【独家原文翻译56页版】麦肯锡大数据:创新、竞争和生产力的下一个前

大数据：创新、竞争和生产力的下一个前沿（原文翻译）

麦肯锡在2011年5月发布了一个关于大数据方面的报告：《Big data: The next

frontier for innovation, competition, and productivity》，虽然是6年前的报告，但是今天读来，还是非常用指导意义。

报告分为两个版本，一个是概要版20页，一个是完整版156页。

正好最近看了一遍概要版，觉得收益甚大。所以试着翻译一下，仅供参考。

标题：Big data: The next frontier for innovation, competition, and

productivity

译文：大数据：创新、竞争和生产力的下一个前沿

第二页是关于MGI（麦肯锡全球研究院）的介绍，就不翻译了。

略。

Data have become a torrent flowing into every area of the global

economy. 1 Companies churn out a burgeoning volume of transactional

data, capturing trillions of bytes of information about their customers,

suppliers, and operations. millions of networked sensors are being

embedded in the physical world in devices such as mobile phones, smart

energy meters, automobiles, and industrial machines that sense, create,

and communicate data in the age of the Internet of Things. 2 Indeed, as

companies and organizations go about their business and interact with

individuals, they are generating a tremendous amount of digital

“exhaust data,” i.e., data that are created as a by-product of other

activities. Social media sites, smartphones, and other consumer devices

including PCs and laptops have allowed billions of individuals around the

world to contribute to the amount of big data available. And the growing

volume of multimedia content has played a major role in the exponential

growth in the amount of big data (see Box 1, “What do we mean by ‘big

data’?”). Each second of high-definition video, for example, generates

more than 2,000 times as many bytes as required to store a single page

of text. In a digitized world, consumers going about their day—communicating, browsing, buying, sharing, searching— create their own

enormous trails of data.

译文：数据已成为流入全球经济各个领域的激流。公司制造了数量庞大的交易数据，捕获了数万亿字节的有关其客户、供应商和公司运营的信息。数百万的网络传感器被嵌入在诸如移动电话、智能电表、汽车和工业机器等实体设备中，它们在物联网时代感知、创建和传送着数据。事实上，随着公司和组织开展他们的业务并与个人进行互动，他们正在产生大量的“排放数据”，即作为其他活动的副产品而产生的数据。社交媒体、智能手机和其他消费设备，包括PC和笔记本电脑，使世界上数十亿的个人能够贡献大量数据。而且越来越多的多媒体内容在大数据的指数增长中发挥了重要作用（见插文1，“大数据”是什么？）。例如，每秒的高清视频生成的字节数量是存储单页文本所需的2000倍。在数字世界中，消费者每天都在进行通信、浏览、购买、共享和搜索——创建自己巨大的数据流。

Box 1. What do we mean by "big data"?

“Big data” refers to datasets whose size is beyond the ability of typical

database software tools to capture, store, manage, and analyze. This

definition is intentionally subjective and incorporates a moving

definition of how big a dataset needs to be in order to be considered big

data—i.e., we don’t define big data in terms of being larger than a

certain number of terabytes (thousands of gigabytes). We assume that,

as technology advances over time, the size of datasets that qualify as big

data will also increase. Also note that the definition can vary by sector,

depending on what kinds of software tools are commonly available and

what sizes of datasets are common in a particular industry. With those

caveats, big data in many sectors today will range from a few dozen

terabytes to multiple petabytes (thousands of terabytes).

译文：插文1.“大数据”是什么？

“大数据”是指数据量级超过传统数据库软件工具捕获、存储、管理和分析能力的数据集。 这个定义是主观的，并且包含了一个数据集量级的动态定义（超过这个大小才会被认为是大数据）——也就是说，我们没有定义一个确定的值（比如多少TB）。我们认为随着技术的进步，被认定为“大数据”的数据集的大小数量级也将增加。还要注意，这个数据集大小的定义会因行业而异，它取决于这些行业中普遍使用的软件工具不同以及通常的数据集的大小。基于这些认知，今天许多行业的大数据的数据集大小范围将从几十TB到几PB（几千TB）。

In itself, the sheer volume of data is a global phenomenon—but what

does it mean? Many citizens around the world regard this collection of

information with deep suspicion, seeing the data flood as nothing more

than an intrusion of their privacy. But there is strong evidence that big

data can play a significant economic role to the benefit not only of

private commerce but also of national economies and their citizens. Our

research finds that data can create significant value for the world

economy, enhancing the productivity and competitiveness of companies

and the public sector and creating substantial economic surplus for

consumers. For instance, if US health care could use big data creatively

and effectively to drive efficiency and quality, we estimate that the

potential value from data in the sector could be more than $300 billion in

value every year, two-thirds of which would be in the form of reducing

national health care expenditures by about 8 percent. In the private

sector, we estimate, for example, that a retailer using big data to the full

has the potential to increase its operating margin by more than 60

percent. In the developed economies of Europe, we estimate that

government administration could save more than €100 billion ($149

billion) in operational efficiency improvements alone by using big data.

This estimate does not include big data levers that could reduce fraud,

errors, and tax gaps (i.e., the gap between potential and actual tax

revenue).

译文：数据量激增本身是一个全球现象，但它是意味着什么呢？全球范围内有许多人对这种信息收集持深深的怀疑态度，认为数据泛滥只不过是对他们隐私的侵犯。但有证据表明，大数据不仅惠及商业，而且在国民经济及民生方面，都会发挥重要的经济价值。我们的研究发现，数据可以为世界经济创造巨大的价值、提高公司和公共部门的生产力和竞争力，并为消费者创造显著的经济附加值。例如，如果美国医疗保健行业能够创造性地、有效地使用大数据来提高效率和质量，我们估计，该行业从数据获取的潜在价值可能超过每年3000亿美元，其中三分之二将体现在减少了国民医疗保健约8％的支出。在商业世界里，我们估计，例如，充分使用大数据的零售商有可以将其营业利润率增加60％以上。在欧洲发达经济体，我们预计政府行政部门可以通过使用大数据，在运营效率提升上节省1000亿欧元以上（1 940亿美元）。这个估计不包括大数据杠杆带来的收益，比如减少欺诈、错误和税收差距（即潜在税收和实际税收收入之间的差距）。

Digital data is now everywhere—in every sector, in every economy, in

every organization and user of digital technology. While this topic might

once have concerned only a few data geeks, big data is now relevant for

leaders across every sector, and consumers of products and services

stand to benefit from its application. The ability to store, aggregate, and

combine data and then use the results to perform deep analyses has

become ever more accessible as trends such as Moore’s Law in

computing, its equivalent in digital storage, and cloud computing

continue to lower costs and other technology barriers. 3 For less than

$600, an individual can purchase a disk drive with the capacity to store all

of the world’s music. 4 The means to extract insight from data are also

markedly improving as software available to apply increasingly

sophisticated techniques combines with growing computing

horsepower. Further, the ability to generate, communicate, share, and

access data has been revolutionized by the increasing number of people,

devices, and sensors that are now connected by digital networks. In 2010,

more than 4 billion people, or 60 percent of the world’s population,

were using mobile phones, and about 12 percent of those people had

smartphones, whose penetration is growing at more than 20 percent a

year. More than 30 million networked sensor nodes are now present in

the transportation, automotive, industrial, utilities, and retail sectors. The

number of these sensors is increasing at a rate of more than 30 percent a

year.

译文：

现在数字化数据无处不在——存在于每个行业、每个经济体以及每个使用数字技术的组织和用户中。虽然大数据这个话题可能只有一些数据极客在提及，但大数据现在与每个领域的领导者都相关，而且消费者已经从其被应用的产品和服务中受益。

存储、聚合和组合数据，并将其结果进行深入分析的能力，经历着像摩尔定律在计算领域的趋势一样：数字存储介质、云计算持续地在降低成本和技术障碍。不到600美元，你就可以购买一个磁盘用来存储世界上所有的音乐。

从数据中获取洞察的方法也得到显著提升，这得益于应用了复杂技术的软件和强大的计算能力在一起进步。

此外，由于现在通过网络连接的人、设备和传感器的数量的增加，生成、传播、共享和访问数据的能力得到了革命性的提升。2010年，超过40亿人（占世界人口的60％）使用手机，其中约12％的人使用智能手机，其渗透率还在以每年超过20％的速度增长。目前在交通、汽车、工业、公用事业和零售行业有超过3000万个网络传感器节点。这些传感器的数量以每年超过30％的速度增长。

There are many ways that big data can be used to create value across

sectors of the global economy. Indeed, our research suggests that we are

on the cusp of a tremendous wave of innovation, productivity, and

growth, as well as new modes of competition and value capture—all

driven by big data as consumers, companies, and economic sectors

exploit its potential. But why should this be the case now? Haven’t data

always been part of the impact of information and communication

technology? Yes, but our research suggests that the scale and scope of

changes that big data are bringing about are at an inflection point, set to

expand greatly, as a series of technology trends accelerate and converge.

We are already seeing visible changes in the economic landscape as a

result of this convergence.

译文：

有很多方法可以使大数据在全球经济的各行业中创造价值。事实上，我们的研究表明，我们正处在创新、生产效率和经济增长的巨大浪潮中，伴随着新的竞争和价值获取模式——完全由大数据驱动，这些大数据需要消费者、企业和各行业充分开发其潜力。

但为什么是现在这样呢？ 难道数据不是信息、通信技术影响的一部分？

是的，但我们的研究表明，大数据所带来的变化的规模和范围处于一个拐点，随着一系列技术趋势的加速和趋同，它将大大扩展。由于这种趋同，我们已经看到经济格局在明显变化。

Many pioneering companies are already using big data to create value,

and others need to explore how they can do the same if they are to

compete. Governments, too, have a significant opportunity to boost

their efficiency and the value for money they offer citizens at a time

when public finances are constrained—and are likely to remain so due to

aging populations in many countries around the world. Our research

suggests that the public sector can boost its productivity significantly

through the effective use of big data.

译文：

许多先驱公司已经在使用大数据来创造价值，其它的需要探索他们需要如何做才能去竞争。

政府也面临一个重要的机会，在公共财政预算有限的情况下，提高他们的效率并给公民带来的价值，而且很可能由于世界许多国家的人口老龄化，财政预算一直持续吃紧。我们的研究表明，公共事业领域可以通过有效利用大数据来显著提高其生产力。

However, companies and other organizations and policy makers need to

address considerable challenges if they are to capture the full potential

of big data. A shortage of the analytical and managerial talent necessary

to make the most of big data is a significant and pressing challenge and

one that companies and policy makers can begin to address in the near

term. The United States alone faces a shortage of 140,000 to 190,000

people with deep analytical skills as well as 1.5 million managers and

analysts to analyze big data and make decisions based on their findings.

The shortage of talent is just the beginning. Other challenges we explore

in this report include the need to ensure that the right infrastructure is in

place and that incentives and competition are in place to encourage

continued innovation; that the economic benefits to users, organizations,

and the economy are properly understood; and that safeguards are in

place to address public concerns about big data.

译文：

然而，如果公司和其他组织和政策制定者要获取大数据的全部潜力，需要面对相当大的挑战。

能够充分利用大数据的分析和管理人才的短缺是一个重大而紧迫的挑战，公司和政策制定者可能很快就会遇到。仅美国就面临14万至19万具备深厚的分析能力的人才缺口，更不用说还需要150万名管理人员和分析人员要分析数据并根据上述人的发现做出决策。人才短缺只是开始。

我们在本报告中探讨的其他挑战包括：建立相应的基础设施；鼓励持续创新的激励和竞争机制；用户、组织和经济体对于经济效益的正确理解；保障措施到位以应对公众对大数据的顾虑。

This report seeks to understand the state of digital data, how different

domains can use large datasets to create value, the potential value

across stakeholders, and the implications for the leaders of private sector

companies and public sector organizations, as well as for policy makers.

We have supplemented our analysis of big data as a whole with a

detailed examination of five domains (health care in the United States,

the public sector in Europe, retail in the United States, and

manufacturing and personal location data globally). This research by no

means represents the final word on big data; instead, we see it as a

beginning. We fully anticipate that this is a story that will continue to

evolve as technologies and techniques using big data develop and data,

their uses, and their economic benefits grow (alongside associated

challenges and risks). For now, however, our research yields seven key

insights:

译文：

本报告旨在了解大数据的现状，不同领域如何使用大数据来创造价值，对利益相关者的潜在价值，以及对私营公司和公共事业领域的领导人，也就是决策者的影响。

我们对大数据作为一个整体进行了分析，详细分析了五个领域（美国的卫生保健，欧洲的公共事业领域，美国的零售业，全球的制造和个人位置数据）。

这项研究绝不是大数据的盖棺定论，相反它仅仅是一个开始。我们确信，随着使用大数据的技术和方法的发展，以及数据、数据应用、数据应用所带来的经济效益的增长（伴随着挑战和风险），这将是一个持续发展的故事。

就目前来说，我们的研究涵盖了以下七个核心观点：

1. DATA HAVE SWEPT INTO EVERY INDUSTRY AND BUSINESS

FUNCTION AND ARE NOW AN IMPORTANT FACTOR OF

PRODUCTION

Several research teams have studied the total amount of data generated,

stored, and consumed in the world. Although the scope of their

estimates and therefore their results vary, all point to exponential growth

in the years ahead. MGI estimates that enterprises globally stored more

than 7 exabytes of new data on disk drives in 2010, while consumers

stored more than 6 exabytes of new data on devices such as PCs and

notebooks. One exabyte of data is the equivalent of more than 4,000

times the information stored in the US Library of Congress. Indeed, we

are generating so much data today that it is physically impossible to

store it all. Health care providers, for instance, discard 90 percent of the

data that they generate (e.g., almost all real-time video feeds created

during surgery).

译文：

1. 数据已经渗透到每个行业和商业功能之中，是生产的重要因素。

已经有几个研究团队对世界范围内生成、存储和消费的数据总量进行研究。尽管由于研究范围不同，所带来的结果各不相同，但都表明未来会呈指数级增长。MGI估计，2010年，全球企业在磁盘驱动器中存储超过7 EB的新数据，与此同时，消费者在PC和笔记本电脑等设备上存储超过6 EB的新数据。1 EB的数据相当于4000多倍于美国国会图书馆的存储信息。事实上，我们今天所收集的海量数据，物理存储无法实现。例如，医疗保健提供者会丢弃其90%的生成数据（例如，几乎所有在手术期间所创建的实时视频）。

Big data has now reached every sector in the global economy. Like other

essential factors of production such as hard assets and human capital,

much of modern economic activity simply couldn’t take place without it.

We estimate that by 2009, nearly all sectors in the US economy had at

least an average of 200 terabytes of stored data (twice the size of US

retailer Wal-Mart’s data warehouse in 1999) per company with more

than 1,000 employees. Many sectors had more than 1 petabyte in mean

stored data per company. In total, European organizations have about 70

percent of the storage capacity of the entire United States at almost 11

exabytes compared with more than 16 exabytes in 2010. Given that

European economies are similar to each other in terms of their stage of

development and thus their distribution of firms, we believe that the

average company in most industries in Europe has enough capacity to

store and manipulate big data. In contrast, the per capita data intensity

in other regions is much lower. This suggests that, in the near term at

least, the most potential to create value through the use of big data will

be in the most developed economies. Looking ahead, however, there is

huge potential to leverage big data in developing economies as long as

the right conditions are in place. Consider, for instance, the fact that Asia

is already the leading region for the generation of personal location data

simply because so many mobile phones are in use there. More mobile

phones—an estimated 800 million devices in 2010—are in use in China

than in any other country. Further, some individual companies in

developing regions could be far more advanced in their use of big data

than averages might suggest. And some organizations will take

advantage of the ability to store and process data remotely.

译文：

大数据现已覆盖全球经济中的各个部门，一如有形资产和人力资本这样的重要生产要素，现代经济活动根本离不开大数据。

我们估计，到2009年，美国经济中几乎所有领域拥有1000名以上员工规模的企业，平均至少有200 TB的存储数据（这是1999年美国零售商沃尔玛数据仓库规模的两倍）。在许多行业，每个公司的平均存储数据都超过1 PB。总的来说，欧洲组织大约是整个美国存储容量的70％，接近11 EB，而美国在2010年超过16 EB。

鉴于欧洲各经济体发展阶段彼此类似，因此，我们认为，欧洲大多数行业的公司都有足够的能力来存储和操纵大数据。其他地区的人均数据容量相较更低。这表明，至少在短期内，通过大数据创造价值的最大潜在地区是在最发达经济体。

然而长远来看，只要条件成熟，发展中经济体也同样潜力巨大。例如，目前亚洲已经是个人位置数据的领先地区，因为人口众多，移动电话普及。 2010年，手机使用率还会更高——仅仅在中国就预计有8亿台设备。此外，发展中国家的个别公司，在使用大数据方面表现强劲。还有的组织将充分利用远程存储和数据处理能力。

The possibilities of big data continue to evolve rapidly, driven by

innovation in the underlying technologies, platforms, and analytic

capabilities for handling data, as well as the evolution of behavior among

its users as more and more individuals live digital lives.

译文：

由于基础技术、平台和数据分析能力的创新，并且越来越多的用户行为在虚拟的数字世界展现出来，大数据的可能性将持续迅猛发展。

2. BIG DATA CREATES VALUE IN SEVERAL WAYS

We have identified five broadly applicable ways to leverage big data that

offer transformational potential to create value and have implications for

how organizations will have to be designed, organized, and managed.

For example, in a world in which large-scale experimentation is possible,

how will corporate marketing functions and activities have to evolve?

How will business processes change, and how will companies value and

leverage their assets (particularly data assets)? Could a company’s

access to, and ability to analyze, data potentially confer more value than

a brand? What existing business models are likely to be disrupted? For

example, what happens to industries predicated on information

asymmetry—e.g., various types of brokers—in a world of radical data

transparency? How will incumbents tied to legacy business models and

infrastructures compete with agile new attackers that are able to quickly

process and take advantage of detailed consumer data that is rapidly

becoming available, e.g., what they say in social media or what sensors

report they are doing in the world? And what happens when surplus

starts shifting from suppliers to customers, as they become empowered

by their own access to data, e.g., comparisons of prices and quality

across competitors?

译文：

2. 大数据在众多领域创造价值

我们发现了五种广泛适用的途径，通过利用大数据发现潜在的转型可能来创造价值，并对组织如何设计、运营和管理产生重要影响。

例如，在可以开展大规模试验的环境中，企业如何开展营销活动？企业流程如何调整，企业如何评估和利用他们的资产（特别是数据资产）？公司对数据的访问和分析能力能否具有高于品牌的价值？现有的商业模式会被什么取代？

例如，基于信息不对称的行业——例如，各种类型的经纪人——在数据充分透明度的世界，将会有何改变？在原有商业模式和基础架构之上的现任人员，如何与新晋攻击者竞争，这些攻击者能够快速处理并利用详实的消费者数据，例如，他们在社交媒体中说了些什么，他们在使用哪种设备？当大量数据开始从供应商转移到消费者端，消费者可以自主的访问数据，比如对比各竞争对手间的价格和质量，那又会发生什么呢？

Creating transparency

Simply making big data more easily accessible to relevant stakeholders

in a timely manner can create tremendous value. In the public sector, for

example, making relevant data more readily accessible across otherwise

separated departments can sharply reduce search and processing time.

In manufacturing, integrating data from R&D, engineering, and

manufacturing units to enable concurrent engineering can significantly

cut time to market and improve quality.

译文：

创建透明度

使利益相关者的及时访问更加便捷，仅凭这一点就可以创造巨大的价值。例如，在公共部门，使那些独立部门获取数据更为容易，可以大大减少搜索和处理时间。在制造过程中，整合来自研发、工程和制造单元的数据，以实现并行处理，可显著缩短产品推向市场的时间并提高其质量。

Enabling experimentation to discover needs, expose variability, and

improve performance

As they create and store more transactional data in digital form,

organizations can collect more accurate and detailed performance data

(in real or near real time) on everything from product inventories to

personnel sick days. IT enables organizations to instrument processes

and then set up controlled experiments. Using data to analyze variability

in performance—that which either occurs naturally or is generated by

controlled experiments—and to understand its root causes can enable

leaders to manage performance to higher levels.

译文：

通过实验来发现需求，揭示可变性并提高性能

当他们以电子形式创建和存储更多交易数据时，组织可以收集到更为准确和详细的绩效数据（实时或接近实时），从产品库存到员工病假情况。IT信息技术使组织能够使生产过程仪表化，然后设置控制实验。使用数据来分析性能的变化——可能是自然产生的，或通过受控实验产生——究其根因，可以提高领导者的绩效管理水平。

Segmenting populations to customize actions

Big data allows organizations to create highly specific segmentations

and to tailor products and services precisely to meet those needs. This

approach is well known in marketing and risk management but can be

revolutionary elsewhere—for example, in the public sector where an

ethos of treating all citizens in the same way is commonplace. Even

consumer goods and service companies that have used segmentation

for many years are beginning to deploy ever more sophisticated big data

techniques such as the real-time microsegmentation of customers to

target promotions and advertising.

译文：

根据个性化行为，划分客户群

大数据可以使组织进行更为具体的市场细分，精确地定制产品和服务以满足不同需求。这种方法在营销和风险管理中广泛应用，在其他领域则是革命性的——例如，在公共部门，一视同仁则是通常做法。在市场划分方面，即便是颇有渊源

的消费品和服务公司，也开始部署更精密的大数据技术，例如对客户进行实时的微观层面的细分，以准确制定促销活动和广告。

Replacing/supporting human decision making with automated

algorithms

Sophisticated analytics can substantially improve decision making,

minimize risks, and unearth valuable insights that would otherwise

remain hidden. Such analytics have applications for organizations from

tax agencies that can use automated risk engines to flag candidates for

further examination to retailers that can use algorithms to optimize

decision processes such as the automatic fine-tuning of inventories and

pricing in response to real-time in-store and online sales. In some cases,

decisions will not necessarily be automated but augmented by analyzing

huge, entire datasets using big data techniques and technologies rather

than just smaller samples that individuals with spreadsheets can handle

and understand. Decision making may never be the same; some

organizations are already making better decisions by analyzing entire

datasets from customers, employees, or even sensors embedded in

products.

译文：

用自动算法替代/辅助人类决策

精密的分析能够大大改善决策，最大程度地降低风险，并发掘隐含价值。这样的方式适用于税务机构、零售商等各类组织，税务机构可以通过自动风险引擎来识别候选者，以进一步检查，零售商可以通过算法优化决策过程，诸如存货的自动微调和根据商店和网上商店的实时定价调整。有时候，决策不一定是自动化的，而是通过使用大数据技术，分析整个庞大的数据集，而非个人就能解读并处理的电子表格小样本。决策可能各不相同；有的组织已经能够通过对客户、员工，甚至嵌入在产品中的传感器所获取的整个数据集来做出更好的决策。

Innovating new business models, products, and services

Big data enables companies to create new products and services,

enhance existing ones, and invent entirely new business models.

Manufacturers are using data obtained from the use of actual products

to improve the development of the next generation of products and to

create innovative after-sales service offerings. The emergence of

real-time location data has created an entirely new set of location-based

services from navigation to pricing property and casualty insurance

based on where, and how, people drive their cars.

译文：

创造新的模式、产品和服务

大数据使公司能够创造新的产品和服务，改进现有的数据，并发明全新的商业模式。制造商正通过从实际产品的使用表现中获取数据，以改善下一代产品，并提

供富于创新的售后服务产品。实时定位数据的出现，创造了一套全新的基于位置的服务，从导航到财产定价，以及基于何时何地驾驶汽车的伤亡保险赔付。

3. USE OF BIG DATA WILL BECOME A KEY BASIS OF COMPETITION

AND GROWTH FOR INDIVIDUAL FIRMS

译文：

3. 对每个公司而言，大数据应用会成为核心竞争力和增长驱动力

The use of big data is becoming a key way for leading companies to

outperform their peers. For example, we estimate that a retailer

embracing big data has the potential to increase its operating margin by

more than 60 percent. We have seen leading retailers such as the United

Kingdom’s Tesco use big data to capture market share from its local

competitors, and many other examples abound in industries such as

financial services and insurance. Across sectors, we expect to see value

accruing to leading users of big data at the expense of laggards, a trend

for which the emerging evidence is growing stronger. 8

Forward-thinking leaders can begin to aggressively build their

organizations’ big data capabilities. This effort will take time, but the

impact of developing a superior capacity to take advantage of big data

will confer enhanced competitive advantage over the long term and is

therefore well worth the investment to create this capability. But the

converse is also true. In a big data world, a competitor that fails to

sufficiently develop its capabilities will be left behind.

译文：

大数据应用正成为领先企业超越同行的关键手段。例如，我们估计，拥抱大数据的零售商有可能提高60%以上的营业利润率。许多零售巨头，诸如英国Tesco则通过使用大数据从本地竞争对手获取市场份额，金融服务和保险等行业也屡见不鲜。

我们认为，在各个行业，大数据的先驱应用者价值会上升，恰恰以牺牲落后者为代价，这种趋势逐渐被印证。有前瞻性的领导者可以逐步建立强大的数据能力。这项工作需要时间，但利用大数据的发展优势将在长期内显现，无疑是值得投资的。反之亦然，难以充分发挥大数据能力的竞争对手将被甩在后面。

Big data will also help to create new growth opportunities and entirely

new categories of companies, such as those that aggregate and analyze

industry data. Many of these will be companies that sit in the middle of

large information flows where data about products and services, buyers

and suppliers, and consumer preferences and intent can be captured and

analyzed. Examples are likely to include companies that interface with

large numbers of consumers buying a wide range of products and

services, companies enabling global supply chains, companies that

process millions of transactions, and those that provide platforms for

consumer digital experiences. These will be the big-data-advantaged

businesses. More businesses will find themselves with some kind of big

data advantage than one might at first think. Many companies have

access to valuable pools of data generated by their products and services.

Networks will even connect physical products, enabling those products

to report their own serial numbers, ship dates, number of times used,

and so on.

译文：

大数据还有助于创造新的增长机会和全新类型的公司，例如整合和分析行业数据的公司。这些公司大多处于大数据信息流之中，可以获得关于产品和服务的数据，买方和供应商的数据以及消费者偏好。像这样的例子有很多，广泛消费受众的产品和服务公司，拥有全球供应链的公司，处理数百万桩交易的公司，以及消费者数字体验平台等。这些将成为大数据优势行业。越来越多的企业也会发现自身所具有的某种大数据优势。许多公司拥有自身产品和服务所生成的宝贵数据库。甚至可以连接到实物，使产品能够自动报出序列号、船期、使用次数等。

Some of these opportunities will generate new sources of value; others

will cause major shifts in value within industries. For example, medical

clinical information providers, which aggregate data and perform the

analyses necessary to improve health care efficiency, could compete in a

market worth more than $10 billion by 2020. Early movers that secure

access to the data necessary to create value are likely to reap the most

benefit (see Box 2, “How do we measure the value of big data?”). From

the standpoint of competitiveness and the potential capture of value, all

companies need to take big data seriously. In most industries,

established competitors and new entrants alike will leverage data-driven

strategies to innovate, compete, and capture value. Indeed, we found

early examples of such use of data in every sector we examined.

译文：

这些机会中，一部分会产生新的价值来源，另一部分会导致行业内价值的重大调整。例如，到2020年，医疗临床信息提供者将角逐超过100亿美元的市场份额，他们通过整合数据并进行必要的分析以提高医疗保健的效率。早期快速行动者确保拥有有效的数据入口，必将赢得最大的收益（见插文2“如何衡量大数据的价值？”）。

从竞争力和潜在价值的角度来看，所有公司都应该认真对待大数据。大多数行业中，既定的竞争对手和新进入者都将利用数据驱动战略来创新、竞争和赢得价值。事实上，在每个部门我们都找到了类似现象的早期案例。

Box 2. How do we measure the value of big data?

When we set out to size the potential of big data to create value, we

considered only those actions that essentially depend on the use of big

data—i.e., actions where the use of big data is necessary (but usually not

sufficient) to execute a particular lever. We did not include the value of

levers that consist only of automation but do not involve big data (e.g.,

productivity increases from replacing bank tellers with ATMs). Note also

that we include the gross value of levers that require the use of big data.

We did not attempt to estimate big data’s relative contribution to the

value generated by a particular lever but rather estimated the total value

created.

译文：

插文2. 如何衡量大数据的价值？

当我们想量化大数据的潜在价值时，我们只考虑那些基本上依赖于大数据的行业是如何获得价值杠杆的——对于这些行业来说大数据是必要的，但又不完全依赖于此。我们没有把那些只包括自动化但不涉及大数据的杠杆价值计算进来（例如，用ATM代替银行柜员以获得生产力提高）。还要说明，我们计算那些需要使用大数据的杠杆总价值，并没有试图去估计大数据对特定杠杆产生的价值。

4. THE USE OF BIG DATA WILL UNDERPIN NEW WAVES OF

PRODUCTIVITY GROWTH AND CONSUMER SURPLUS

译文：

4. 大数据的应用将产生新的生产率增长点和消费者盈余

Across the five domains we studied, we identified many big data levers

that will, in our view, underpin substantial productivity growth (Exhibit 1).

These opportunities have the potential to improve efficiency and

effectiveness, enabling organizations both to do more with less and to

produce higher-quality outputs, i.e., increase the value- added content

of products and services. 9 For example, we found that companies can

leverage data to design products that better match customer needs.

Data can even be leveraged to improve products as they are used. An

example is a mobile phone that has learned its owner’s habits and

preferences, that holds applications and data tailored to that particular

user’s needs, and that will therefore be more valuable than a new device

that is not customized to a user’s needs. 10 Capturing this potential

requires innovation in operations and processes. Examples include

augmenting decision making—from clinical practice to tax audits—with

algorithms as well as making innovations in products and services, such

as accelerating the development of new drugs by using advanced

analytics and creating new, proactive after-sales maintenance service for

automobiles through the use of networked sensors. Policy makers who

understand that accelerating productivity within sectors is the key lever

for increasing the standard of living in their economies as a whole need

to ease the way for organizations to take advantage of big data levers

that enhance productivity.

译文：

在我们研究的这五个领域有许多大数据杠杆，它们将带来生产力的大幅增长（见图1）。这些机会很有可能提高效率和效用，使组织能够以更少的投入做更多的事情，并获得更高质量的产出，即提高产品和服务的增值价值。

例如，我们发现公司可以利用数据，设计出更有效地匹配客户需求的产品。随着产品的使用，还能通过数据进一步完善产品。例如手机能够获得用户习惯和偏好，

保存适合于该特定用户需要的应用和数据，这样按需定制的产品更有价值。当然这也需要操作和流程的创新。还有通过算法来增强决策的有效性，并在产品和服务方面实现创新——从临床实践到税务审。例如，通过使用高级算法分析加快新药的开发，通过使用网络传感器为汽车用户创造新的、积极的售后服务。加快每个环节的生产率是提高这个经济体整体水平的关键，这为企业提高生产率开启了另一扇门。

We also find a general pattern in which customers, consumers, and

citizens capture a large amount of the economic surplus that big data

enables—they are both direct and indirect beneficiaries of

big-data-related innovation. 11 For example, the use of big data can

enable improved health outcomes, higher-quality civic engagement with

government, lower prices due to price transparency, and a better match

between products and consumer needs. We expect this trend toward

enhanced consumer surplus to continue and accelerate across all sectors

as they deploy big data. Take the area of personal location data as

illustration. In this area, the use of real-time traffic information to inform

navigation will create a quantifiable consumer surplus through savings

on the time spent traveling and on fuel consumption. Mobile

location-enabled applications will create surplus from consumers, too. In

both cases, the surplus these innovations create is likely to far exceed the

revenue generated by service providers. For consumers to benefit, policy

makers will often need to push the deployment of big data innovations.

译文：

我们还发现了一个通用模式，客户、消费者和公民可以通过大数据支持来获得大量经济盈余——它们是大数据相关创新的直接和间接受益者。例如，使用大数据可以改善健康结果，公民可以高效的参与政府决策，价格透明度提高带来价格的降低，以及更好的将产品和消费者需求匹配。我们估计，随着大数据的部署，这一趋势会使消费者获得更多盈余，加快所有行业的发展。以个人位置数据为例。使用实时交通信息导航可以节省旅行时间和燃料消耗，以此创造可量化的消费者剩余。支持移动定位的应用程序也会从用户中获得信息盈余。这两种情况中，这些创新所带来的盈余甚至可能远远超过服务提供商所获得的收入。为了使消费者受益，决策者通常需要推动持续的大数据部署。

图1：大数据能为多个行业获取显著的经济效益

麦肯锡在2011年5月发布了一个关于大数据方面的报告：《Big data: The next

frontier for innovation, competition, and productivity》，这是最后一部分的翻译，欠大家很长时间了，非常抱歉。。。

<续（4）>

5. WHILE THE USE OF BIG DATA WILL MATTER ACROSS SECTORS, SOME

SECTORS ARE POISED FOR GREATER GAINS

译文：

5. 大数据的应用会影响很多行业，一些行业会有更大的收益

Illustrating differences among different sectors, if we compare the

historical productivity of sectors in the United States with the potential of

these sectors to capture value from big data (using an index that

combines several quantitative metrics), we observe that patterns vary

from sector to sector (Exhibit 2).

译文：

各行业有着明显的差异，如果我们对比一下美国这些行业传统的生产力与采用大数据之后的潜力（使用结合了集中量化指标的指数），我们会发现各行业的模式差异巨大（见图2）。

Computer and electronic products and information sectors (Cluster A),

traded globally, stand out as sectors that have already been experiencing

very strong productivity growth and that are poised to gain substantially

from the use of big data. Two services sectors (Cluster B)—finance and

insurance and government—are positioned to benefit very strongly from

big data as long as barriers to its use can be overcome. Several sectors

(Cluster C) have experienced negative productivity growth, probably

indicating that these sectors face strong systemic barriers to increasing

productivity. Among the remaining sectors, we see that globally traded

sectors (mostly Cluster D) tend to have experienced higher historical

productivity growth, while local services (mainly Cluster E) have

experienced lower growth.

译文：

计算机、电子产品和信息技术行业（A组），属于全球贸易，并且已经经历了非常强劲的生产力增长，有望从使用大数据中获益匪浅。

B组中的两个服务行业——金融保险、政府——只要可以克服使用中的障碍，也将受益于大数据。

C组的几个行业，刚经历了生产力的负增长，可能表明这些行业面临着提升生产率的强大系统性障碍。

其他行业中，我们看到全球贸易行业（主要是D组）往往经历较高的历史生产力增长，而本地服务业（主要是E组）则呈现较低增长态势。

While all sectors will have to overcome barriers to capture value from the

use of big data, barriers are structurally higher for some than for others

(Exhibit 3). For example, the public sector, including education, faces

higher hurdles because of a lack of data-driven mind-set and available

data. Capturing value in health care faces challenges given the relatively

low IT investment performed so far. Sectors such as retail, manufacturing,

and professional services may have relatively lower degrees of barriers to

overcome for precisely the opposite reasons.

译文：

虽然所有部门都必须克服使用大数据获取价值的障碍，但一些行业的结构性障碍更高（见图3）。例如，公共事业部门包括教育行业，由于缺乏数据驱动的心态和现有数据，将面临更大的障碍。在医疗保健中获取价值的主要障碍是现有的IT水平较低。恰恰相反的是，零售，制造和专业服务等行业可能会出现相对较低的障碍。

6. THERE WILL BE A SHORTAGE OF TALENT NECESSARY FOR

ORGANIZATIONS TO TAKE ADVANTAGE OF BIG DATA

译文：

6. 大数据需求组织将面临人才短缺

A significant constraint on realizing value from big data will be a

shortage of talent, particularly of people with deep expertise in statistics

and machine learning, and the managers and analysts who know how to

operate companies by using insights from big data.

译文：

限制大数据实现价值的一个显著原因是人才的短缺，特别是在统计和机器学习方面具有深厚造诣的人才，以及知道如何通过使用大数据来运营公司的经理和分析师们。

In the United States, we expect big data to rapidly become a key

determinant of competition across sectors. But we project that demand

for deep analytical positions in a big data world could exceed the supply

being produced on current trends by 140,000 to 190,000 positions

(Exhibit 4). Furthermore, this type of talent is difficult to produce, taking

years of training in the case of someone with intrinsic mathematical

abilities. Although our quantitative analysis uses the United States as

illustration, we believe that the constraint on this type of talent will be

global, with the caveat that some regions may be able to produce the

supply that can fill talent gaps in other regions.

译文：

在美国，我们预计大数据将迅速成为各行业竞争的关键决定因素。但是，我们预测，在大数据行业中对深度分析职位的需求可能远远超过当前的供应量，达到14万至19万个职位（见图表4）。此外，这种人才难以生产，有一定数学能力的基础上，仍需要多年的培训。虽然我们的定量分析使用美国作为例证，但我们认为，这种类型的人才的短缺将是全球性的，有些地区可能能够产生可以填补其他地区人才差距的供应。

In addition, we project a need for 1.5 million additional managers and

analysts in the United States who can ask the right questions and

consume the results of the analysis of big data effectively. The United

States—and other economies facing similar shortages—cannot fill this

gap simply by changing graduate requirements and waiting for people

to graduate with more skills or by importing talent (although these could

be important actions to take). It will be necessary to retrain a significant

amount of the talent in place; fortunately, this level of training does not

require years of dedicated study.

译文：

此外，我们预计在美国需要150万名额外的经理和分析师，他们可以有效地提出正确的问题并消费大数据分析的结果。面对类似人才短缺问题的美国和其他经济体，不能简单地通过改变毕业要求以等待学生以更多的技能毕业，或进口人才

来填补这一空白（尽管这些可能是重要的行动）。有必要重新培养大量的人才; 幸运的是，这一级的培训不需要多年的专门学习。

7. SEVERAL ISSUES WILL HAVE TO BE ADDRESSED TO CAPTURE THE

FULL POTENTIAL OF BIG DATA

译文：

7. 要获取大数据全部潜力需要解决的几个问题

Data policies. As an ever larger amount of data is digitized and travels

across organizational boundaries, there is a set of policy issues that will

become increasingly important, including, but not limited to, privacy,

security, intellectual property, and liability. Clearly, privacy is an issue

whose importance, particularly to consumers, is growing as the value of

big data becomes more apparent. Personal data such as health and

financial records are often those that can offer the most significant

human benefits, such as helping to pinpoint the right medical treatment

or the most appropriate financial product. However, consumers also view

these categories of data as being the most sensitive. It is clear that

individuals and the societies in which they live will have to grapple with

trade-offs between privacy and utility.

译文：

数据政策。随着越来越多的数据被数字化并跨越组织边界，一系列政策问题将变得越来越重要，包括但不限于隐私、安全性、知识产权和责任。显然，隐私是一个问题，特别是对消费者的重要性正在随着大数据的价值增长变得越来越明显。健康和财务记录等个人资料通常是可以提供最重要的福利的资料，例如帮助确定正确的医疗方案或最合适的金融产品。然而，消费者也将这些数据视为最敏感的。很明显，个人和他们所在的社会将必须处理隐私和效用之间的权衡。

Another closely related concern is data security, e.g., how to protect

competitively sensitive data or other data that should be kept private.

Recent examples have demonstrated that data breaches can expose not

only personal consumer information and confidential corporate

information but even national security secrets. With serious breaches on

the rise, addressing data security through technological and policy tools

will become essential.

译文：

另一个需要密切关注的问题是数据安全性，例如，如何保护竞争敏感的数据或其他应该保持私有的数据。最近的例子表明，数据泄露不仅可以暴露个人消费者信息和机密公司信息，甚至暴露国家安全秘密。随着严重违规行为的增多，通过技术和政策工具处理数据安全将变得至关重要。

Big data’s increasing economic importance also raises a number of legal

issues, especially when coupled with the fact that data are fundamentally

different from many other assets. Data can be copied perfectly and easily

combined with other data. The same piece of data can be used

simultaneously by more than one person. All of these are unique

characteristics of data compared with physical assets. Questions about

the intellectual property rights attached to data will have to be answered:

Who “owns” a piece of data and what rights come attached with a

dataset? What defines “fair use” of data? There are also questions

related to liability: Who is responsible when an inaccurate piece of data

leads to negative consequences? Such types of legal issues will need

clarification, probably over time, to capture the full potential of big data.

译文：

大数据日益增长的经济重要性也引起了一些法律问题，尤其是数据与许多其他资产从根本上不同的事实。数据可以完美地复制并轻松地与其他数据结合。相同的数据可以由多个人同时使用。所有这些都是数据与实物资产相比的独特特征。关于数据附带的知识产权的问题必须得到回答：谁拥有这些数据，附带有什么权益？什么定义“合理使用”数据？还有与责任相关的问题：当不准确的数据导致负面后果时，谁负责？这种类型的法律问题随着时间的推移，在获取大数据的全部潜力的同时，将需要澄清。

Technology and techniques. To capture value from big data,

organizations will have to deploy new technologies (e.g., storage,

computing, and analytical software) and techniques (i.e., new types of

analyses). The range of technology challenges and the priorities set for

tackling them will differ depending on the data maturity of the

institution. Legacy systems and incompatible standards and formats too

often prevent the integration of data and the more sophisticated

analytics that create value from big data. New problems and growing

computing power will spur the development of new analytical

techniques. There is also a need for ongoing innovation in technologies

and techniques that will help individuals and organizations to integrate,

analyze, visualize, and consume the growing torrent of big data.

译文：

技术和技能。为了从大数据中获取价值，组织必须部署新技术（如存储，计算和分析软件）和新技能（即新型分析方法）。技术挑战的范围和处理这些挑战的优先级，将根据机构的数据成熟度而有所不同。遗留系统和不兼容的标准和格式，也经常阻碍数据的集成以及大数据创造价值需要的更复杂的分析。新的问题和不断增长的计算能力将刺激新的分析技术的发展。还需要技术和技能的不断创新，帮助个人和组织整合、分析、可视化和消费不断增长的大数据。

Organizational change and talent. Organizational leaders often lack the

understanding of the value in big data as well as how to unlock this value.

In competitive sectors this may prove to be an Achilles heel for some

companies since their established competitors as well as new entrants

are likely to leverage big data to compete against them. And, as we have

discussed, many organizations do not have the talent in place to derive

insights from big data. In addition, many organizations today do not

structure workflows and incentives in ways that optimize the use of big

data to make better decisions and take more informed action.

译文：

组织变革与人才。组织领导者往往缺乏对大数据价值的了解以及如何解锁这个价值。在竞争激烈的行业，这可能被证明是一些公司的阿喀琉斯之踵（唯一致命的弱点），因为自己的竞争对手以及新进入者都有可能利用大数据与他们竞争。而且，正如我们所讨论的，许多组织没有能力从大数据中获得洞见。此外，许多组织今天不会以优化大数据使用的方式构建工作流程和激励措施，从而做出更好的决策并采取更明智的行动。

Access to data. To enable transformative opportunities, companies will

increasingly need to integrate information from multiple data sources. In

some cases, organizations will be able to purchase access to the data. In

other cases, however, gaining access to third-party data is often not

straightforward. The sources of third- party data might not have

considered sharing it. Sometimes, economic incentives are not aligned

to encourage stakeholders to share data. A stakeholder that holds a

certain dataset might consider it to be the source of a key competitive

advantage and thus would be reluctant to share it with other

stakeholders. Other stakeholders must find ways to offer compelling

value propositions to holders of valuable data.

译文：

访问数据。为了实现转型机会，企业将越来越需要整合来自多个数据源的信息。

在某些情况下，组织将能够购买对数据的访问然而，在其他情况下，获得第三方数据的访问通常并不直接。第三方数据的来源可能没有考虑共享。有时，经济激励措施不利于鼓励利益相关者共享数据。持有某一数据集的利益相关者可能认为它是主要竞争优势的来源，因此不愿与其他利益相关者分享。其他利益相关者必须设法为有价值的数据持有人提供引人注目的价值主张。

Industry structure. Sectors with a relative lack of competitive intensity

and performance transparency, along with industries where profit pools

are highly concentrated, are likely to be slow to fully leverage the

benefits of big data. For example, in the public sector, there tends to be a

lack of competitive pressure that limits efficiency and productivity; as a

result, the sector faces more difficult barriers than other sectors in the

way of capturing the potential value from using big data. US health care

is another example of how the structure of an industry impacts on how

easy it will be to extract value from big data. This is a sector that not only

has a lack of performance transparency into cost and quality but also an

industry structure in which payors will gain (from fewer payouts for

unnecessary treatment) from the use of clinical data. However, the gains

accruing to payors will be at the expense of the providers (fewer medical

activities to charge for) from whom the payors would have to obtain the

clinical data. As these examples suggest, organization leaders and policy

makers will have to consider how industry structures could evolve in a

big data world if they are to determine how to optimize value creation at

the level of individual firms, sectors, and economies as a whole.

译文：

行业结构。相对缺乏竞争力和绩效透明度的行业以及利润集中度较高的行业，可能很难充分利用大数据的优势。例如，在公共事业部门，往往缺乏竞争压力，限制了效率和生产力;因此，他们在获取大数据的潜在价值方面，面临比其他行业更难的障碍。美国医疗保健是行业结构如何影响从大数据中提取价值的容易程度的另一个例子。这是一个不仅在成本和质量方面缺乏绩效透明度的行业，而且还是支付者将从使用临床数据中获得的行业结构（从不必要的治疗费用的减少）。然而，付款人所获得的收益将以付款人必须获得临床资料的供应商（较少的医疗费用）为代价。正如这些例子所示，组织领导者和决策者如果要确定如何在个别

公司，部门和整个经济层面优化价值创造，就必须考虑行业结构如何在大数据时代中发展。

The effective use of big data has the potential to transform economies,

delivering a new wave of productivity growth and consumer surplus.

Using big data will become a key basis of competition for existing

companies, and will create new competitors who are able to attract

employees that have the critical skills for a big data world. Leaders of

organizations need to recognize the potential opportunity as well as the

strategic threats that big data represent and should assess and then

close any gap between their current IT capabilities and their data

strategy and what is necessary to capture big data opportunities relevant

to their enterprise. They will need to be creative and proactive in

determining which pools of data they can combine to create value and

how to gain access to those pools, as well as addressing security and

privacy issues. On the topic of privacy and security, part of the task could

include helping consumers to understand what benefits the use of big

data offers, along with the risks. In parallel, companies need to recruit

and retain deep analytical talent and retrain their analyst and

management ranks to become more data savvy, establishing a culture

that values and rewards the use of big data in decision making.

译文：

有效利用大数据有可能转变经济，产生新一轮的生产力增长和消费者剩余。使用大数据将成为现有公司竞争的关键基础，并将创造能够吸引具有大数据时代关键技能的员工的新竞争对手。组织领导者需要认识到潜在的机会以及大数据所代表的应该评估的战略威胁，然后努力缩小目前IT能力、数据战略与获取企业相关的大数据机会的能力之间的差距。他们需要创造性地、积极主动地确定哪些数据池可以组合起来创造价值，以及如何获取这些池，以及解决安全和隐私问题。关于隐私和安全问题，有一部分任务可能包括帮助消费者了解使用大数据提供的好处以及风险。同时，公司需要招聘和留住深层次的分析人才，重新培训分析人员和管理人才，以获得更多的数据能力，建立一种重视和奖励使用大数据进行决策的文化。

Policy makers need to recognize the potential of harnessing big data to

unleash the next wave of growth in their economies. They need to

provide the institutional framework to allow companies to easily create

value out of data while protecting the privacy of citizens and providing

data security. They also have a significant role to play in helping to

mitigate the shortage of talent through education and immigration

policy and putting in place technology enablers including infrastructure

such as communication networks; accelerating research in selected areas

including advanced analytics; and creating an intellectual property