與收集的數(shù)據(jù)一樣，這些目標也非?！按蟆?。毋庸置疑，商業(yè)渴望更好地認識社會。畢竟，與客戶行為及文化相關(guān)的信息不僅是經(jīng)營的關(guān)鍵；在知識經(jīng)濟時代，它們也逐漸成為一種貨幣，用來交換點擊數(shù)、瀏覽量、廣告費，或是更簡單直接的——權(quán)力。在此過程中，倘若谷歌、facebook這類公司能幫助我們不斷地增進對自己的認識，它們便將獲得更大的權(quán)力。問題是聲稱電腦終將組織所有數(shù)據(jù)，或是向我們提供對流感、健康、社交聯(lián)系或任何其他事情的全面認識，這徹底拉低了數(shù)據(jù)和認識的意義。
如果硅谷的大數(shù)據(jù)傳教士們真想“了解世界”，那么他們不僅需要掌握數(shù)據(jù)的量，也要掌握數(shù)據(jù)的質(zhì)。不幸的是，要實現(xiàn)后者，人們要將電腦放下，不僅“從谷歌眼鏡中看世界”（或是從facebook中、從虛擬現(xiàn)實中），還要去體驗真實的世界。這樣做有兩個重要原因。
要了解人，你就要了解他們所處的情境
如果你對一個領(lǐng)域高度熟悉，薄數(shù)據(jù)則是最有用的。你有能力填補信息的不足，設(shè)想到人們?yōu)槭裁催@樣做或為什么有這樣的反應(yīng)——當(dāng)你能想象并重建行為發(fā)生的情境時，薄數(shù)據(jù)便是有意義的。如果不知道情境，想推斷出任何因果關(guān)系或是了解人們的行為動機則是很難實現(xiàn)的。
這也是為什么在科學(xué)實驗中，研究人員需要竭盡全力掌控實驗室環(huán)境的方方面面，以求打造一個人為場所，使各種影響因素都在可計量范圍內(nèi)。不過，真實世界并不是一個實驗室。能確保你對陌生情境有所了解的唯一途徑即是置身其中地去觀察、去內(nèi)化并闡述正在發(fā)生的每一件事。
世上大部分是我們所不知道的隱性知識
如果說大數(shù)據(jù)擅長測量人們的行為，那么它在認識人們?nèi)粘Ｊ挛锏碾[性知識方面則是失敗的。我怎么知道刷牙時該擠多少牙膏？什么時候該并入行車道?眨眼是表示“這東西真有趣”還是“我的眼睛進了東西”？這些都是內(nèi)化的能力、無意識的行為，一種內(nèi)隱的認識在控制著我們的行為。跟身邊的事物一樣，這些不可見的隱性知識只有主動去看，我們才能發(fā)現(xiàn)。不過，它們卻對每個人的行為方式有著重要影響。它能夠解釋事物是怎樣、以哪種意義與我們聯(lián)系起來的。
人類及社會科學(xué)中有一系列俘獲和解釋人的方法，他們所處的情境，他們的隱性知識，而且這些都擁有一個特質(zhì)：它們要求研究者進入雜亂而真實的生活。
沒有哪一個工具可以成為認識人類的快捷方式。盡管硅谷有許多出色的發(fā)明，不過我們對數(shù)字技術(shù)的期望還是要有個限度?！肮雀枇鞲汹厔荨闭嬲探o我們的是：不能僅僅問這個數(shù)據(jù)有多“大”，還要問問這個數(shù)據(jù)有多“厚”。
有時，走進真實的生活將會得到更好的效果。有時，我們必須要離開電腦一會兒。
英語原文：
In a generation, the relationship between the “tech genius” and society has been transformed: from shut-in to savior, from antisocial to society’s best hope. Many now seem convinced that the best way to make sense of our world is by sitting behind a screen analyzing the vast troves of information we call “big data.”
Just look at Google Flu Trends. When it was launched in 2008 many in Silicon Valley touted it as yet another sign that big data would soon make conventional analytics obsolete.
But they were wrong.
IF THE BIG-DATA EVANGELISTS OF SILICON VALLEY REALLY WANT TO “UNDERSTAND THE WORLD” THEY NEED TO CAPTURE BOTH ITS (BIG) QUANTITIES AND ITS (THICK) QUALITIES.
Not only did Google Flu Trends largely fail to provide an accurate picture of the spread of influenza, it will never live up to the dreams of the big-data evangelists. Because big data is nothing without “thick data,” the rich and contextualized information you gather only by getting up from the computer and venturing out into the real world. Computer nerds were once ridiculed for their social ineptitude and told to “get out more.” The truth is, if big data’s biggest believers actually want to understand the world they are helping to shape, they really need to do just that.
It Is Not About Fixing the Algorithm
The dream of Google Flu Trends was that by identifying the words people tend to search for during flu season, and then tracking when those same words peaked in the real time, Google would be able alert us to new flu pandemics much faster than the official CDC statistics, which generally lag by about two weeks.
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For many, Google Flu Trends became the poster child for the power of big data. In their best-selling book Big data: A Revolution That Will Transform How We Live, Work and Think, Viktor Mayer-Sch?nberger and Kenneth Cukier claimed that Google Flu Trends was “a more useful and timely indicator [of flu] than government statistics with their natural reporting lags.” Why even bother checking the actual statistics of people getting sick, when we know what correlates to sickness? “Causality,” they wrote, “won’t be discarded, but it is being knocked off its pedestal as the primary fountain of meaning.”