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.
上周我的生活是这样的。 我做了什么, 我跟谁在一起, 清醒时的每个小时我的 主要感受,等等。 我是否在想起刚去世的爸爸时 产生了这感觉, 或者我能否绝对避免担忧和焦虑。 如果你认为我有点着魔, 你应该是对的。 但是很明显,从这个画面中, 你对我的了解会比 从另外的途径了解得多很多, 你可能对这些图片更熟悉, 甚至你的手机里现在就有。 你走路步数的条形图, 你睡眠质量的饼图, 你晨跑的路线。
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.
我的工作是与数据打交道。 我运营一家数据可视化设计公司, 我们设计和开发一些方法, 意图通过视觉表现 使信息容易理解。 多年来我的工作教给我的是, 要真正了解数据及其真实的潜力, 实际上有时我们必须忘掉它们, 反而才能识破它们。 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, 但它们永远不是真实的事物。
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.
但是让我先回溯一下, 回到我个人第一次 明白这道理的时候。 1994年,我13岁, 生活在意大利。 我太年轻了,对政治不感兴趣, 但是我知道有个商人, 西尔维奥·贝卢斯科尼, 正在代表右翼温和派竞选总统。 我们的镇上非常偏向自由党, 而且我父亲是民主党的政客。 我记得没有人认为 贝卢斯科尼可以当选—— 他完全不可能当选。 然而事实相反,他当选了。 我非常清晰地记得那种感觉。 那是个巨大的意外, 因为我爸爸信誓旦旦地说, 他知道我们镇上没人投票给他。
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.
这是第一次 我手里的数据反映出的现实 是完全错误的。 我的数据样本实际上 很有限且有偏向性, 可能是由于我以为 自己生活在一个气泡里, 没有足够的机会看到外面的世界。
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.
现在,快进到2016年11月8日, 在美国。 互联网民意调查、 统计模型、 所有专家对总统选举的 预测结果意见一致。 好像这次我们的信息很充足, 而且有更多机会看到 我们所在的封闭圈以外的世界, 但是很显然,事实并非如此。 那感觉太熟悉了。 我亲身经历过。 我认为可以说这次是 数据让我们失望了, 而且非常严重。 我们相信了数据, 但真正发生的是, 即便是最受尊敬的报纸也只是 痴迷于将所有事情缩减成 两个简单的百分比数字, 用来制作震撼的标题, 让我们聚焦在这两个数字上, 并且只看到这两个数字。 为了简化信息, 画出漂亮的、无法抵御的红蓝地图, 我们完全失去了重点。 我们莫名其妙地忘记了还有故事—— 这些数字背后的人类的故事。
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.
还有一件背景不同 但情况很相似的事件, 这位女士给我的团队 提出了一个特殊的挑战。 她带着很多数据来找我们, 但最终她想要的是 讲一个可能最有人性的故事。 她是萨曼莎·克里斯托维蒂, 意大利第一位女性宇航员, 在出发去国际空间站进行 为期六个月的远征之前, 她联系到我们。 她说:“我要去太空了, 我想用我的任务数据 做些有意义的事, 去联络人们。” 去国际空间站的任务 带着兆兆字节(太字节)的数据, 涉及你能想到的任何事—— 绕地轨道, ISS的速度和位置, 还有另外数千个 来自其传感器的直播流。 我们拥有所有可以想到的硬数据—— 就像那次选举前的专家一样—— 但是这些数字是什么意思? 人们对数据本身不感兴趣, 因为数字永远不是重点。 它们总是用来结束的手段。 我们要讲的故事是, 小箱子里有一个人 正在你头上的太空中飞行 你在晴朗的夜晚能用肉眼看到她。 所以我们决定用数据在萨曼莎和 正从地面看着她的 所有人之间建立一个连接。 我们设计和开发了“太空中的朋友”, 这是一个网络应用程序, 简单地让你从你的位置 对萨曼莎说“你好”, 对世界各地的所有同时在线的人 说“你好”。 所有这些“你好”都能在 萨曼莎飞过的地图上 留下可见的痕迹, 而且她每天都在使用推特 从国际空间站向我们问候。
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.
这使人们看待任务数据的 角度大有不同。 这一切突然变得 更加关乎人性和好奇心, 而不是技术。 所以虽然数据丰富了经历, 但人类的故事才是背后的驱动力。 数千用户的积极回应 教给我非常重要的一点—— 处理数据意味着设计各种方法, 将抽象和无法量化的信息转化成 可以看到、感觉到并 与我们的生活和行为 直接重新连接的东西, 而如果我们让对数字的痴迷和 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。 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。 我们可以完全去掉技术。
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.
几年前,我遇到另一位女士, 斯蒂芬妮·波萨维奇—— 一位伦敦的设计师, 与我一样对数据热爱和痴迷。 我们不认识对方, 但我们决定进行 一个非常激进的实验, 开始一场只使用数据的交流, 不使用任何其他语言, 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 是通过老式邮局。 为了“亲爱的数据”, 一年中的每个星期, 我们用自己的 个人数据来了解彼此—— 个人信息包括每周分享的平凡话题, 从我们自己的感受 到我们与爱人之间的互动, 从我们收到的赞美到周围的声音。 然后我们把这些个人信息 手绘在一张明信片大小的纸上, 每周从伦敦寄到我所在的 纽约, 以及从纽约寄到她所在的伦敦。 明信片的正面是数据图, 卡片背面当然包括 对方的地址, 还有如何破译数据图的方法。 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。 一周内看了多少次时间? 这里是我的卡片的正面, 你可以看到,每一个小符号 代表着我每次看时间, 位置按顺序代表日期和小时—— 没有什么复杂的。 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。 实际上,不同类型的符号 代表着我为什么要看时间—— 当时我在做什么? 我无聊吗?我饿吗? 我迟到了吗? 我是有意看表 还是随意瞥一眼时钟? 但关键是—— 我的数据收集代表了我的 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 我对迟到无休止的焦虑, 即使我绝对每次都准时。
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.
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—— 至少现在还无法收集的细节, 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。 比如在第三周, 我们记录了我们所说的 以及收到的“感谢”, 它让我意识到,我多数时间 在感谢我不认识的人。 显然我对感谢男女服务生有强迫症, 但绝对没有对身边的人 表达足够的感谢。
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.
在这一年多里, 对这些类型的行为 积极留意和计数的过程 成为了一种仪式。 它真的改变了我们自己。 我们变得更加贴近真实的自己, 更加了解我们的行为和周围环境。 一年多的时间,斯蒂芬妮和我 通过共享数据日记 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, 并加入了我们的个人故事背景。 这是使它们真正有意义 并代表了我们自己的唯一途径。
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.
我不是要你开始画你的个人数据, 也不是要你找个跨洋笔友。 但是我请你把数据—— 各种数据—— 看成交谈的开始, 而不是终止。 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—— 因为我们没有考虑到 要用适量的背景信息 来展示现实—— 微妙的、错综复杂的现实。 我们一直盯着这两个数字, 痴迷于这两个数字, 假装我们的世界 可以缩减成两个数字和一场赛马, 而真实的故事, 真正重要的故事 在别处。
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.
如果只用模型和算法 来看待这些故事,我们错过的是 我所说的“数据人文主义”。 在文艺复兴时期的人文主义中, 欧洲的智者们 在他们世界观的中心位置 摆放的是人类本性,而不是上帝。 我相信在数据的世界, 也需要类似的事情。 现在我们显然把数据 当成了一个神—— 我们现在和未来的永恒真理持有者。
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.
我今天分享的经验告诉我, 为了使数据忠实地代表我们的人性, 并确保数据不再误导我们, 我们需要开始设计方法, 在收集、处理、分析和 演示数据时, 纳入同情、不完美和人文素质。 我能预见,终将有个地方, 数据不会被单纯用来提高效率, 我们都会用数据来变得更人性化。
Thank you.
谢谢。
(Applause)
(掌声)