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当下最热门的职业:数据翻译师

Annie Fisher 2019年10月29日

这项工作要求人们将数据科学转化为能够推动变革或解决问题的行动方案。

 
 

数据翻译师这个职业存在着巨大的需求(而且还在不断增长),这项工作要求人们将数据科学转化为能够推动变革或解决问题的行动方案。

美国万通保险这家价值300亿美元的人寿保险公司遇到了一个问题。当时是在2013年,与其他的险企一样,该公司因为骗保而备感苦恼。美国联邦调查局估计,骗保每年让美国保险行业(以及投保人)亏损400亿美元。万通保险的首席技术策略和数据科学负责人西尔斯·梅里特说:“我们不得不大幅改善我们对骗保的实时识别能力。”

万通保险在公司数据科学家和其业务经理之间发起了创新的合作模式,并创建了产品经理这个新职位。产品经理将在数据分析师与负责公司不同业务的日常决策者之间担任沟通工作。一开始,产品经理将从各个部门——人寿、伤残、长期护理等等——搜集信息,然后向数据分析师解释应该通过哪些手段来发现和打击每个领域的骗保行为。数据科学家然后选择和定制相关数据,然后产品经理帮助业务经理将其转化为具体的防骗保举措。

如今,万通保险在全企业采取了这一举措,不仅仅局限于骗保识别。梅里特说:“它适用于每一个流程和每一个业务线,从营销、承保一直到理赔。结果真的非常有效。”

数据科学家和业务经理之间的合作发现了效率低下的领域以及业务增长的新途径。梅里特指出,这一举措将万通保险的营收和利润提升了“数千万美元”,而且产品经理“对于实现这一业绩至关重要。”

数据正在迅猛地渗透各个领域,但经理和高管对于数据的认识并没有跟上其节奏。《哈佛商业评论》指出,这也是为什么数据翻译师成为了一个“必须设置的分析职务”。即便你以前从未听说过数据翻译师这一术语——例如万通保险的产品经理,但他可能已经成为了你的合作伙伴。毫无疑问的是,那些擅长在现实生活中解读数据,并在实际中加以运用的人才当前已经成为了备受追捧的商品。麦肯锡全球研究所预计,到2026年,数据翻译师在美国的需求就将达到200万至400万。

当前,聘请数据翻译师并不容易。部分原因在于这项工作要求雇员拥有独特的组合技能,通常包括扎实的数据科学以及将复杂理念简化为明确、实用选项的才能。全球云软件巨头Infor旗下公司Birst的产品策略副总裁布拉德·斯蒂尔威尔称,这些人如此之稀缺,以至于他们“属于招聘者所称的‘独角兽’门类。”

斯蒂尔威尔在他18年的职业生涯中招聘了多名翻译师。他指出,尽管人工智能可被用于在某些问题上为业务经理提供建议,并回答他们与数据相关的某些问题,但它依然无法取代人类。斯蒂尔威尔说:“这项工作存在一定的艺术性。商业决策通常必须依据不完整的信息,并依靠直觉和创意,而且决策的时间也很紧张。因此理想的翻译师需要同时擅长左脑和右脑思维。”

斯蒂尔威尔说,这也是为什么“在团队中与数据分析师紧密合作的文科毕业生通常会成为翻译大师的原因。一些攻读历史专业的人可能并不知道如何进行线性数据调度,但他们深知如何通过研究历史数据来发现规律,并推断其可能的结果。”

似乎同时具备数学天赋和沟通才能的人才倒也不算太稀缺,然而,最为有效的翻译师还具备另一项能力:全面了解所从事的业务。如果没有这个层面的信息,他们就无法理解业务经理需要从数据中搜集的内容以及原因。拥有以上三方面要素的人才的确是少之又少,所以这里就不光是独角兽那么简单,而是带波尔卡圆点的粉色独角兽。因此,很多公司已经放弃尝试从外部招聘翻译师,而是在公司内部培养。例如,麦肯锡在数年前便推出了内部培养机构,如今每年能够提供约1000名数据翻译师。

万通保险也走上了同样的道路。这家险企在2014年与其马萨诸塞州西部的五家大学合作,共同创建了其数据科学开发项目(DSDP)。该项目的学生会先在包括史密斯学院、芒特·霍利奥克学院和马萨诸塞大学安姆斯特分校在内的几所学校学习为期三年的数据密集型课程,然后加入万通保险担任初级数据科学家,同时就读数据科学研究生课程。新招聘员工会与高级雇员并肩作战,将数据运用至万通保险经理所面临的日常现实世界中的商业挑战。

DSDP项目提供数据科学和数据翻译方面的培训,在任何时候都只有约20名学生和少数研究生就读。负责该项目的梅里特说:“算法只能告诉哪些是你可以解决的商业问题。但人类的判断和直觉则要强大的多,它们能够告诉你应该去解决哪些问题。”

数据背后的故事

在未来,我们所有人可能都得学一些数据翻译技能。Salesforce的首席执行官马克·贝尼欧夫在南希·度尔特的新书《数据故事:通过故事来解释数据和激励行动》(DataStory: Explain Data and Inspire Action Through Story)中说:“我们需要新一代的高管能够了解如何在茫茫数据中进行管理和领导。而且我们还需要新一代的雇员能够帮助我们围绕这些数据来组织和构建我们的业务。” 换句话说,几乎所有的工作都得使用大多数雇员从未用过或需要过的数据翻译技能。

当然,一些人生来就有能力让枯燥的事实变得丰富多彩、引人入胜,尤其是我们中间的那些健谈者。硅谷传播公司Duarte, Inc.的负责人南希·度尔特认为,讲故事——将苍白、枯燥的数据转化为其受众能够轻易理解和记住的生动故事——要比大多数其他翻译技巧更有效,由其是在劝说人们开展某种具体行动的时候。

这是因为,受大脑构造的影响,人类明显更渴望听到带有情节的故事,其中包括开始、过程和结局。她说:“MRI图像展示,相对于对数据的反应,大脑对基于数据的故事的反应要强烈得多。”她还说,数据“在没有人类经验和判断的情况下毫无用处。如果我们仅依靠机器进行决策,那么这些决策都将是错误的。”(财富中文网)

译者:冯丰

审校:夏林

There is a tremendous (and growing) need for data translators, a job that requires people to be able to interpret data science into actions that drive change or fix problems.

MassMutual, a $30 billion per year life insurance company, had a problem. It was 2013 and, along with the rest of the insurance industry, it was bedeviled by fraud. According to FBI estimates, fraud sets the U.S. insurance industry (and policyholders) back by $40 billion a year. “We had to get much better at detecting fraud in real time,” says Sears Merritt, MassMutual’s chief of technology strategy and data science.

So MassMutual launched an innovative collaboration between the company’s data scientists and its line managers. They created a new role, product managers, who act as translators between the data analysts and the day-to-day decision-makers who run the company’s various lines of business. At the outset, the product managers gathered information from each department—life, disability, long-term care, and so on—and explained to data analysts exactly what was needed to spot, and thwart, fraud in each area. The data scientists then culled and customized the relevant numbers, which the product managers helped line managers translate into specific antifraud moves.

Now MassMutual relies on this approach companywide, far beyond fraud detection. It works “in every process, in every line of business from marketing to underwriting to claims,” says Merritt. “The results have been really impactful.”

The collaboration between data scientists and line managers has pinpointed inefficiencies and identified new pathways to growth. That has boosted MassMutual’s revenues and profits by “tens of millions of dollars,” says Merritt, and the product managers “have been crucial to making it happen.”

Data is proliferating at warp speed, but data literacy among managers and executives hasn’t caught up. That’s why data translators have, according to the Harvard Business Review, become a “must-have analytics role.” Even if you’ve never heard the term data translator, you may already be working with one. Because they go by so many different titles—like MassMutual’s product managers—no one knows how many translators exist right now. But there’s no doubt that people who are adept at interpreting data for practical use in the real world are a hot commodity. By 2026, the McKinsey Global Institute predicts that there could be a demand for 2 million to 4 million translators in the U.S. alone.

At the moment, hiring translators isn’t easy. That’s partly because the job requires a unique combination of skills, usually including both a strong grounding in data science and a talent for boiling complex ideas down to clear, practical choices. They’re so rare that translators “belong to a category recruiters call ‘unicorns’,” notes Brad Stillwell, vice president of product strategy at Birst, a unit of global cloud software giant Infor.

Stillwell has hired a number of translators in his 18-year career. He notes that though artificial intelligence can be used to advise line managers on some issues and answer some of their data-related questions, it can’t replace humans. “There is still an art to it,” Stillwell says. “Business decisions often have to be made based on incomplete information, using intuition and creativity, and without much time. So the ideal translator is equally adept at both left- and right-brain thinking.”

That’s why “liberal arts graduates, collaborating closely on a team with data analysts, often make great translators,” Stillwell says. “Someone who majored in history may not know how to do a linear data progression, but they often do know, from studying historical data, how to spot patterns and infer where the data might lead.”

As if a mathematical bent and a knack for communications together weren’t scarce enough, the most effective translators bring with them one more thing: a thorough knowledge of the business they’re working in. Without that level of information, they won’t be able to understand what line managers need to glean from the data and why. People with this trifecta of talents are so scarce—so not just unicorns but pink unicorns with purple polka dots—that many companies have given up trying to hire translators from outside and are training them in-house instead. McKinsey, for instance, launched its own internal academy a few years ago, which now turns out about 1,000 data translators annually.

MassMutual has taken this route, too. The insurer launched its Data Science Development Program (DSDP) in 2014, in partnership with five colleges near its western Massachusetts headquarters. After a data-intensive three-year curriculum at the schools, including Smith, Mount Holyoke, and UMass Amherst, graduates join MassMutual as junior data scientists, while attending grad school in data science at the same time. The new hires work alongside senior colleagues on applying data to the everyday, real-life business challenges that MassMutual line managers face.

The DSDP program, which has about 20 people and a handful of grad students at any given time, offers training in both data science and translation. “Algorithms can only tell you what business problems you can solve,” says Sears Merritt, who runs the program. “But human judgment and intuition can go way beyond that, and tell you what problems you should solve.”

The story behind data

In the years ahead, we may all need to learn some data translation skills. “We need a new generation of executives who understand how to manage and lead through data,” says Salesforce CEO Marc Benioff in Nancy Duarte’s new book, DataStory: Explain Data and Inspire Action Through Story. “And we also need a new generation of employees who are able to help us organize and structure our businesses around that data.” Just about every job, in other words, will call for data-translation skills that most employees haven’t used, or needed, yet.

Of course, some people have a natural ability to render dry facts colorful and interesting, especially the born raconteurs among us. Nancy Duarte, head of Silicon Valley communications firm Duarte, Inc., believes that storytelling—turning plain, eye-glazing heaps of data into a vivid tale its audience can easily grasp and remember—works better than most other translation techniques, especially when it comes to persuading people to take a specific course of action.

That’s because the human brain is apparently hardwired to crave narratives with plots that include a beginning, a middle, and an end. “MRI images show that telling a story based on data lights up the human brain in a way that data alone just can’t match,” she says, adding that data is “useless without human experience and judgment. If we relied solely on machines to make decisions, they’d all be the wrong ones.”

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