????大數(shù)據(jù)被親切地稱為“新石油”,，并被視為能讓美國日漸衰落的制造業(yè)止住下滑態(tài)勢的重要砝碼。盡管“數(shù)據(jù)是新石油”這個比喻并不完美,，甚至不那么站得住腳（畢竟數(shù)據(jù)數(shù)量極大,，還可以不斷再生），但這個說法還是有它的可取之處,。就像石油在上世紀(jì)初所發(fā)揮的作用一樣,，大數(shù)據(jù)也將推動本世紀(jì)的經(jīng)濟(jì)發(fā)展。只不過它可能并不會像多數(shù)人所設(shè)想的那樣發(fā)揮這種作用,。

????就像石油一樣,，企業(yè)也知道數(shù)據(jù)大量存在，而且光知道它在哪兒是遠(yuǎn)遠(yuǎn)不夠的——要讓這些數(shù)據(jù)產(chǎn)生價值,，就必須提煉,、加工并用合適的形式呈現(xiàn)出來。也和能源經(jīng)濟(jì)一樣,，數(shù)據(jù)經(jīng)濟(jì)也需要全身心奉獻(xiàn)的勞動者——據(jù)一份常被引用的高德納研究公司（Gartner Research）的分析報告稱,，目前僅IT領(lǐng)域就有440萬人這樣一支大軍。

????不過,，這兩者的相似之處也就到此為止了,。石油業(yè)想要找到并培訓(xùn)足夠的勞動力開采石油從來就不用費太大的勁，但要培訓(xùn)嫻熟的大數(shù)據(jù)專業(yè)人才就完全是另一碼事了,。麥肯錫公司（McKinsey & Company）的一份報告預(yù)計,，到2018年僅美國在“具備深入分析能力”的大數(shù)據(jù)專業(yè)人才方面的缺口就在14萬人到18萬人之間——這種人才精通機(jī)器學(xué)習(xí),、統(tǒng)計學(xué)、及/或計算機(jī)科學(xué),，而真正實干的大數(shù)據(jù)人才就是那種知道如何將大量數(shù)據(jù)轉(zhuǎn)化為有意義信息的人,。

????但是，對大數(shù)據(jù)勞動力市場的這一悲觀預(yù)計中常常被忽略的因素是,，大數(shù)據(jù)對就業(yè)的影響遠(yuǎn)比深度分析和IT領(lǐng)域要更深遠(yuǎn),。企業(yè)需要的專業(yè)人才不一定是專攻深度分析專業(yè)的，但必須對大數(shù)據(jù)具有獨特的悟性,。這類人才并不是非要有計算機(jī)科學(xué)或統(tǒng)計學(xué)的學(xué)位不可,。

????管理和技術(shù)咨詢公司博思艾倫咨詢公司（Booz Allen Hamilton）的一位副總裁最近向《信息周刊》（InformationWeek）雜志透露，他們已成功地將物理學(xué)和音樂專業(yè)的人才吸納進(jìn)了數(shù)據(jù)科學(xué)團(tuán)隊——這些人能創(chuàng)造性地思考問題,。他們對計算機(jī)科學(xué)也許知之甚少,，但卻懂得如何運用與眾不同的方法看待大數(shù)據(jù)問題。盡管眾多企業(yè)或各經(jīng)濟(jì)體確實需要數(shù)據(jù)科學(xué)家來管理龐大的數(shù)據(jù)庫,，也需要信息技術(shù)團(tuán)隊提供相應(yīng)支持,，但在更大程度上，他們需要的是知識豐富,、善于創(chuàng)造性思考的專業(yè)人才來最大限度地利用好大數(shù)據(jù)資源,。

????喬治城大學(xué)（Georgetown University）麥克唐納商學(xué)院教授貝奇?佩奇?西格曼博士是一位技術(shù)和信息系統(tǒng)領(lǐng)域的專家，他說：“隨著軟件,、界面設(shè)計及相關(guān)領(lǐng)域的發(fā)展,，今后分析大數(shù)據(jù)會變得更加容易。所以技術(shù)問題不會構(gòu)成太大阻礙,。對企業(yè)來說,，更重要的是要有大量不光是會制作統(tǒng)計圖表和分析表格,，而是會利用手頭信息優(yōu)化決策的人才,。”

????與大數(shù)據(jù)分析廣泛應(yīng)用密切相關(guān)的用人難題將不僅局限于企業(yè)的IT部門或?qū)ＴO(shè)的“數(shù)據(jù)部門”,。同時也不僅僅是像數(shù)據(jù)科學(xué)家和統(tǒng)計學(xué)家這樣的大數(shù)據(jù)專家才能從這股熱潮中獲益,。在以數(shù)據(jù)分析為中心的領(lǐng)域里，如風(fēng)險管理,、市場營銷和研究科學(xué),，與大數(shù)據(jù)有關(guān)的眾多機(jī)會早已獲得充分利用，不過這種應(yīng)用實際上沒有止境,。

????IBM公司的“全球大學(xué)關(guān)系項目”（Global University Relations Programs）總監(jiān),、同時也是計算機(jī)科學(xué)家的吉姆?斯伯熱表示，從學(xué)術(shù)角度看,，在一些本來跟數(shù)據(jù)無緣的學(xué)科里,，比如社會科學(xué)和人文學(xué)科的一些分支,，大數(shù)據(jù)也正在發(fā)揮重要作用。同時,，在醫(yī)藥研究,、各種產(chǎn)品開發(fā)和建模，以及所有研究科學(xué)中,，大數(shù)據(jù)分析也正日益成為不可或缺的角色,。為了保持競爭優(yōu)勢，企業(yè)會要求各級專業(yè)人才充分掌握大數(shù)據(jù)的有關(guān)概念,，同時了解如何充分運用它們,。

????Big data has been favorably cast as "the new oil" and held up as the economic counterweight to America's sinking manufacturing sector. And while the "data is the new oil" analogy isn't perfect or even necessarily sound (data is both abundant and renewable, after all), there's some merit to the metaphor. As oil did at the beginning of the last century, big data is going to drive economies in the century ahead. But it may not do so in the way that many people think it will.

????As with oil, companies know data is out there in large quantities and that it's not enough to simply know where it is -- it has to be extracted, refined, and delivered in a usable format to be valuable. And like the energy economy before it, the data economy needs dedicated people -- 4.4 million of them by 2015 in the IT field alone, according to an oft-cited Gartner Research analysis.

????But here the similarities end. The oil patch has never had much trouble finding and training enough roughnecks to get oil out of the ground, but training up skilled big data professionals is a different enterprise entirely. In the U.S. alone, a McKinsey & Company report projects a shortfall of between 140,000 and 190,000 "deep analytical" big data professionals by 2018 -- that is, people with highly technical skills in machine learning, statistics, and/or computer science, the actual hands-on big data people that know how to crunch huge data sets into meaningful information.

????But what's often overlooked in this dim projection of the big data labor market is that the impact of big data on employment goes far deeper than the deep analytics and IT fields. Companies need professionals at all levels that are not necessarily schooled in deep analytics but are nonetheless big data-savvy. These professionals don't need degrees in computer science or statistics.

????A VP at management consulting and technology advisory outfit Booz Allen Hamilton recently told InformationWeek that the company has had great success bringing physicists and music majors onto data science teams -- creative thinkers who know less about computer science and more about how to look at big data problems in a different way. Though companies and economies will certainly need data scientists to manage their massive databases and information technology teams to support them, to a far greater degree they'll need professionals knowledgeable and creative enough to leverage big data to the greatest possible advantage.

????"Advances in software, in interface design, and things like that will make it easier to analyze big data in the future," says Dr. Betsy Page Sigman, a professor at Georgetown University's McDonough School of Business and an expert on technology and information systems. "So it won't be as big of a technological hurdle. The more important thing for companies will be to have a lot of people that understand not just how to produce statistics and analytics, but understand how to make better decisions because they have this information."

????Any employment bump tied to the proliferation of big data analytics won't be confined to IT departments or even to dedicated "data divisions" that emerge within companies. And it isn't just big data specialists like data scientists and statisticians that stand to benefit from this boom. Big data opportunities are already being exploited in data-centered pursuits like risk management, marketing, and research science, but the applications are virtually limitless.

????Academically, big data is playing a role in decidedly non-data disciplines, like some portions of the social sciences and humanities, says Jim Spohrer, computer scientist and director of IBM's Global University Relations Programs. It will increasingly become integral in medical research, various kinds of product development and modeling, and all types of research science. To remain competitive, companies will require professionals at all levels that fundamentally grasp big data concepts and and know how to use them to their advantage.