大數據：創新、競爭和生產力的下一個前沿（原文翻譯）（5）

麥肯錫在2011年5月發布了一個關於大數據方面的報告：《Big data: The next frontier for innovation, competition, and productivity》，這是最後一部分的翻譯，欠大家很長時間了，非常抱歉。。。

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5. WHILE THE USE OF BIG DATA WILL MATTER ACROSS SECTORS, SOME SECTORS ARE POISED FOR GREATER GAINS

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5. 大數據的應用會影響很多行業，一些行業會有更大的收益

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).

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各行業有著明顯的差異，如果我們對比一下美國這些行業傳統的生產力與採用大數據之後的潛力（使用結合了集中量化指標的指數），我們會發現各行業的模式差異巨大（見圖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.

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計算機、電子產品和信息技術行業（A組），屬於全球貿易，並且已經經歷了非常強勁的生產力增長，有望從使用大數據中獲益匪淺。

B組中的兩個服務行業——金融保險、政府——只要可以克服使用中的障礙，也將受益於大數據。

C組的幾個行業，剛經歷了生產力的負增長，可能表明這些行業面臨著提升生產率的強大系統性障礙。

其他行業中，我們看到全球貿易行業（主要是D組）往往經歷較高的歷史生產力增長，而本地服務業（主要是E組）則呈現較低增長態勢。

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.

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雖然所有部門都必須克服使用大數據獲取價值的障礙，但一些行業的結構性障礙更高（見圖3）。例如，公共事業部門包括教育行業，由於缺乏數據驅動的心態和現有數據，將面臨更大的障礙。在醫療保健中獲取價值的主要障礙是現有的IT水平較低。恰恰相反的是，零售，製造和專業服務等行業可能會出現相對較低的障礙。

6. THERE WILL BE A SHORTAGE OF TALENT NECESSARY FOR ORGANIZATIONS TO TAKE ADVANTAGE OF BIG DATA

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6. 大數據需求組織將面臨人才短缺

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.

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限制大數據實現價值的一個顯著原因是人才的短缺，特別是在統計和機器學習方面具有深厚造詣的人才，以及知道如何通過使用大數據來運營公司的經理和分析師們。

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.

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在美國，我們預計大數據將迅速成為各行業競爭的關鍵決定因素。但是，我們預測，在大數據行業中對深度分析職位的需求可能遠遠超過當前的供應量，達到14萬至19萬個職位（見圖表4）。此外，這種人才難以生產，有一定數學能力的基礎上，仍需要多年的培訓。雖然我們的定量分析使用美國作為例證，但我們認為，這種類型的人才的短缺將是全球性的，有些地區可能能夠產生可以填補其他地區人才差距的供應。

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.

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此外，我們預計在美國需要150萬名額外的經理和分析師，他們可以有效地提出正確的問題並消費大數據分析的結果。面對類似人才短缺問題的美國和其他經濟體，不能簡單地通過改變畢業要求以等待學生以更多的技能畢業，或進口人才來填補這一空白（儘管這些可能是重要的行動）。有必要重新培養大量的人才; 幸運的是，這一級的培訓不需要多年的專門學習。

7. SEVERAL ISSUES WILL HAVE TO BE ADDRESSED TO CAPTURE THE FULL POTENTIAL OF BIG DATA

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7. 要獲取大數據全部潛力需要解決的幾個問題

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.

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數據政策。隨著越來越多的數據被數字化並跨越組織邊界，一系列政策問題將變得越來越重要，包括但不限於隱私、安全性、知識產權和責任。顯然，隱私是一個問題，特別是對消費者的重要性正在隨著大數據的價值增長變得越來越明顯。健康和財務記錄等個人資料通常是可以提供最重要的福利的資料，例如幫助確定正確的醫療方案或最合適的金融產品。然而，消費者也將這些數據視為最敏感的。很明顯，個人和他們所在的社會將必須處理隱私和效用之間的權衡。

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.

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另一個需要密切關注的問題是數據安全性，例如，如何保護競爭敏感的數據或其他應該保持私有的數據。最近的例子表明，數據泄露不僅可以暴露個人消費者信息和機密公司信息，甚至暴露國家安全秘密。隨著嚴重違規行為的增多，通過技術和政策工具處理數據安全將變得至關重要。

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.

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大數據日益增長的經濟重要性也引起了一些法律問題，尤其是數據與許多其他資產從根本上不同的事實。數據可以完美地複製並輕鬆地與其他數據結合。相同的數據可以由多個人同時使用。所有這些都是數據與實物資產相比的獨特特徵。關於數據附帶的知識產權的問題必須得到回答：誰擁有這些數據，附帶有什麼權益？什麼定義「合理使用」數據？還有與責任相關的問題：當不準確的數據導致負面後果時，誰負責？這種類型的法律問題隨著時間的推移，在獲取大數據的全部潛力的同時，將需要澄清。

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.

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技術和技能。為了從大數據中獲取價值，組織必須部署新技術（如存儲，計算和分析軟體）和新技能（即新型分析方法）。技術挑戰的範圍和處理這些挑戰的優先順序，將根據機構的數據成熟度而有所不同。遺留系統和不兼容的標準和格式，也經常阻礙數據的集成以及大數據創造價值需要的更複雜的分析。新的問題和不斷增長的計算能力將刺激新的分析技術的發展。還需要技術和技能的不斷創新，幫助個人和組織整合、分析、可視化和消費不斷增長的大數據。

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.

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組織變革與人才。組織領導者往往缺乏對大數據價值的了解以及如何解鎖這個價值。在競爭激烈的行業，這可能被證明是一些公司的阿喀琉斯之踵（唯一致命的弱點），因為自己的競爭對手以及新進入者都有可能利用大數據與他們競爭。而且，正如我們所討論的，許多組織沒有能力從大數據中獲得洞見。此外，許多組織今天不會以優化大數據使用的方式構建工作流程和激勵措施，從而做出更好的決策並採取更明智的行動。

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.

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訪問數據。為了實現轉型機會，企業將越來越需要整合來自多個數據源的信息。 在某些情況下，組織將能夠購買對數據的訪問然而，在其他情況下，獲得第三方數據的訪問通常並不直接。第三方數據的來源可能沒有考慮共享。有時，經濟激勵措施不利於鼓勵利益相關者共享數據。持有某一數據集的利益相關者可能認為它是主要競爭優勢的來源，因此不願與其他利益相關者分享。其他利益相關者必須設法為有價值的數據持有人提供引人注目的價值主張。

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.

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行業結構。相對缺乏競爭力和績效透明度的行業以及利潤集中度較高的行業，可能很難充分利用大數據的優勢。例如，在公共事業部門，往往缺乏競爭壓力，限制了效率和生產力;因此，他們在獲取大數據的潛在價值方面，面臨比其他行業更難的障礙。美國醫療保健是行業結構如何影響從大數據中提取價值的容易程度的另一個例子。這是一個不僅在成本和質量方面缺乏績效透明度的行業，而且還是支付者將從使用臨床數據中獲得的行業結構（從不必要的治療費用的減少）。然而，付款人所獲得的收益將以付款人必須獲得臨床資料的供應商（較少的醫療費用）為代價。正如這些例子所示，組織領導者和決策者如果要確定如何在個別公司，部門和整個經濟層面優化價值創造，就必須考慮行業結構如何在大數據時代中發展。

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.

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有效利用大數據有可能轉變經濟，產生新一輪的生產力增長和消費者剩餘。使用大數據將成為現有公司競爭的關鍵基礎，並將創造能夠吸引具有大數據時代關鍵技能的員工的新競爭對手。組織領導者需要認識到潛在的機會以及大數據所代表的應該評估的戰略威脅，然後努力縮小目前IT能力、數據戰略與獲取企業相關的大數據機會的能力之間的差距。他們需要創造性地、積極主動地確定哪些數據池可以組合起來創造價值，以及如何獲取這些池，以及解決安全和隱私問題。關於隱私和安全問題，有一部分任務可能包括幫助消費者了解使用大數據提供的好處以及風險。同時，公司需要招聘和留住深層次的分析人才，重新培訓分析人員和管理人才，以獲得更多的數據能力，建立一種重視和獎勵使用大數據進行決策的文化。

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 framework that encourages innovation. Creative solutions to align incentives may also be necessary, including, for instance, requirements to share certain data to promote the public welfare.

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政策制定者需要認識到利用大數據挖掘經濟下一波增長的潛力。他們需要提供製度框架，允許企業輕鬆創造數據價值，同時保護公民的隱私和提供數據安全。他們還可以通過教育和移民政策幫助緩解人才短缺，建立包括通信網路等基礎設施在內的技術推動力，發揮重要作用;加快對先進分析領域的研究;並創建一個鼓勵創新的知識產權框架。激勵措施的創造性解決方案也可能是必要的，包括例如分享某些數據以促進公共福利的要求。

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