What I did,

我上週的生活長這樣。

who I was with,

我做了什麼、

the main sensations I had for every waking hour ...

和誰在一起、

If the feeling came as I thought of my dad

清醒時，每個小時的感受......

who recently passed away,

是否我思念起了

or if I could have just definitely avoided the worries and anxieties.

剛過世的父親。

And if you think I'm a little obsessive,

或有些無法避免的煩惱和焦慮。

you're probably right.

若你覺得我有點走火入魔，

But clearly, from this visualization,

你可能是對的。

you can learn much more about me than from this other one,

但顯然這些視覺化的圖表，

which are images you're probably more familiar with

比起其它方式，讓你更了解我，

and which you possibly even have on your phone right now.

像是一些大家都很熟悉的圖表，

Bar charts for the steps you walked,

可能你手機裡現在就有了。

pie charts for the quality of your sleep --

比如記錄走路步數的長條圖、

the path of your morning runs.

表示睡眠品質的圓餅圖、

In my day job, I work with data.

晨跑的路徑圖......

I run a data visualization design company,

我的工作就是與數據打交道。

and we design and develop ways to make information accessible

我有一間數據視覺化設計公司，

through visual representations.

負責設計和開發

What my job has taught me over the years

視覺化呈現資訊的方式。

is that to really understand data and their true potential,

過去幾年的工作經驗告訴我，

sometimes we actually have to forget about them

想要真正了解數據和它的潛力，

and see through them instead.

有時不能只看表象，

Because data are always just a tool we use to represent reality.

而是要深入核心。

They're always used as a placeholder for something else,

因為數據只是表達現實的工具。

but they are never the real thing.

它們只是一些代碼，

But let me step back for a moment

不是實際的狀況。

to when I first understood this personally.

讓我退一步說明，

In 1994, I was 13 years old.

回到我第一次有所體會的那年。

I was a teenager in Italy.

1994 年，我 13 歲，

I was too young to be interested in politics,

一名生活在義大利的年輕人。

but I knew that a businessman, Silvio Berlusconi,

當時還小，對政治沒興趣，

was running for president for the moderate right.

但我知道有個商人， 叫做貝魯斯柯尼，

We lived in a very liberal town,

當時正代表右翼溫和派競選總統。

and my father was a politician for the Democratic Party.

我住的地方是左派重鎮，

And I remember that no one thought that Berlusconi could get elected --

我爸還是民主黨的政治人物。

that was totally not an option.

我還記得，大家都說 貝魯斯柯尼選不上，

But it happened.

沒人覺得他會選上。

And I remember the feeling very vividly.

結果他當選了。

It was a complete surprise,

當時的感受我仍記憶猶新。

as my dad promised that in my town he knew nobody who voted for him.

完全出乎我們的意料，

This was the first time

我爸信誓旦旦地說， 鎮上不會有人投給他。

when the data I had gave me a completely distorted image of reality.

這是第一次，

My data sample was actually pretty limited and skewed,

我收集的數據與現實有落差。

so probably it was because of that, I thought, I lived in a bubble,

我的數據樣本既狹隘又偏頗，

and I didn't have enough chances to see outside of it.

也因此我覺得我只活在同溫層，

Now, fast-forward to November 8, 2016

沒機會看到外面的真實情況。

in the United States.

接著快轉到 2016 年 11 月 8 日。

The internet polls,

美國的總統大選。

statistical models,

網路民調、

all the pundits agreeing on a possible outcome for the presidential election.

統計模型、

It looked like we had enough information this time,

專家學者都說希拉蕊會贏。

and many more chances to see outside the closed circle we lived in --

好像這一次我們的資訊很充足，

but we clearly didn't.

而且有更多機會看到， 同溫層以外的世界。

The feeling felt very familiar.

但我們根本沒有。

I had been there before.

這感覺似曾相識。

I think it's fair to say the data failed us this time --

我以前就經歷過。

and pretty spectacularly.

這次真的可以說數據騙了我們，

We believed in data,

而且騙慘了。

but what happened,

我們太相信數據了，

even with the most respected newspaper,

結果呢？

is that the obsession to reduce everything to two simple percentage numbers

連最權威的報紙，

to make a powerful headline

都只想將所有事情

made us focus on these two digits

簡化成兩位數的支持率，

and them alone.

製作出最聳動的標題，

In an effort to simplify the message

讓大眾只看到數字。

and draw a beautiful, inevitable red and blue map,

他們費盡心思簡化資料，

we lost the point completely.

畫出精美的紅藍分布圖，

We somehow forgot that there were stories --

我們完全失去焦點。

stories of human beings behind these numbers.

我們忘記數據背後的故事，

In a different context,

數字背後關於人的故事。

but to a very similar point,

這邊要岔個題，

a peculiar challenge was presented to my team by this woman.

但要說的道理是一樣的，

She came to us with a lot of data,

這名女子向我的團隊 提出了一個特殊的挑戰。

but ultimately she wanted to tell one of the most humane stories possible.

她帶著一堆數據找上我們，

She's Samantha Cristoforetti.

但最終她想要說出的， 就是一個最有人情味的故事。

She has been the first Italian woman astronaut,

這個人就是 薩曼莎‧克里斯托福雷蒂。

and she contacted us before being launched

她是義大利第一位女太空人，

on a six-month-long expedition to the International Space Station.

她在出任務前找上我們，

She told us, "I'm going to space,

她要到國際太空站待六個月。

and I want to do something meaningful with the data of my mission

她告訴我們：「我要上太空了，

to reach out to people."

我想用任務中的數據，

A mission to the International Space Station

和社會大眾交流。」

comes with terabytes of data

一趟國際太空站的任務，

about anything you can possibly imagine --

會有好幾兆位元組的數據，

the orbits around Earth,

你能想到的資料都有：

the speed and position of the ISS

環繞地球的軌道數據、

and all of the other thousands of live streams from its sensors.

國際太空站的速率和位置、

We had all of the hard data we could think of --

還有感應器上一大堆的即時資訊。

just like the pundits before the election --

我們握有太空任務的所有數據，

but what is the point of all these numbers?

專家學者在大選前也都有數據，

People are not interested in data for the sake of it,

但這些數字到底可以做什麼？

because numbers are never the point.

大家對數據本身根本沒興趣，

They're always the means to an end.

因為數字不是重點。

The story we needed to tell

數據只是了解現實的手段。

is that there is a human being in a teeny box

我們要說的故事是，

flying in space above your head,

在這個小箱子裡有個人，

and that you can actually see her with your naked eye on a clear night.

正在你頭上的外太空飛行，

So we decided to use data to create a connection

而且你能在清朗的夜空 用肉眼看見她。

between Samantha and all of the people looking at her from below.

所以我們要用數據創造連結，

We designed and developed what we called "Friends in Space,"

連結薩曼莎和地上的我們。

a web application that simply lets you say "hello" to Samantha

我們設計並開發了 「太空中的朋友」，

from where you are,

它是一個網路應用程式

and "hello" to all the people who are online at the same time

可以讓你從所在地透過網頁，

from all over the world.

跟薩曼莎說「哈囉」，

And all of these "hellos" left visible marks on the map

同時也可以跟線上的 全球網友們說「哈囉」。

as Samantha was flying by

如果薩曼莎經過這些「哈囉」，

and as she was actually waving back every day at us

地圖上就會有記號，

using Twitter from the ISS.

她每天也都從國際太空站，

This made people see the mission's data from a very different perspective.

透過推特跟大家互動。

It all suddenly became much more about our human nature and our curiosity,

這讓大家用非常不同的角度， 去看任務的數據。

rather than technology.

讓一切更貼近人性並 引發我們的好奇心，

So data powered the experience,

而不只是冷冰冰的科技。

but stories of human beings were the drive.

數據能強化體驗，

The very positive response of its thousands of users

但人的故事才是關鍵。

taught me a very important lesson --

數千位使用者的正面回饋，

that working with data means designing ways

給我上了非常重要的一課：

to transform the abstract and the uncountable

與數據為伍就是要設計出

into something that can be seen, felt and directly reconnected

可以把抽象、不可數的概念，

to our lives and to our behaviors,

轉化成看得見、感受得到、

something that is hard to achieve

並直接與生活和行為 重新連結的方法，

if we let the obsession for the numbers and the technology around them

有時候很難做到，

lead us in the process.

如果我們只著迷於數字及科技，

But we can do even more to connect data to the stories they represent.

就會走偏掉。

We can remove technology completely.

但我們能進一步 連結數據與背後的故事。

A few years ago, I met this other woman,

不需要科技也辦得到。

Stefanie Posavec --

幾年前，我遇見一名女子，

a London-based designer who shares with me the passion and obsession about data.

史黛芬妮‧波薩維克。

We didn't know each other,

她是住倫敦的設計師， 跟我一樣對數據癡迷。

but we decided to run a very radical experiment,

我們之前不認識，

starting a communication using only data,

但我們做了一個大膽的實驗，

no other language,

就是只用數據交談，

and we opted for using no technology whatsoever to share our data.

而不是語言。

In fact, our only means of communication

而且不用任何科技當媒介。

would be through the old-fashioned post office.

事實上，我們聯絡的唯一管道，

For "Dear Data," every week for one year,

就是最老派的郵政系統。

we used our personal data to get to know each other --

《親愛的數據》計畫長達一年，

personal data around weekly shared mundane topics,

我們每週透過數據了解對方。

from our feelings

每週都是很普通的一些主題：

to the interactions with our partners,

從各自的情緒、

from the compliments we received to the sounds of our surroundings.

到跟另一半的互動、

Personal information that we would then manually hand draw

收到的讚美或周圍的聲音。

on a postcard-size sheet of paper

這些資訊我們都手繪在

that we would every week send from London to New York,

明信片大小的表格上，

where I live,

每週她會從倫敦寄明信片到

and from New York to London, where she lives.

我住的紐約，

The front of the postcard is the data drawing,

我也從紐約寄到她住的倫敦。

and the back of the card

明信片的正面是手繪的圖表，

contains the address of the other person, of course,

卡片的背面，

and the legend for how to interpret our drawing.

除了對方的地址，

The very first week into the project,

還有前面圖表的註解。

we actually chose a pretty cold and impersonal topic.

計畫開始的第一週，

How many times do we check the time in a week?

我們選了個很生冷、客套主題。

So here is the front of my card,

「我們一週內會看幾次錶？」

and you can see that every little symbol

這是我畫的紀錄，

represents all of the times that I checked the time,

上面的那些小記號，

positioned for days and different hours chronologically --

就是我每次看時間的記錄，

nothing really complicated here.

按照每天、每小時依序紀錄，

But then you see in the legend

其實不會很複雜。

how I added anecdotal details about these moments.

但在註解這邊，

In fact, the different types of symbols indicate why I was checking the time --

我說明了記號的涵義。

what was I doing?

不同的記號代表不同的理由，

Was I bored? Was I hungry?

當時在幹嘛？

Was I late?

無聊嗎？餓了嗎？

Did I check it on purpose or just casually glance at the clock?

遲到了嗎？

And this is the key part --

我是認真看時間， 還是隨意瞄一下？

representing the details of my days and my personality

這些才是關鍵，

through my data collection.

我每天、個性上的細節，

Using data as a lens or a filter to discover and reveal, for example,

透過數據表現出來。

my never-ending anxiety for being late,

把數據當鏡頭或濾鏡，

even though I'm absolutely always on time.

去發現和揭露，比如說，

Stefanie and I spent one year collecting our data manually

就算我一定會準時到， 我仍對遲到這件事非常焦慮，

to force us to focus on the nuances that computers cannot gather --

我們花了一年收集對方的數據，

or at least not yet --

專注在電腦抓不到的細節——

using data also to explore our minds and the words we use,

至少目前還無法收集，

and not only our activities.

用數據去了解想法、用字遣詞，

Like at week number three,

而不只是行為。

where we tracked the "thank yous" we said and were received,

像在第三週，

and when I realized that I thank mostly people that I don't know.

我們記錄了道謝和被道謝情況，

Apparently I'm a compulsive thanker to waitresses and waiters,

才發現我常和不認識的人道謝。

but I definitely don't thank enough the people who are close to me.

顯然我會制式地向服務生道謝，

Over one year,

對身邊的人卻沒那麼客氣。

the process of actively noticing and counting these types of actions

一年以後，

became a ritual.

有意識地關注、記錄這些事，

It actually changed ourselves.

變成了一個習慣。

We became much more in tune with ourselves,

我們開始有些改變。

much more aware of our behaviors and our surroundings.

我們更清楚自己的步調，

Over one year, Stefanie and I connected at a very deep level

更了解自己的行為和周遭環境。

through our shared data diary,

一年後，因為這個計畫，

but we could do this only because we put ourselves in these numbers,

我們兩個有了很深的牽絆。

adding the contexts of our very personal stories to them.

這都是因為我們在數字之外，

It was the only way to make them truly meaningful

加上了自己的故事。

and representative of ourselves.

數據因此有了意義，

I am not asking you to start drawing your personal data,

因此能代表我們。

or to find a pen pal across the ocean.

我不是要大家開始手繪數據，

But I'm asking you to consider data --

或是去找個海外的筆友。

all kind of data --

是希望今後大家面對數據，

as the beginning of the conversation

各式各樣的數據，

and not the end.

都當成對話的開始，

Because data alone will never give us a solution.

而不是終結。

And this is why data failed us so badly --

因為數據本身不會提供解答。

because we failed to include the right amount of context

所以我們才會一直被數據所騙，

to represent reality --

因為我們忘記數據背後

a nuanced, complicated and intricate reality.

所呈現的現實，

We kept looking at these two numbers,

是細微、複雜、盤根錯節的。

obsessing with them

我們看到候選人的支持率，

and pretending that our world could be reduced

就只看到數字，

to a couple digits and a horse race,

假裝我們的世界可以被簡化成

while the real stories,

兩個數字和一場競賽，

the ones that really mattered,

然而真實的故事、

were somewhere else.

真正重要的事，

What we missed looking at these stories only through models and algorithms

卻被拋在一旁。

is what I call "data humanism."

不要只專注在模型和演算法，

In the Renaissance humanism,

也就是所謂的「數據人文主義」。

European intellectuals

在文藝復興人文主義時代，

placed the human nature instead of God at the center of their view of the world.

歐洲的知識分子，

I believe something similar needs to happen

將眼光從「上帝」轉向「人性」。

with the universe of data.

我覺得類似的轉變，

Now data are apparently treated like a God --

也該發生在數據的研究。

keeper of infallible truth for our present and our future.

現在大家都把數據當上帝來拜，

The experiences that I shared with you today

覺得數據是貫通古今的真理。

taught me that to make data faithfully representative of our human nature

我今天跟各位分享的經驗，

and to make sure they will not mislead us anymore,

就是要讓數據去真實呈現人性，

we need to start designing ways to include empathy, imperfection

而不是再次誤導大眾。

and human qualities

我們要將同理心、不完美

in how we collect, process, analyze and display them.

以及人性，

I do see a place where, ultimately,

投入數據的收集、 處裡、分析、呈現。

instead of using data only to become more efficient,

我相信未來有一天，

we will all use data to become more humane.

數據不只讓我們更有效率，

Thank you.

也讓我們更有人情味。

(Applause)

謝謝。