????比方說，你需要做出一項重要的戰略決策，而分析師團隊已精心建立了一個復雜的數學模型，它應該會告訴你往哪個方向走。問題是，即使數據科學家按照他們認為簡單明了的表達方式闡述了他們統計算法的詳細情況，你還是一竅不通。

????不要驚慌。在一本題為《跟上量化分析師：了解與使用數據分析的指南》（Keeping Up with the Quants: Your Guide to Understanding + Using Analytics）的新書中，托馬斯?達文波特與合著者金振浩（音譯）試圖就如何明智使用大數據向企業管理人員提供了建議，其中包括提出哪些問題，如何判斷量化分析師是否真正理解他們旨在解決的業務問題。

????麻省理工學院電子商務中心（the MIT Center for Digital Business）研究員、哈佛商學院（Harvard Business School）客座教授達文波特此前還曾出版過兩本有關量化分析的著作。他在這本新書中引用了英國著名統計學家喬治?伯克斯下面這句名言：“所有的（數學）模型都是錯誤的,但其中有些模型是有用的。”更有用的是富有經驗的管理者的直覺。 達文波特寫道：“很少有企業管理人員在分析和直覺兩方面都擅長。因此，我們的目標就是做出善于分析的決策，同時讓管理人員的直覺發揮作用。”

????舉例而言，卡爾?肯普夫就認同這種看法。肯普夫是一位資深科學家，負責領導英特爾（Intel）的一個決策工程部門，他在公司的綽號是超級量化分析師（UberQuant）和首席數學家。即便如此，肯普夫依然認為，良好的量化決策“并不取決于數學。而是取決于決策人員與分析人員之間的合作關系。”達文波特指出：“如果被稱為首席數學家的某個人聲明這并不取決于數學，那么我們應該引起注意。”

????《跟上量化分析師》引人入勝地詳細敘述了英特爾及其他成功的公司——包括威瑞森移動公司（Verizon Wireless）、多倫多道明銀行集團（TD Bank Group）及默克制藥公司（Merck）——如何幫助管理人員和數據科學家加深彼此了解，從而實現有效合作。就英特爾而言，肯普夫把負責解決某個問題而資歷較淺的“數學分析人員”與不擅長數學的人員一起派遣到海外，讓他們聆聽、學習及獲得一些一般商業知識。

????達文波特寫道：“至多，正如新員工那樣，分析人員經過培訓后可以參與業務流程。肯普夫認為，衡量這方面培訓成功的最低標準是數學分析人員自己認為自己理解了業務問題，而最高標準則是企業管理人員認為數學分析人員理解了業務問題。”

????對于企業管理人員而言，他們可能需要復習代數學。達文波特寫道：“比如，企業管理人員不必明白雙曲型偏微分方程。（大家可以松口氣了。）但復習課堂的白板上至少必須有一幅圖表，下面列出這樣的問題：‘由于A和X呈正相關關系，如果A上升，那么X的變化方向是什么？’” 他補充說：“與任何其他類型的模型一樣，幾個具體的例子（無論是歷史、還是預想的例子）非常有用。”諸如餅分圖和條形統計圖等視覺輔助工具也是如此，在默克公司擔任商業分析部門主管的帕特里克?摩爾最喜歡這類工具。

????Let's say you've got a crucial strategic decision to make, and a team of analysts has painstakingly built a complex mathematical model that's supposed to show you which way to go. The trouble is, even after the data scientists have laid out the details of their statistical algorithm in what they think are simple terms, it's Greek to you.

????Don't panic. In a new book called Keeping Up with the Quants: Your Guide to Understanding + Using Analytics, Thomas H. Davenport and co-author Jinho Kim set out to advise executives on how to make sensible use of big data, including which questions to ask and how to tell whether the quant jocks really understand the business problem they're purporting to solve.

????Davenport, a visiting professor at Harvard Business School, a research fellow at the MIT Center for Digital Business, and the author of two previous books about quantitative analysis, quotes eminent British statistician George Box: "All [mathematical] models are wrong, but some are useful." Even more useful is seasoned managers' intuition. "Few executives are skilled at both analytics and intuition," Davenport writes. "The goal, then, is to make analytical decisions while preserving the role of the executive's gut."

????Karl Kempf, for one, agrees. Kempf is a senior scientist who heads a decision engineering group at Intel (INTC), and whose nicknames around the company are UberQuant and Chief Mathematician. Even so, Kempf believes that good quantitative decisions "are not about the math. They're about the relationships." Notes Davenport, "If someone referred to as the Chief Mathematician declares that it's not about the math, we should pay attention."

????Keeping Up with the Quants goes into fascinating detail about how Intel and other successful companies -- including Verizon Wireless (VZ), TD Bank Group (TD), and Merck (MRK) -- help managers and data scientists understand each other well enough to collaborate effectively. In Intel's case, Kempf sends the "math people" charged with solving a problem on a kind of junior year abroad among non-math types, to listen, learn, and pick up some general business knowledge.

????"At most, the analyst can be trained, as a new hire would be, to participate in the business process," Davenport writes. "Kempf judges the low bar for success as when the math person thinks he or she understands the business problem. The high bar is when the business person thinks the math person understands the business problem."

????For their part, executives may need to brush up on their algebra. "The business person doesn't have to understand, for example, hyperbolic partial differential equations," Davenport writes. (Well, there's a relief.) "But at a minimum there has to be a diagram on the white board setting out such questions as, 'Since A and X are related, if A goes up, in what direction does X go?'" He adds, "As with any other type of model, a few concrete examples -- historical or made up -- are extremely useful." So are visual aids like pie charts and bar graphs, a favorite tool of Patrick Moore, who heads the commercial analytics group at Merck.