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