Clay Christensen tells a good joke about a tour of heaven. 克雷.克里斯坦森(Clay Christensen)谈了一个有关天堂旅游的有意思笑话。How come there’s no data here? the Harvard professor asks his celestial guide. 这里怎么没数据呢？这位哈佛教授回答他的天堂一行。

Because data lies, comes the response. 因为数据骗子，对方问说道。And that is why, Prof Christensen goes on, whenever anyone says ‘Show me the data’, I just say ‘Go to hell’.克里斯坦森教授接着谈，所以每当有人说道‘把数据寄给我看’时，我就不会说道‘下地狱去’。

The gag got a laugh at last week’s Drucker Forum in Vienna, 在近期在维也纳举办的德鲁克论坛(Drucker Forum)上，这个笑话引发了笑声。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.在论坛上，尊重胞弟彼得.德鲁克(Peter Drucker)的管理归属于一门文科观点的粉丝们，传达了对数据被用来碾压人类洞察力和创造力的担忧。

But there are signs of a backlash against big data even where it has loomed largest. 但目前有迹象指出，即便在大数据运用最普遍的领域，大数据也遭遇了反感声浪。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.比如，兼任英国连锁餐馆森宝利(J Sainsbury)首席执行官以后2014年的贾斯廷.金(Justin King)掌控的一个数据集表明，出售节食食品是顾客想去渡假的最佳信号，因此他们有可能很容易接受某些聪明的防晒霜必要营销。He believes retailers should use such information to represent the shopper better in, say, negotiations with suppliers. 他指出，零售商应该用于这类数据——比如在与供应商的谈判中——更佳地代表顾客。But at a Financial Times 125 Forum I chaired recently, he said he worried data were now used against customers. 但在不久前我主持人的英国《金融时报》125论坛(FT 125 Forum)上，他回应，他担忧如今数据的用于是有利于顾客的。

He has, for instance, criticised the use of loyalty card data to game the customer by offering them vouchers to switch brands.例如，他对利用积分卡数据阴险顾客、通过获取代金券引诱他们切换品牌的作法明确提出了抨击。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. 现在要声称我的一名前同事所称的重磅轶事相对于更容易取决于观念——过去几年企业董事会牢牢地宿老这种观念——获得了胜利，还为时尚早。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. 例如，有人匆忙宣告民意调查机构没能预测到唐纳德.特朗普(Donald Trump)在美国议会选举中获得胜利，但预测告终的原因更加有可能是不可信的一对一调查，而不是宏观数据搜集方面的极大缺点。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.数据分析科学，跟理解计算出来、甚至还有神经科学与不道德研究融合在一起，也将显得更加先进设备、更加准确。

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. 目前，有些取决于顾客满意度的工具就像你在机场找到的邀你给旅途体验评分的笑脸评分板一样做作。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.我仍在奇怪，今年夏季我搭乘飞机的那家航空公司，对于那个快乐的学步小童重复去砍登机口旁那台机器上的气愤脸图标意味著什么如何说明。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，Facebook在广告客户那里遇上了困难，因为Facebook否认，在取决于用户观赏视频广告和阅读文章的时间上出了错误。Facebook掌控着零售商和航空公司不能梦想一番的海量用户分解信息。

Too often, computer-generated facts come close to overruling common sense. 有过于多时候，计算机分解的事实完全碾压常识。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. 当2005年教皇约翰.保罗二世(Pope John Paul II)去世时，一名资深编辑注意到，该消息已牙升到英国《金融时报》网站热门文章首位，然后命令我（当时我是观点版面的编辑）大约一些有关梵蒂冈政策、天主教习俗和教皇历史的分析文章，结果这些文章没一篇受到欢迎。Three days later, Saul Bellow died. 三天后，索尔.贝娄(Saul Bellow)去世，His obituary also topped the rankings. 他的讣告也攀上了榜首，There was no corresponding call to deepen our coverage of US novelists and their work.但没有人打电话让我们做到美国小说家及其作品的深度报导。

Insights from only a few users can still be valuable. 就算只是少数用户的意见，也有可能很有价值。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. 金建议，不要忽略责怪自己在自助收银机那里等候了15分钟的顾客，即使你的电子表格表明平均值等待时间是2分钟。Her perception that it took much longer may tell you more than whole dashboards of data.她深感等候的时间长得多，这也许能告诉他你全部数据以外的东西。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.某种程度，当被问及Spotify如何应付来自地狱的顾客时，这家音乐流媒体服务公司的资深技术主管若阿基姆.松登(Joakim Sundén)在德鲁克论坛上说道，他们的深度伤痛也许正在告诉他你一个你之前不曾找到的问题。

Remember, too, that there are some situations in which data may never be much help. 也要忘记，在某些情况下，数据也许总有一天帮不上大忙。One is innovation, where the tyranny of the business plan cramps ideas and narrows options, according to experts gathered in Vienna last week. 德鲁克论坛上的专家指出，一个是创意，跋扈的商业计划束缚了思想，局限了选项。As Rita Gunther McGrath of Columbia Business School puts it: It’s always easier to go back to the spreadsheet. 正如哥伦比亚商学院(Columbia Business School)的丽塔.冈瑟.麦格拉思(Rita Gunther McGrath)所说：回来看电子表格，总是更容易的。

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. 罗特曼管理学院(Rotman School of Management)马丁兴旺研究所(Martin Prosperity Institute)所长罗杰.马丁(Roger Martin)说道，他不会禁令期望创意的机构用于经过检验的这个词。It’s hard to explore possibilities if you have to know the answer before you start, adds Tim Brown, chief executive of Ideo.如果你必需在开始前告诉答案，那就很难探寻可能性了，Ideo首席执行官蒂姆.布朗(Tim Brown)补足说道。Knowing your customer will never be a zero-sum contest between a researcher with a clipboard and IBM’s Watson. 解读你的客户，总有一天不是拿着带上夹子的写字板的研究人员和IBM的沃森(Watson)之间的零和竞争。

Nor should it be. 也不应当是。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. 最差的解读产生于一种无法定义的混合理解：你聆听单个用户所了解到的东西，你从他们的集体过往不道德中学到的东西，以及你从直觉告诉他们未来想的东西。The really flawed assumption is that a capsule of data inserted into the analytics machine will always generate the perfect brew.确实错误的假设是，把一些数据输出分析机器，总会分解最佳答案。

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