托马斯·H·达文波特【托马斯· H · 达文波特(Thomas H. Davenport)， 哈佛大学商学院访问教授、巴布森学院（Babson College）信息技术与管理学总统杰出奖教授，2003 年，他被《咨询》杂志评为全球“最优秀的25位咨询大师”之一，2005 年被《优化》杂志评为世界商业与技术分析顶级三强之一】
无论是对中国政府，还是就中国的商业组织而言，《大数据》都是一本重要的书。大数据及其分析，将会在未来 10年改变几乎每一个行业的业务功能。任何一个组织，如果早一点着手大数据的工作，都可以获得明显的竞争优势，正如早期在“小数据”时代脱颖而出的竞争者一样，如第一资本金融公司、前进保险公司、万豪酒店等等。时光荏苒，现在到了抓住大数据机遇的时候了。
大数据之所以产生，是因为今天无处不在的传感器和微处理器。我们正在迈进普适计算的时代。其实，所有的机械或电子设备都可以留下数据痕迹，这些痕迹表明了它的性能、位置或状态。这些设备和使用它的人，通过互联网互相交流，又形成了另外一个庞大的数据源。当这些数据和来自其他媒体、无线或有线电话、有线电视、卫星等等来源的数据相结合的时候，更加显得庞大无比。
这些数据可以被使用，这意味着我们可以把所有的商业或组织活动都视为大数据的问题。如今的制造业，大多数机器上都已经安装有一个或多个微处理器，已经进入了大数据的状态。消费营销行业，无数顾客的交易触点和网上点击的流量，也成了大数据的问题。谷歌甚至认为其无人驾驶汽车也是一个大数据的问题。
世界各国的政府也开始认识到，他们坐拥海量数据，这些数据都有待分析。在亚洲国家的政府，也出现了大数据战略以及基于数据分析的方案和倡议。去年，新加坡成立了德勤数据分析研究所（ DAI），这个新的机构是由新加坡政府经济发展委员会资助成立的。德勤数据分析研究所的目标，就是引领政府和企业对于数据的研究和应用。新加坡政府还资助了几所大学开展大数据和数据分析的研究活动。
任何一个组织，要抓住大数据的机遇，就必须做好几个方面的工作。从技术角度来看，首先要收集并且开发特定的工具，来管理大规模并行服务器产生的结构化和非结构化数据，这些数据，可能是自己专有的，也可能来源于“云”。其次，每一个组织都需要选定分析软件，用它来挖掘数据的意义。但可能最重要的是，任何组织都需要人才来管理和分析大数据。这些人被称为“数据科学家”，他们集黑客和定量分析员的优势和特长于一身，非常短缺。聪明的领导人，将想方设法留住这类人才。
不少公司都意识到了这种难得的机遇，现在已经采取了行动。例如，通用电气将投资 15亿美元在旧金山湾区建立一个全球软件和分析中心，作为其全球研发机构的一部分。这个中心拟雇用至少 400名数据科学家，现在已经有 180名各就其位了。通用电气在全球拥有超过 1万名工程师从事软件开发和数据分析工作，通过共同的分析平台、训练、领导力培训以及创新，他们的努力得以协调合作。通用电气对于大数据的研究活动，相当一部分集中在工业产品上，例如机车、涡轮机、喷气发动机以及大型能源发电设施。
对任何一个试图通过大数据获得成功的组织来说，通用电气的投资规模和雄心都是一个榜样。在很多领域，中国政府和中国的企业都有雄心勃勃的计划，这引起了全世界的关注，这些雄心和计划，现在应该拓展到大数据的领域。涂子沛先生的这本书，将在这个重要的领域，为中国政府和企业的努力提供引导和帮助。
Foreword
[达文波特序言英文原文]
This book is an important one for Chinese government and business organizations. Big data and analytics based on it promise to change virtually every industry and business function over the next decade. Any organization that gets started early with big data can gain a signi cant competitive edge. Just as early analytical competitors in the “small data” era (including Capital One bank， Progressive Insurance， and Marriott hotels) moved out ahead of their competitors and built a sizable competitive edge， the time is now for rms to seize the big data opportunity.
The pervasive future of big data is enabled by the pervasive nature of sensors and microprocessors today. We are entering into the ubiquitous computing age now. Virtually every mechanical or electronic device can leave a trail that describes its performance， location， or state. ese devices， and the people who use them， communicate through the Internet—which leads to another vast data source. When all these bits are combined with those from other media—wireless and wired telephony， cable， satellite， and so forth—the future of data appears even bigger.
e availability of all this data means that virtually every business or organizational activity can be viewed as a big data problem or initiative. Manufacturing， in which most machines already have one or more microprocessors， is already a big data situation. Consumer marketing， with myriad customer touchpoints and clickstreams， is already a big data problem. Google has even described the self-driving car as a big data problem.
Governments have begun to recognize that they sit on enormous collections of data that wait to be analyzed. We can see big data and analytics initiatives among governments in Asia. Last year， Singapore helped to launch the Deloitte Analytics Institute (DAI). is new institute is sponsored in part by the Economic Development Board of the Singapore government. e DAI"s goal is to do research and thought leadership on the application of analytics to government and business. Singapore has also sponsored several university-based research initiatives on analytics and big data.
Organizations that want to pursue big data opportunities need to begin working along several fronts. From a technology standpoint， they need to acquire and develop tools to manage both structured and unstructured data in massively parallel server environments， either on premise or in the cloud. ey need to select analytical so ware to make sense of the data. Perhaps most importantly， they need to hire or develop the human talent to manage and analyze big data. ese people are typically known as “ data scientists”— hybrids of hacker and quantitative analyst— and they are in extremely short supply. e wise executive will develop approaches to securing the best people.
Some companies are beginning to realize the extent of the opportunity， and to act upon it now. GE， for example， has committed to spend more than $1.5 billion to develop its Global So ware and Analytics Center in the San Francisco Bay Area as a part of its Global Research organization. e company plans to hire at least 400 computer and data scientists at this location， and has already hired 180. Globally GE has over 10，000 engineers engaged in developing software and analytics products and services， and their efforts will be coordinated through common analytics platforms， training and leadership education， and innovative oerings. A signi cant portion of big data activities at GE will be focused on industrial products， such as locomotives， turbines， jet engines， and large energy generation facilities.
The size and ambition of GE’s commitment should set the tone for other organizations that want to succeed with big data. Chinese government agencies and rms are noted worldwide for their ambitious plans in other domains， and these should be extended to big data. Zipei Tu’s book will help to guide government and business organization’s eorts in this important area.
Thomas H. Davenport
