大數(shù)據(jù)并非萬(wàn)能

Clay Christensen tells a good joke about a tour of heaven. “How come there’s no data here?” the Harvard professor asks his celestial guide. “Because data lies,” comes the response. And that is why, Prof Christensen goes on, “whenever anyone says ‘Show me the data’, I just say ‘Go to hell’.”

克雷•克里斯坦森(Clay Christensen)講了一個(gè)有關(guān)天堂旅游的有趣笑話。“這里怎么沒(méi)有數(shù)據(jù)呢?”這位哈佛教授問(wèn)他的天堂向?qū)А?ldquo;因?yàn)閿?shù)據(jù)撒謊，”對(duì)方回答說(shuō)?？死锼固股淌诮又v，所以“每當(dāng)有人說(shuō)‘把數(shù)據(jù)拿給我看’時(shí)，我就會(huì)說(shuō)‘下地獄去’”。

The gag got a laugh at last week’s Drucker Forum in Vienna, where fans of the late Peter Drucker’s claim that management is a “liberal art” voiced fears about the way data are wielded to crush human insight and inventiveness.

在近期在維也納舉行的德魯克論壇(Drucker Forum)上，這個(gè)笑話引起了笑聲。在論壇上，認(rèn)同已故彼得•德魯克(Peter Drucker)的管理屬于一門“文科”觀點(diǎn)的粉絲們，表達(dá)了對(duì)數(shù)據(jù)被用來(lái)碾壓人類洞察力和創(chuàng)造力的擔(dān)心。

But there are signs of a backlash against big data even where it has loomed largest. As chief executive of UK supermarket chain J Sainsbury until 2014, Justin King commanded a data set that showed, for instance, that purchases of diet products were the best indication that customers were planning to go on holiday — and that they might therefore be open to some deft direct marketing of suntan lotion.

但目前有跡象表明，即便在大數(shù)據(jù)運(yùn)用最廣泛的領(lǐng)域，大數(shù)據(jù)也遭遇了強(qiáng)烈反彈。比如，擔(dān)任英國(guó)連鎖超市森寶利(J Sainsbury)首席執(zhí)行官直至2014年的賈斯廷•金(Justin King)掌握的一個(gè)數(shù)據(jù)集顯示，購(gòu)買減肥食品是顧客打算去度假的最佳信號(hào)，因此他們可能很容易接受某些精明的防曬霜直接營(yíng)銷。

He believes retailers should use such information to represent the shopper better in, say, negotiations with suppliers. But at a Financial Times 125 Forum I chaired recently, he said he worried data were now used against customers. He has, for instance, criticised the use of loyalty card data to “game the customer” by offering them vouchers to switch brands.

他認(rèn)為，零售商應(yīng)當(dāng)使用這類數(shù)據(jù)——比如在與供應(yīng)商的談判中——更好地代表顧客。但在不久前我主持的英國(guó)《金融時(shí)報(bào)》125論壇(FT 125 Forum)上，他表示，他擔(dān)心如今數(shù)據(jù)的使用是不利于顧客的。例如，他對(duì)利用積分卡數(shù)據(jù)“算計(jì)顧客”、通過(guò)提供代金券誘使他們轉(zhuǎn)換品牌的做法提出了批評(píng)。

It is too soon to declare the triumph of what one ex-colleague used to call “big anecdote” over the ideology of easy-to-measurism that has held boardrooms in thrall for the past few years. For example, the hastily declared failure of pollsters to predict a Donald Trump victory in the US election is more likely to be due to unsound one-on-one surveys than yawning deficiencies in wider data-gathering. The science of data analytics, when combined with cognitive computing and even neuroscientific and behavioural research, is also going to get more sophisticated and precise.

現(xiàn)在要宣稱我的一名前同事所稱的“重磅軼事”相對(duì)于“易于衡量”觀念——過(guò)去幾年企業(yè)董事會(huì)牢牢奉行這種觀念——取得了勝利，還為時(shí)尚早。例如，有人倉(cāng)促宣布民意調(diào)查機(jī)構(gòu)未能預(yù)測(cè)到唐納德•特朗普(Donald Trump)在美國(guó)大選中獲勝，但預(yù)測(cè)失敗的原因更有可能是不可靠的一對(duì)一調(diào)查，而不是宏觀數(shù)據(jù)收集方面的巨大缺點(diǎn)。數(shù)據(jù)分析科學(xué)，跟認(rèn)知計(jì)算、甚至還有神經(jīng)科學(xué)與行為研究結(jié)合在一起，也將變得更先進(jìn)、更精確。

For now, some of the tools measuring customer satisfaction are as blunt as those smiley-face pads you find at airports, asking you to assess your experience. I still wonder how the airline I flew with last summer interpreted the input from the cheerful toddler who was repeatedly stabbing the angry-face icon on the machine at our departure gate.

目前，有些衡量顧客滿意度的工具就像你在機(jī)場(chǎng)發(fā)現(xiàn)的邀請(qǐng)你給旅途體驗(yàn)打分的笑臉打分板一樣生硬。我仍在好奇，今年夏季我乘坐飛機(jī)的那家航空公司，對(duì)于那個(gè)開(kāi)心的學(xué)步小童反復(fù)去戳登機(jī)口旁那臺(tái)機(jī)器上的憤怒臉圖標(biāo)意味著什么如何解釋。

Separately, Facebook — whose access to vast user-created troves of information retailers and airlines can only dream about — has got into trouble with its advertising customers after admitting mistakes measuring the time users spend viewing video advertisements and articles.

另外，F(xiàn)acebook在廣告客戶那里遇到了麻煩，因?yàn)镕acebook承認(rèn)，在衡量用戶觀看視頻廣告和閱讀文章的時(shí)間上出了錯(cuò)誤。Facebook掌握著零售商和航空公司只能夢(mèng)想一番的海量用戶生成信息。

Too often, computer-generated “facts” come close to overruling common sense. When Pope John Paul II died in 2005, a senior editor noted that the news had surged to the top of the FT website’s most-read stories and ordered me (I was then editing our opinion pages), to commission insights into Vatican policies, Catholic mores and papal history — none of which was a hit. Three days later, Saul Bellow died. His obituary also topped the rankings. There was no corresponding call to deepen our coverage of US novelists and their work.

有太多時(shí)候，計(jì)算機(jī)生成的“事實(shí)”幾乎碾壓常識(shí)。當(dāng)2005年教皇約翰•保羅二世(Pope John Paul II)去世時(shí)，一名資深編輯注意到，該消息已猛升至英國(guó)《金融時(shí)報(bào)》網(wǎng)站熱門文章首位，然后命令我(當(dāng)時(shí)我是觀點(diǎn)版面的編輯)約一些有關(guān)梵蒂岡政策、天主教習(xí)俗和教皇歷史的分析文章，結(jié)果這些文章沒(méi)有一篇受到追捧。三天后，索爾•貝婁(Saul Bellow)去世，他的訃告也登上了榜首，但沒(méi)人打電話讓我們做美國(guó)小說(shuō)家及其作品的深度報(bào)道。

Insights from only a few users can still be valuable. Mr King advises against ignoring the shopper who complains she waited 15 minutes at the self-service tills, even if your spreadsheet shows the average wait was two minutes. Her perception that it took much longer may tell you more than whole dashboards of data.

就算只是少數(shù)用戶的意見(jiàn)，也可能很有價(jià)值。金建議，不要忽視抱怨自己在自助收銀機(jī)那里等待了15分鐘的顧客，即使你的電子表格顯示平均等待時(shí)間是2分鐘。她感到等待的時(shí)間長(zhǎng)得多，這或許能告訴你全部數(shù)據(jù)以外的東西。

Similarly, asked what Spotify would do with the “customers from hell”, Joakim Sundén, senior tech leader at the music streaming service, told the Drucker Forum that their “deep pain” might be telling you about a problem you had not identified.

同樣，當(dāng)被問(wèn)到Spotify如何應(yīng)對(duì)“來(lái)自地獄的顧客”時(shí)，這家音樂(lè)流媒體服務(wù)公司的資深技術(shù)主管若阿基姆•松登(Joakim Sundén)在德魯克論壇上說(shuō)，他們的“深度痛苦”或許正在告訴你一個(gè)你之前未曾發(fā)現(xiàn)的問(wèn)題。

Remember, too, that there are some situations in which data may never be much help. One is innovation, where the tyranny of the business plan cramps ideas and narrows options, according to experts gathered in Vienna last week. As Rita Gunther McGrath of Columbia Business School puts it: “It’s always easier to go back to the spreadsheet.” Roger Martin, who heads the Rotman management school’s Martin Prosperity Institute, says he would ban the word “proven” from organisations that wish to innovate. “It’s hard to explore possibilities if you have to know the answer before you start,” adds Tim Brown, chief executive of Ideo.

也要記住，在某些情況下，數(shù)據(jù)或許永遠(yuǎn)幫不上大忙。德魯克論壇上的專家認(rèn)為，一個(gè)是創(chuàng)新，專橫的商業(yè)計(jì)劃束縛了思想，局限了選項(xiàng)。正如哥倫比亞商學(xué)院(Columbia Business School)的麗塔•岡瑟•麥格拉思(Rita Gunther McGrath)所說(shuō)：“回去看電子表格，總是更容易的。”羅特曼管理學(xué)院(Rotman School of Management)馬丁繁榮研究所(Martin Prosperity Institute)所長(zhǎng)羅杰•馬丁(Roger Martin)說(shuō)，他會(huì)禁止希望創(chuàng)新的機(jī)構(gòu)使用“經(jīng)過(guò)驗(yàn)證的”這個(gè)詞。“如果你必須在開(kāi)始前知道答案，那就很難探索可能性了，”Ideo首席執(zhí)行官蒂姆•布朗(Tim Brown)補(bǔ)充說(shuō)。

Knowing your customer will never be a zero-sum contest between a researcher with a clipboard and IBM’s Watson. Nor should it be. The best insights come from some hard-to-define blend of what you know from listening to individual users, what you can learn from their collective past behaviour and what you intuit they will want in future. The really flawed assumption is that a capsule of data inserted into the analytics machine will always generate the perfect brew.

理解你的客戶，永遠(yuǎn)不是拿著帶夾子的寫字板的研究人員和IBM的沃森(Watson)之間的零和競(jìng)爭(zhēng)。也不應(yīng)該是。最好的理解產(chǎn)生于一種難以定義的混合認(rèn)知：你傾聽(tīng)單個(gè)用戶所了解到的東西，你從他們的集體過(guò)往行為中學(xué)到的東西，以及你從直覺(jué)知道他們未來(lái)想要的東西。真正錯(cuò)誤的假設(shè)是，把一些數(shù)據(jù)輸入分析機(jī)器，總會(huì)生成最佳答案。