconi,
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但我知道有個商人，
叫做貝魯斯柯尼，
叫做貝魯斯柯尼，
01:42
was running for president
for the moderate right.
for the moderate right.
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當時正代表右翼溫和派競選總統。
01:46
We lived in a very liberal town,
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我住的地方是左派重鎮，
01:48
and my father was a politician
for the Democratic Party.
for the Democratic Party.
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我爸還是民主黨的政治人物。
01:51
And I remember that no one thought
that Berlusconi could get elected --
that Berlusconi could get elected --
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我還記得，大家都說
貝魯斯柯尼選不上，
貝魯斯柯尼選不上，
01:55
that was totally not an option.
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沒人覺得他會選上。
01:58
But it happened.
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結果他當選了。
01:59
And I remember the feeling very vividly.
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當時的感受我仍記憶猶新。
02:02
It was a complete surprise,
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完全出乎我們的意料，
02:04
as my dad promised that in my town
he knew nobody who voted for him.
he knew nobody who voted for him.
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我爸信誓旦旦地說，
鎮上不會有人投給他。
鎮上不會有人投給他。
02:10
This was the first time
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這是第一次，
02:12
when the data I had gave me
a completely distorted image of reality.
a completely distorted image of reality.
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我收集的數據與現實有落差。
02:17
My data sample was actually
pretty limited and skewed,
pretty limited and skewed,
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我的數據樣本既狹隘又偏頗，
02:20
so probably it was because of that,
I thought, I lived in a bubble,
I thought, I lived in a bubble,
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也因此我覺得我只活在同溫層，
02:24
and I didn't have enough chances
to see outside of it.
to see outside of it.
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沒機會看到外面的真實情況。
02:28
Now, fast-forward to November 8, 2016
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接著快轉到 2016 年 11 月 8 日。
02:31
in the United States.
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美國的總統大選。
02:33
The internet polls,
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網路民調、
02:35
statistical models,
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統計模型、
02:36
all the pundits agreeing on a possible
outcome for the presidential election.
outcome for the presidential election.
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專家學者都說希拉蕊會贏。
02:41
It looked like we had
enough information this time,
enough information this time,
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好像這一次我們的資訊很充足，
02:44
and many more chances to see outside
the closed circle we lived in --
the closed circle we lived in --
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而且有更多機會看到，
同溫層以外的世界。
同溫層以外的世界。
02:48
but we clearly didn't.
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但我們根本沒有。
02:50
The feeling felt very familiar.
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這感覺似曾相識。
02:52
I had been there before.
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我以前就經歷過。
02:54
I think it's fair to say
the data failed us this time --
the data failed us this time --
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這次真的可以說數據騙了我們，
02:57
and pretty spectacularly.
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而且騙慘了。
02:59
We believed in data,
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我們太相信數據了，
03:00
but what happened,
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結果呢？
03:02
even with the most respected newspaper,
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連最權威的報紙，
03:05
is that the obsession to reduce everything
to two simple percentage numbers
to two simple percentage numbers
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都只想將所有事情
03:09
to make a powerful headline
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簡化成兩位數的支持率，
03:11
made us focus on these two digits
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製作出最聳動的標題，
03:13
and them alone.
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讓大眾只看到數字。
03:15
In an effort to simplify the message
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他們費盡心思簡化資料，
03:17
and draw a beautiful,
inevitable red and blue map,
inevitable red and blue map,
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畫出精美的紅藍分布圖，
03:21
we lost the point completely.
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我們完全失去焦點。
03:23
We somehow forgot
that there were stories --
that there were stories --
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我們忘記數據背後的故事，
03:25
stories of human beings
behind these numbers.
behind these numbers.
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數字背後關於人的故事。
03:29
In a different context,
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這邊要岔個題，
03:30
but to a very similar point,
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但要說的道理是一樣的，
03:32
a peculiar challenge was presented
to my team by this woman.
to my team by this woman.
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這名女子向我的團隊
提出了一個特殊的挑戰。
提出了一個特殊的挑戰。
03:36
She came to us with a lot of data,
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她帶著一堆數據找上我們，
03:38
but ultimately she wanted to tell
one of the most humane stories possible.
one of the most humane stories possible.
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但最終她想要說出的，
就是一個最有人情味的故事。
就是一個最有人情味的故事。
03:43
She's Samantha Cristoforetti.
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這個人就是
薩曼莎‧克里斯托福雷蒂。
薩曼莎‧克里斯托福雷蒂。
03:45
She has been the first
Italian woman astronaut,
Italian woman astronaut,
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她是義大利第一位女太空人，
03:47
and she contacted us before being launched
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她在出任務前找上我們，
03:50
on a six-month-long expedition
to the International Space Station.
to the International Space Station.
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她要到國際太空站待六個月。
03:54
She told us, "I'm going to space,
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她告訴我們：「我要上太空了，
03:56
and I want to do something meaningful
with the data of my mission
with the data of my mission
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我想用任務中的數據，
03:59
to reach out to people."
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和社會大眾交流。」
04:01
A mission to the
International Space Station
International Space Station
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一趟國際太空站的任務，
04:04
comes with terabytes of data
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會有好幾兆位元組的數據，
04:06
about anything you can possibly imagine --
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你能想到的資料都有：
04:08
the orbits around Earth,
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環繞地球的軌道數據、
04:10
the speed and position of the ISS
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國際太空站的速率和位置、
04:12
and all of the other thousands
of live streams from its sensors.
of live streams from its sensors.
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還有感應器上一大堆的即時資訊。
04:16
We had all of the hard data
we could think of --
we could think of --
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我們握有太空任務的所有數據，
04:19
just like the pundits
before the election --
before the election --
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專家學者在大選前也都有數據，
04:22
but what is the point
of all these numbers?
of all these numbers?
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但這些數字到底可以做什麼？
04:25
People are not interested
in data for the sake of it,
in data for the sake of it,
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大家對數據本身根本沒興趣，
04:27
because numbers are never the point.
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因為數字不是重點。
04:29
They're always the means to an end.
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數據只是了解現實的手段。
04:32
The story we needed to tell
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我們要說的故事是，
04:34
is that there is a human being
in a teeny box
in a teeny box
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在這個小箱子裡有個人，
04:37
flying in space above your head,
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正在你頭上的外太空飛行，
04:39
and that you can actually see her
with your naked eye on a clear night.
with your naked eye on a clear night.
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而且你能在清朗的夜空
用肉眼看見她。
用肉眼看見她。
04:43
So we decided to use data
to create a connection
to create a connection
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所以我們要用數據創造連結，
04:46
between Samantha and all of the people
looking at her from below.
looking at her from below.
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連結薩曼莎和地上的我們。
04:50
We designed and developed
what we called "Friends in Space,"
what we called "Friends in Space,"
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我們設計並開發了
「太空中的朋友」，
「太空中的朋友」，
04:53
a web application that simply
lets you say "hello" to Samantha
lets you say "hello" to Samantha
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它是一個網路應用程式
可以讓你從所在地透過網頁，
跟薩曼莎說「哈囉」，
04:58
from where you are,
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04:59
and "hello" to all the people
who are online at the same time
who are online at the same time
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同時也可以跟線上的
全球網友們說「哈囉」。
全球網友們說「哈囉」。
05:03
from all over the world.
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05:05
And all of these "hellos"
left visible marks on the map
left visible marks on the map
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如果薩曼莎經過這些「哈囉」，
05:09
as Samantha was flying by
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地圖上就會有記號，
05:11
and as she was actually
waving back every day at us
waving back every day at us
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她每天也都從國際太空站，
05:14
using Twitter from the ISS.
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透過推特跟大家互動。
05:16
This made people see the mission's data
from a very different perspective.
from a very different perspective.
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這讓大家用非常不同的角度，
去看任務的數據。
去看任務的數據。
05:21
It all suddenly became much more
about our human nature and our curiosity,
about our human nature and our curiosity,
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讓一切更貼近人性並
引發我們的好奇心，
引發我們的好奇心，
05:26
rather than technology.
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而不只是冷冰冰的科技。
05:28
So data powered the experience,
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數據能強化體驗，
05:30
but stories of human beings
were the drive.
were the drive.
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但人的故事才是關鍵。
05:34
The very positive response
of its thousands of users
of its thousands of users
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數千位使用者的正面回饋，
05:38
taught me a very important lesson --
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給我上了非常重要的一課：
05:40
that working with data
means designing ways
means designing ways
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與數據為伍就是要設計出
05:43
to transform the abstract
and the uncountable
and the uncountable
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可以把抽象、不可數的概念，
05:45
into something that can be seen,
felt and directly reconnected
felt and directly reconnected
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轉化成看得見、感受得到、
05:49
to our lives and to our behaviors,
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並直接與生活和行為
重新連結的方法，
重新連結的方法，
05:52
something that is hard to achieve
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有時候很難做到，
05:54
if we let the obsession for the numbers
and the technology around them
and the technology around them
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如果我們只著迷於數字及科技，
05:57
lead us in the process.
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就會走偏掉。
06:00
But we can do even more to connect data
to the stories they represent.
to the stories they represent.
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但我們能進一步
連結數據與背後的故事。
連結數據與背後的故事。
06:05
We can remove technology completely.
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不需要科技也辦得到。
06:08
A few years ago, I met this other woman,
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幾年前，我遇見一名女子，
06:10
Stefanie Posavec --
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史黛芬妮‧波薩維克。
06:11
a London-based designer who shares with me
the passion and obsession about data.
the passion and obsession about data.
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她是住倫敦的設計師，
跟我一樣對數據癡迷。
跟我一樣對數據癡迷。
06:17
We didn't know each other,
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我們之前不認識，
06:19
but we decided to run
a very radical experiment,
a very radical experiment,
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但我們做了一個大膽的實驗，
06:22
starting a communication using only data,
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就是只用數據交談，
06:24
no other language,
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而不是語言。
06:26
and we opted for using no technology
whatsoever to share our data.
whatsoever to share our data.
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而且不用任何科技當媒介。
06:30
In fact, our only means of communication
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事實上，我們聯絡的唯一管道，
06:33
would be through
the old-fashioned post office.
the old-fashioned post office.
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就是最老派的郵政系統。
06:36
For "Dear Data," every week for one year,
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《親愛的數據》計畫長達一年，
06:39
we used our personal data
to get to know each other --
to get to know each other --
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我們每週透過數據了解對方。
06:42
personal data around weekly
shared mundane topics,
shared mundane topics,
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每週都是很普通的一些主題：
06:46
from our feelings
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從各自的情緒、
06:47
to the interactions with our partners,
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到跟另一半的互動、
06:49
from the compliments we received
to the sounds of our surroundings.
to the sounds of our surroundings.
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收到的讚美或周圍的聲音。
06:53
Personal information
that we would then manually hand draw
that we would then manually hand draw
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這些資訊我們都手繪在
06:57
on a postcard-size sheet of paper
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明信片大小的表格上，
06:59
that we would every week
send from London to New York,
send from London to New York,
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每週她會從倫敦寄明信片到
07:02
where I live,
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我住的紐約，
07:03
and from New York to London,
where she lives.
where she lives.
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我也從紐約寄到她住的倫敦。
07:06
The front of the postcard
is the data drawing,
is the data drawing,
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明信片的正面是手繪的圖表，
07:10
and the back of the card
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卡片的背面，
07:11
contains the address
of the other person, of course,
of the other person, of course,
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除了對方的地址，
07:13
and the legend for how
to interpret our drawing.
to interpret our drawing.
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還有前面圖表的註解。
07:17
The very first week into the project,
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計畫開始的第一週，
07:19
we actually chose
a pretty cold and impersonal topic.
a pretty cold and impersonal topic.
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我們選了個很生冷、客套主題。
07:22
How many times do we
check the time in a week?
check the time in a week?
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「我們一週內會看幾次錶？」
07:26
So here is the front of my card,
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這是我畫的紀錄，
07:28
and you can see that every little symbol
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上面的那些小記號，
07:30
represents all of the times
that I checked the time,
that I checked the time,
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就是我每次看時間的記錄，
07:34
positioned for days
and different hours chronologically --
and different hours chronologically --
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按照每天、每小時依序紀錄，
07:37
nothing really complicated here.
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其實不會很複雜。
07:40
But then you see in the legend
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但在註解這邊，
07:41
how I added anecdotal details
about these moments.
about these moments.
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我說明了記號的涵義。
07:45
In fact, the different types of symbols
indicate why I was checking the time --
indicate why I was checking the time --
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不同的記號代表不同的理由，
07:49
what was I doing?
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當時在幹嘛？
07:51
Was I bored? Was I hungry?
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無聊嗎？餓了嗎？
07:52
Was I late?
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遲到了嗎？
07:54
Did I check it on purpose
or just casually glance at the clock?
or just casually glance at the clock?
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我是認真看時間，
還是隨意瞄一下？
還是隨意瞄一下？
07:57
And this is the key part --
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這些才是關鍵，
07:59
representing the details
of my days and my personality
of my days and my personality
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我每天、個性上的細節，
08:03
through my data collection.
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透過數據表現出來。
08:05
Using data as a lens or a filter
to discover and reveal, for example,
to discover and reveal, for example,
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把數據當鏡頭或濾鏡，
去發現和揭露，比如說，
08:10
my never-ending anxiety for being late,
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就算我一定會準時到，
我仍對遲到這件事非常焦慮，
我仍對遲到這件事非常焦慮，
08:12
even though I'm absolutely always on time.
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08:16
Stefanie and I spent one year
collecting our data manually
collecting our data manually
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我們花了一年收集對方的數據，
08:20
to force us to focus on the nuances
that computers cannot gather --
that computers cannot gather --
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專注在電腦抓不到的細節——
08:24
or at least not yet --
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至少目前還無法收集，
08:26
using data also to explore our minds
and the words we use,
and the words we use,
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用數據去了解想法、用字遣詞，
08:29
and not only our activities.
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而不只是行為。
08:31
Like at week number three,
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像在第三週，
08:33
where we tracked the "thank yous"
we said and were received,
we said and were received,
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我們記錄了道謝和被道謝情況，
08:37
and when I realized that I thank
mostly people that I don't know.
mostly people that I don't know.
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才發現我常和不認識的人道謝。
08:41
Apparently I'm a compulsive thanker
to waitresses and waiters,
to waitresses and waiters,
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顯然我會制式地向服務生道謝，
08:46
but I definitely don't thank enough
the people who are close to me.
the people who are close to me.
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對身邊的人卻沒那麼客氣。
08:51
Over one year,
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一年以後，
08:52
the process of actively noticing
and counting these types of actions
and counting these types of actions
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有意識地關注、記錄這些事，
08:56
became a ritual.
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變成了一個習慣。
08:58
It actually changed ourselves.
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我們開始有些改變。
09:00
We became much more
in tune with ourselves,
in tune with ourselves,
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我們更清楚自己的步調，
09:02
much more aware of our behaviors
and our surroundings.
and our surroundings.
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更了解自己的行為和周遭環境。
09:06
Over one year, Stefanie and I
connected at a very deep level
connected at a very deep level
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一年後，因為這個計畫，
09:09
through our shared data diary,
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我們兩個有了很深的牽絆。
09:11
but we could do this only because
we put ourselves in these numbers,
we put ourselves in these numbers,
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這都是因為我們在數字之外，
09:16
adding the contexts
of our very personal stories to them.
of our very personal stories to them.
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加上了自己的故事。
09:20
It was the only way
to make them truly meaningful
to make them truly meaningful
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數據因此有了意義，
09:22
and representative of ourselves.
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因此能代表我們。
09:26
I am not asking you
to start drawing your personal data,
to start drawing your personal data,
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我不是要大家開始手繪數據，
09:29
or to find a pen pal across the ocean.
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或是去找個海外的筆友。
09:32
But I'm asking you to consider data --
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是希望今後大家面對數據，
09:35
all kind of data --
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各式各樣的數據，
09:36
as the beginning of the conversation
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都當成對話的開始，
09:38
and not the end.
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而不是終結。
09:40
Because data alone
will never give us a solution.
will never give us a solution.
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因為數據本身不會提供解答。
09:43
And this is why data failed us so badly --
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所以我們才會一直被數據所騙，
09:46
because we failed to include
the right amount of context
the right amount of context
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因為我們忘記數據背後
09:49
to represent reality --
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所呈現的現實，
09:50
a nuanced, complicated
and intricate reality.
and intricate reality.
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是細微、複雜、盤根錯節的。
09:54
We kept looking at these two numbers,
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我們看到候選人的支持率，
09:57
obsessing with them
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就只看到數字，
09:58
and pretending that our world
could be reduced
could be reduced
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假裝我們的世界可以被簡化成
10:01
to a couple digits and a horse race,
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兩個數字和一場競賽，
10:03
while the real stories,
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然而真實的故事、
10:05
the ones that really mattered,
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真正重要的事，
10:06
were somewhere else.
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卻被拋在一旁。
10:08
What we missed looking at these stories
only through models and algorithms
only through models and algorithms
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不要只專注在模型和演算法，
10:12
is what I call "data humanism."
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也就是所謂的「數據人文主義」。
10:15
In the Renaissance humanism,
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在文藝復興人文主義時代，
10:17
European intellectuals
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歐洲的知識分子，
10:19
placed the human nature instead of God
at the center of their view of the world.
at the center of their view of the world.
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將眼光從「上帝」轉向「人性」。
10:24
I believe something similar
needs to happen
needs to happen
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我覺得類似的轉變，
10:27
with the universe of data.
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也該發生在數據的研究。
10:28
Now data are apparently
treated like a God --
treated like a God --
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現在大家都把數據當上帝來拜，
10:31
keeper of infallible truth
for our present and our future.
for our present and our future.
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覺得數據是貫通古今的真理。
10:35
The experiences
that I shared with you today
that I shared with you today
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我今天跟各位分享的經驗，
10:38
taught me that to make data faithfully
representative of our human nature
representative of our human nature
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就是要讓數據去真實呈現人性，
10:43
and to make sure they will not
mislead us anymore,
mislead us anymore,
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而不是再次誤導大眾。
10:47
we need to start designing ways
to include empathy, imperfection
to include empathy, imperfection
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我們要將同理心、不完美
10:50
and human qualities
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以及人性，
10:52
in how we collect, process,
analyze and display them.
analyze and display them.
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投入數據的收集、
處裡、分析、呈現。
處裡、分析、呈現。
10:57
I do see a place where, ultimately,
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我相信未來有一天，
11:00
instead of using data
only to become more efficient,
only to become more efficient,
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數據不只讓我們更有效率，
11:03
we will all use data
to become more humane.
to become more humane.
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也讓我們更有人情味。
11:06
Thank you.
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謝謝。
11:08
(Applause)
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（掌聲）
ABOUT THE SPEAKER
Giorgia Lupi - Information designerGiorgia 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.
Giorgia Lupi | Speaker | TED.com