????“嗯,，為了證明,，人們不惜編造數(shù)據(jù)。14%的人都知道這一點(diǎn),?！薄缎疗丈患摇坊裟?辛普森語(yǔ)

????電腦天才們宣稱，大數(shù)據(jù)時(shí)代已經(jīng)來(lái)臨,。

????電腦已經(jīng)強(qiáng)大到可以收集,、匯總數(shù)以兆兆字節(jié)計(jì)的信息來(lái)回答各種問(wèn)題，從如何安排員工薪酬待遇,，到某支抵押貸款債券的風(fēng)險(xiǎn)有多大,，無(wú)所不包。

????雖然數(shù)據(jù)不會(huì)說(shuō)謊,，但人們使用數(shù)據(jù)的方式卻極為主觀,。量化分析在2007年的金融危機(jī)中起到了推波助瀾的作用，但是如果企業(yè)只是簡(jiǎn)單地認(rèn)為,，光靠一屋子擺弄數(shù)據(jù)的分析師就可以解決問(wèn)題,，那么結(jié)果不僅會(huì)對(duì)他們的資產(chǎn)損益造成損害，同時(shí)也會(huì)損害他們的企業(yè)文化和員工的福利,。

????企業(yè)執(zhí)行委員會(huì)（Corporate Executive Board）的執(zhí)行理事施維坦克?沙表示：“數(shù)據(jù)可以幫助人們做出決策,，但如果覺(jué)得所有重要的決策都可以交給電腦那就錯(cuò)了?！逼髽I(yè)執(zhí)行委員會(huì)最近出版了一份調(diào)查報(bào)告,，名為《超越洞察力赤字：大數(shù)據(jù)時(shí)代的大判斷》（Overcoming the Insight Deficit: Big Judgment in an Era of Big Data.）。報(bào)告指出,，認(rèn)為只要有10個(gè)分析師，就能解決公司所有數(shù)據(jù)問(wèn)題的想法是錯(cuò)誤的,。

????未來(lái)那些最擅于利用數(shù)據(jù)分析來(lái)引導(dǎo)決策的企業(yè)將獲得許多競(jìng)爭(zhēng)優(yōu)勢(shì),，對(duì)于這一點(diǎn)沒(méi)什么人會(huì)表示懷疑。不過(guò)施維坦克?沙表示,，僅僅擁有數(shù)據(jù)是不夠的,。根據(jù)企業(yè)執(zhí)行委員會(huì),，在一份針對(duì)4,941人進(jìn)行的調(diào)查中，只有38%的員工稱得上是“消息靈通的懷疑主義者”,，他們依賴數(shù)據(jù),，但并不盲從，既不害怕置疑數(shù)據(jù)分析的結(jié)果,，也敢于從他人那里索要反饋,。43%的員工對(duì)數(shù)據(jù)堅(jiān)信不疑。還有19%的員工很少相信數(shù)據(jù)分析,，喜歡憑直覺(jué)做事,。

????施維坦克?沙認(rèn)為：“我們必須面對(duì)這樣一個(gè)事實(shí)：我們的教育系統(tǒng)沒(méi)有教會(huì)我們?nèi)绾斡行У胤治鰯?shù)據(jù)。我們向受訪者展示了一些圖表,，問(wèn)他們這些圖表代表什么意思,。結(jié)果就連那些從常青藤名校畢業(yè)的學(xué)生也很難搞清這些數(shù)據(jù)究竟代表了什么?！边@種教育的缺失并非沒(méi)有辦法彌補(bǔ),。該研究顯示，僅僅向?qū)W生提供培訓(xùn),，教給他們分析工具和軟件的使用方法還不夠,，更重要的是教會(huì)他們?nèi)绾闻c數(shù)據(jù)互動(dòng)。換句話說(shuō),，就是如何批判性地思考,。

????要想讓員工成為“消息靈通的懷疑主義者”，企業(yè)可以建立一種數(shù)據(jù)導(dǎo)向型的文化（但并不是成為數(shù)據(jù)的奴隸）,，從CEO開(kāi)始自上而下地推廣,。如果CEO身體力行，其他員工也很可能參與進(jìn)來(lái),。

????施坦維克?沙表示,，另一種有效的方法就是在雇傭數(shù)據(jù)分析師的時(shí)候，不僅要考慮他們的分析能力,，還要考慮他們是否有能力和意愿去向其他人解釋這些數(shù)據(jù)代表了什么意思,。“招聘分析師時(shí)還得考慮指導(dǎo)技能,，”他說(shuō),。“每一個(gè)分析師都能改善幾十,、甚至幾百個(gè)人的決策能力,。”

????葛蘭素史克制藥公司（GlaxoSmithKline）正在用數(shù)據(jù)分析來(lái)挑戰(zhàn)一些人們認(rèn)為無(wú)可辯駁的常理。該公司北美制藥部的IT高級(jí)副總裁喬?托伊介紹道,，葛蘭素史克正在利用數(shù)據(jù)分析來(lái)重新設(shè)計(jì)銷售運(yùn)作,，把這項(xiàng)過(guò)去完全依賴人脈的業(yè)務(wù)轉(zhuǎn)變成一項(xiàng)依賴數(shù)據(jù)的業(yè)務(wù)。

????"Oh, people can come up with statistics to prove anything. Fourteen percent of people know that." – Homer Simpson

????The era of big data is here, the nerds proclaim.

????Computers are powerful enough to gather and synthesize terabytes of information to answer questions ranging from how best to compensate employees to how risky is that mortgage-backed security.

????But while the numbers don't lie, how people use them is extremely subjective. Quantitative analysis played a part in the financial crisis of 2007, after all, and companies that think a room full of analysts crunching numbers can solve their problems can do damage to not only their profits and losses but also to their corporate culture and employee well-being.

????"Making the decision at the end of the day can be aided by data, but the thought that computers will make all the important decisions is just not true," says Shvetank Shah, executive director of the Corporate Executive Board (CEB), which recently published a study titled Overcoming the Insight Deficit: Big Judgment in an Era of Big Data. "Saying that I've got 10 quant jocks who are going to solve all my data problems is the wrong way to go about it."

????There is little argument that many competitive advantages in the future will go to those who most effectively use analytics to guide decisions. Having the data, though, is not enough, Shah says. According to the CEB, only 38% of employees in a 4,941-person study were considered "informed skeptics" who rely on data but not so much that they are afraid to question the results and solicit feedback from others. The rest of the workforce either trust data without question (43% of the study participants) or rarely trust analysis and prefer to go with their gut (19%).

????"We have to face up to the fact that our education system isn't preparing us to analyze data effectively," Shah says. "We showed people graphs and asked them what they mean, and even folks with Ivy League educations struggled to make sense of the data."

????There are ways to bridge the education gap, though. Shah's research has shown that training is ineffective when students are told to focus on the analytic tool or software being used. Instead, they should be taught how to interact with the data or, in other words, how to think critically.

????One way for a company to ensure that they turn their employees into "informed skeptics" is to work on creating a data-driven (but not data-enslaved) culture, starting with the CEO on down. If the chief executive is on board, others will likely join.

????Another effective method, according to Shah, is to hire quants not only for their analytic abilities, but also for their ability and willingness to explain what they do to others. "You've got to hire quants for their coaching skills," he says. "Each quant can improve the decision making capabilities of 10s or 100s of others."

????GlaxoSmithKline (GSK) is using analytics to roast several sacred cows, says Joe Touey, senior vice president in IT at its North America Pharmaceuticals division. The company has embraced data analysis to redesign its sales operations, moving what was once a field based solely on relationships to one that's now based on data.