想像一下以下情景：在會議室裏，CEO正和她的高管團隊成員開例會。CEO分享着她近期在一次全球經濟論壇上聽到的關於技術將影響或威脅商業經營活動的一些熱門觀點。她轉向她的團隊，眼神尤爲深意地落在營銷總監和技術總監身上，問道：“針對這些技術變化，我們的策略是什麼？我們準備好了嗎？”然後在緊接着的幾周或幾個月的時間裏，新任務佈置下來了，新的成員被招聘進來，企業努力去消化技術進步在實際經營活動中的應用，並且學習充分使用它。
1993年，我們談論互聯網。到1997年，數字營銷在美國迅速擴張成5億美元的市場。15年之後，這一市場規模超過100億。
另一個例子是，在20世紀80年代，商品交易數據初被應用，營銷人員憑此能在一定程度上推算產品的銷售量、消費者爲產品支付的價格以及市場份額。而今天，我們對於這些數據習以爲常。回想一下在廣告狂人的時代，由Rober K. Merton和Daniel Yankelovich這類行業領袖所引領的聚焦於消費者羣體和消費者細分市場的營銷變革，我們或許能發現此類事物的本質。
我的觀點是大數據改變的僅僅是商業規律，是由一系列技術變革所帶來的面向消費者行爲的創新營銷。我們對大數據的反應應該就像過去我們對技術變革的反應一樣。而對我來說，無非兩點，即跟上技術變革的節拍、專注於解決核心的商業問題。
跟上技術變革的節拍
這是說大數據不僅僅關於更多的信息和更好的分析方法，而在於它是數據驅動決策的新的行爲範式。人們的決策通過對可獲得的全局數據的挖掘分析而得出。我相信大數據的出現已經引起了各個領域對一手數據、實驗數據、數字化數據、交易數據和非結構化數據的重視。這對大數據的發展是極具意義的。無論數據量的大小，通過收集、整理、處理、解釋數據，並做出決策將成爲一項企業的核心能力。而數據的採集、處理、分析工作必須牢牢以商業決策制定爲中心，不能假借他人之手。各行各業應該跟上大數據變革的節拍，一旦錯過，將在商業競爭被淘汰。
爲了跟上技術的節拍，企業應該怎麼做呢？大型企業如沃爾瑪已經收購了數據分析企業來獲得大量庫存數據分析的能力。當然，也有其他循序漸進的方法。大型銀行通過創新整合內外部數據資源和先進的分析方法能夠顯著地降低信用卡欺詐。手機製造商通過衆包平臺允許用戶參與討論產品，平臺能夠根據文本分析歸納出產品的改進設想。保險公司舉辦了一系列跨職能部門的頭腦風暴會議，來識別大數據能夠應用於解決哪些經營活動的問題等等。
專注於解決核心的商業問題
一旦一個企業敞開懷抱，擁抱數據驅動決策制定的行爲範式，它將從聚焦最重要的事務中獲得最大化的投資回報。大數據通過優化日常營銷活動，爲企業提供了很多新的改良機會，如精準數字廣告、提升電子商務網站的轉化率。但這些都僅僅是冰山一角。企業需要面對的關鍵的問題沒有改變，即理解消費者的需求，提升和改善用戶價值、建立能夠獲得消費者認知的強大品牌、與渠道合作伙伴創建雙贏的互利互惠關係。在諸如此類的環境中，明智地使用數據將爲企業經營者打開新的局面。想想Nike FuelBand的創新，它建立起用戶收集並分享生理活動數據的全新體驗。美國運通融資使用先進分析技術爲有資質的製造商方便快捷地提供生產經營所需的資金。這些遊戲規則的顛覆者正是通過爲企業和消費者創造新的價值而實現這一切的。識別這些新的價值並建立它們應該是數據驅動決策所要關注的最基本的事情。
英語原文：
Imagine for a moment the following scenario: You and your fellow members of the senior leadership team are gathering in the conference room for a regular meeting with the CEO. She starts talking about a recent global economic forum where there was a lot of buzz about a technology that promises—or threatens—to turn the business world upside down. She turns to the group, with particular glances toward the chief marketing and chief technology officers, and asks, “What’s our strategy for dealing with this? Are we ready?” During the next few weeks and months, task forces form, new people are hired, and the organization strains to digest the implications of this technology revolution and make the most of it.
The year: 1993. Because for the moment, I’m not talking about “big data”; I’m talking about the Internet. In 1997, digital advertising in the United States cracked the US$500 million mark. A decade and a half later, it was more than $10 billion.
Here’s another example: When point-of-sale transaction data first became available in the late 1980s, marketers could finally know with some certainty their market shares, the prices consumers were paying, and what percent of sales were on deal—all things we take for granted today. And going back to the Mad Men era, think of the revolution in marketing triggered by pioneers like Robert K. Merton and Daniel Yankelovich when they invented, respectively, focus groups and consumer segmentation.
My point is that big data is just the latest in a series of technology revolutions that have changed the nature of business, in particular customer-facing activities such as innovation and marketing. Our reaction to it should be informed by all we’ve learned from past revolutions, which for me boils down to two main points: Don’t miss the boat, and stay focused on solving core business issues.
The reason I emphasize not missing the boat is that big data isn’t just a matter of more information and better analytics, but a true paradigm shift toward more data-driven decision making. This means extracting insight from the full range of available data. My belief is that the emergence of big data as a major topic is causing increased attention to all kinds of data—including old-fashioned, created data and experiments; digital data; transaction data; and unstructured data. And that’s a good thing. The ability to collect, harmonize, process, interpret, and act on all your data, big and small, will become a core enterprise capability. But it has to live at the center of business decision making—it won’t (and can’t) be relegated to the periphery, performed by insights specialists and third-party vendors. Missing the boat means delaying this inevitable journey.
What can companies do to get on board? The largest and most sophisticated companies, such as Walmart, have actually acquired analytics companies to bring a scalable capability in-house. But there are other, more gradual ways to get started. One large bank used a creative combination of internal and external data sources and advanced analytics to dramatically decrease credit card fraud. A telephone handset manufacturer created a crowdsourcing platform to allow customers to discuss its products, and the platform automatically generated product improvement ideas based on text analysis. An insurance company held a series of daylong cross-functional brainstorming sessions that including marketing, strategy, and IT to identify high-value problems where practical big data solutions could be piloted.
Once a company has embraced data-driven decision making as a new paradigm, it will get the maximum return on its investment by focusing on the most important issues. Big data has gained traction in large part for the many new opportunities it offers to optimize routine marketing activities such as targeting digital ads and improving conversion on e-commerce sites. But these should not be the only, or even the primary, uses of your data. The core issues businesses face haven’t changed: understanding consumer/customer needs, developing and refining value propositions, building strong brands that consumers care about, and creating win-win relationships with channel partners. In these and many other areas, data, wisely used, can open up new markets. Think about innovations such as Nike FuelBand, which creates new consumer experiences by collecting and sharing data about physical activities. Or American Express Merchant Financing, which uses advanced analytics to provide qualified merchants with quick and simple access to cash for their business needs. Game changers like these create value for customers and companies alike. Identifying and building them should be the primary focus of data-driven capabilities.
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