【商務(wù)英語_閱讀推薦】WhyYourAnalyticsareFailingYou

1、【商務(wù)英語閱讀推薦】why your analytics are failing you michael schrage harvard business review為什么數(shù)據(jù)分析聊勝于無？閱讀中你可能會(huì)遇到的詞匯：institutional ,insti*tju: n i adj.制度的；制度上的；學(xué)會(huì)的 incentive in'sentiv n.動(dòng)機(jī)；刺激adj.激勵(lì)的；刺激的 compliance k m'plai ns n.順從，服從;承諾revolve ri'v iv, -'v :lv vi.旋轉(zhuǎn);循環(huán)出現(xiàn)；反復(fù)考慮vt.使旋轉(zhuǎn);使循環(huán);反復(fù)考慮n

2、.旋 轉(zhuǎn)；循壞；旋轉(zhuǎn)舞臺consensus k n'sens s n一致；輿論；合意many organizations investing millions in big data, analytics, and hiring quants appear frustrated. they undeniably have more and even better data their analysts and analytics are first-rate, too. but managers still seem to be having the same kinds of bus

3、iness arguments and debates except with much better data and analytics the ultimate decisions may be more data-driven but the organizational culture still feels the same. as one cio recently told me, "we're doing analytics in real-time that i could n't eve n have imagi ned five years ag

4、o but it's not havi ng any where n ear the impact i'd have thought/很多公司在大數(shù)據(jù)，數(shù)據(jù)分析，分析人員雇傭上投資數(shù)百萬，卻收獲甚微。無可置疑，公司 掌握的數(shù)據(jù)越來越多，質(zhì)量也在提高；他們的分析師和分析成果都是世界一流的。可是除此 以外，管理者們面對的業(yè)務(wù)難題還是一樣的。最終的決策的確是建立在數(shù)據(jù)基礎(chǔ)之上的，但 組織文化沒有改變。就像-個(gè)首席信息官告訴我的，“目前數(shù)據(jù)分析達(dá)到了五年麗我想都不 敢想的高度，可是其作用卻與我的估計(jì)相距甚遠(yuǎn)。”what gives? after facilitating several

5、 big data and analytics sessions with fortune 1000 firms and spending serious time with organizations that appear quite happy with their returns on analytic investment, a clear "data heuristic" has emerged. companies with mediocre to moderate outcomes use big data and analytics for decisio

6、n support; successful roareturn on analyticsfirms use them to effect and support behavior change. better data-driven analyses are n't simply /zplugged-i n" to existing processes and reviews, they're used to invent and encourage different kinds of conversations and interactions.原因是什么呢？與兒

7、個(gè)財(cái)富1000強(qiáng)金業(yè)合作人數(shù)據(jù)和數(shù)據(jù)分析的經(jīng)歷，加上調(diào)研對分 析投資回報(bào)感到滿意的企業(yè)，我得出了一個(gè)“關(guān)于數(shù)據(jù)的啟發(fā)”。使用大數(shù)據(jù)和數(shù)據(jù)分析用 作決策支持的企業(yè)都成果甚微；而用作影響和支持行為變革的公司都獲得了高數(shù)據(jù)利潤(return on analytics)o只在現(xiàn)有的業(yè)務(wù)和審核屮便用更好的數(shù)據(jù)分析是不夠的，更應(yīng)該通 過數(shù)據(jù)分析去創(chuàng)造和鼓勵(lì)不同類型的對話及互動(dòng)。u we don't do the analytics or business intelligenee stuff until management identifies the behaviors we want to

8、cha nge or in flue nee, ” says one finan cial services cio. "improvi ng compliance and financial reporting is the low-hanging fruit but that just means we're using analytics to do what we are already doing better/“在企業(yè)明確想要改變或影響的行為z前，我們不做任何分析或商業(yè)情報(bào)z類的東西?！?一 個(gè)金融服務(wù)的首席信息官說。“改善合規(guī)報(bào)告和財(cái)務(wù)報(bào)告很容易實(shí)現(xiàn)。但這說明數(shù)

9、據(jù)分析只 是在錦上添花，并沒有發(fā)揮它的潛力?！眛he real challenge is recognizing that using big data and analytics to better solve problems and/or make decisions obscures the organizational reality that new analytics often requires new behaviors people may need to share and collaborate more; functions may need to set up di

10、fferent or complementary business processes; managers and executives may need to make sure existing incentives don't undermine analytic-enabled opportunities for growth and efficiencies.現(xiàn)在的挑戰(zhàn)是需要意識到這樣一個(gè)問題：使用人數(shù)據(jù)和數(shù)據(jù)分析來提高解決問題和（或）決 策能力的共識，便人們忽視了一個(gè)組織現(xiàn)實(shí)新數(shù)據(jù)需要新行為。企業(yè)員工需要進(jìn)一步分 享和合作；企業(yè)職能需要建立不同的或者互補(bǔ)的業(yè)務(wù)流程；企業(yè)管理

11、者和執(zhí)行者需要保證現(xiàn) 有的激勵(lì)機(jī)制不會(huì)影響到數(shù)據(jù)分析帶來的發(fā)展和增效機(jī)遇。for example, at one medical supply company, integrating the analytics around "most profitable customers" and "most profitable products" has required a complete re-education of the account sales and technical support teams both for "upsett

12、ing and "educatidients on higher value-added offerings. the company realized that these analytics shouldrft simply be used to support existing sales and services practices but treated as an opport unity to facilitate a new kind of facilitative and consultative sales and support organization.比如說

13、，一個(gè)藥甜供應(yīng)公司，在融合有關(guān)公司“最大客戶”和“最賺錢的產(chǎn)品”的信息之后， 進(jìn)一步就如何測試及提高客戶對高收益訂單接受能力，對銷售和技術(shù)支持部門進(jìn)行了重新培 訓(xùn)。這個(gè)公司意識到，他所獲得的分析結(jié)果不能只用于支持現(xiàn)有的銷傳和服務(wù)事宜，而更是 一個(gè)機(jī)會(huì)一幫助開發(fā)全新的協(xié)助咨詢式銷售，進(jìn)而使整個(gè)機(jī)構(gòu)受益。the quality of big data and analytics, ironically, mattered less than the purpose to which they were put. the most interesting tensions and arguments

14、 consistently revolved around whether the organization would reap the greatest returns from using analytics to better optimize existing process behaviors or get people to behave differently. but the rough consensus was that the most productive conversations centered on how analytics changed behavior

15、s rather than solved problems.不得不說，相比起大數(shù)據(jù)和數(shù)據(jù)分析的用途來說，其質(zhì)量顯得不那么重要了。目詢最有趣的 矛盾和爭論都圍繞這樣一個(gè)問題，即通過使用分析數(shù)據(jù)，進(jìn)一步優(yōu)化現(xiàn)冇的業(yè)務(wù)行為或者改 變成員的行為，能不能便機(jī)構(gòu)獲得最大收益。但是，關(guān)于數(shù)據(jù)如何改變行為，而不是如何解 決問題，才是最有成果的對話。在這一點(diǎn)上，人們基本達(dá)成了共識?！癿ost people in our organization do better with history lessons than with math lessons, ” one consumer product analyt

16、ics executive told me. "its easier for people to understand how new information and metrics should change how they do things than getting them to understand the underlying algorithms . we've learned the hard way that ,/over-the-wall,/ data and analytics isn't the way for our internal cu

17、stomers to get value from our work."“我們機(jī)構(gòu)里的大多數(shù)人歷史比數(shù)學(xué)學(xué)得好，” 一個(gè)消費(fèi)品分析官告訴我?！叭藗兏菀桌斫?新信息和指標(biāo)如何影響他們做事的方式，而理解信息背后的數(shù)學(xué)問題就很難了我們在這 方面吃過虧，更好的數(shù)據(jù)和分析并不能使內(nèi)部客戶從我們工作屮受益?！眊etting the right answeror even asking the right questionturns out not to be the dominant concern of high roa enterprises the questions, the answersthe data and the analyticsare undeniably important but how those questions, answers and analytics align, or conflict, with individual and i