如何在人群中认出一定会创业的人

If Target can figure out a teen girl was pregnant before her father did, venture firms should be able to identify founders before they start companies. All it takes is the right data.
既然塔吉特（Target）可以先于少女的父亲知道她已经怀孕，那么，风险投资公司也应该有能力在初创公司诞生之前，便锁定潜在的创始人。所需要的只是适当的数据而已。

That’s where venture capital—ever evolving—is headed. As Mark Susterpointed out last week, the venture capital landscape has become increasingly bifurcated. Seed funds are springing up everywhere, representing 67% of all new funds created, and large funds have gotten even larger. For the early stage investors, this means increased competition and frothy valuations. By the time a founder sets out to raise a seed round, the startup’s valuation might be $10 million.
这正是不断演进的风险投资行业未来的发展方向。正如马克•苏斯特上周指出的那样，风险投资行业的两极分化日益加剧。一方面，种子基金如雨后春笋般崛起，占新建基金总数的67%，另一方面，大型基金则变得日益庞大。对于早期投资者而言，这意味着竞争日益激烈，进而导致估值泡沫。等到一位公司创始人开始进行种子期融资时，其公司的估值可能已经达到1,000万美元。

One way to get around that is to invest even earlier. Invest before the company is a company. Before the founder even knows they’re a founder. Bloomberg Beta, the venture investment arm of Bloomberg LP, has been doing this for a year now.
要避免这种情况，方法之一是将投资的节点提前。在公司还未诞生之前就进行投资。甚至连创始人自己都不知道他们会进行创业的时候，便提前开始“烧冷灶”。彭博资讯（Bloomberg LP）旗下的创投基金Bloomberg Beta已经花了一年时间这么做了。

After an unsuccessful attempt to build a database of “future founders” on its own, the firm teamed up with Mattermark, the deal intelligence company founded by Danielle Morrill. The results could have ramifications for the way investment decisions, typically driven by gut instinct and intuition, are made.
该基金曾尝试独自建立“未来创始人”数据库，但以失败告终，因此，它决定与丹尼尔•莫里尔创建的交易情报公司Mattermark合作。其研究结果可能对投资决策方式产生深远影响。目前的投资决策通常均基于投资者的本能和直觉。

Mattermark identified the most likely career paths of successful founders, creating a pool of 1.5 million people who were connected by one to two degrees of separation to tech startups, but were not founders yet. By analyzing the people that started companies over nine months, Mattermark mapped out the strongest predictors of starting a company: a person’s education, which previous companies they’ve worked for and how senior they were, their geography, and their age. The goal was to find things that didn’t fit the standard path to entrepreneurship. As Morrill points out: “Anything that looks like a pattern, people will already find it.”
Mattermark确定了成功的创始人最有可能的职业发展路径，并创建了一个150万人的数据库，由距离科技创业公司1-2个维度的人组成，但还不是创始人。通过分析八个月内创建公司的人，Mattermark先标出了确定一个人是否会创建公司最强有力的预测因素：一个人所接受的教育；他们之前工作的公司与所达到的职务级别；地理位置和年龄。这么做的目标是找出那些不符合标准创业路径的东西。正如莫里尔指出的：“凡是看起来成型的东西，那肯定已经有人找到它了。”

The resulting mix of people were older but less senior than you’d expect. Almost 40% of those in the dataset were over 40 years old. Almost half of the people in the data set had worked for a VC-backed company, but two thirds were not in senior leadership positions. Management consultants were twice as likely to start companies. Bloomberg Beta narrowed the list to 350 potential founders, and invited them to parties in New York and San Francisco.
最终结果显示，创业者的年龄要高于预期，但从事的职位没有达到预计高度。数据库内的群体，约40%超过了40岁。约有一半的人曾在有风投注资的公司工作，但有三分之二的人未从事过高级管理职务。管理咨询顾问创业的几率是其他职业的两倍。Bloomberg Beta最终锁定了350名潜在创始人，并邀请他们前往纽约和旧金山参加聚会。

Cold-emailing people based on data could feel like a creepy invasion of privacy, like Target’s maternity ads. Hi, our algorithm knows your career dreams! Indeed, some people thought it was a scam. But for the self-selecting group of around 75 people that turned up at each party, it was validating.
根据数据贸然发送陌生邮件，感觉像是赤裸裸的侵犯个人隐私，正如塔吉特的孕妇广告一样。嗨，我们的算法能预测到你的职业梦想！事实上，确实有人认为这是诈骗。但对于自愿参加了两次聚会的75人来说，这是对他们的一次检验。

“People would say things like, ‘I thought about becoming a founder but I had never even told anyone,’” Morrill says. “When someone believes in you before anyone else—that’s what is really cool here . . . You can actually reinforce a dream they held very closely but never considered seriously.” Morrill admitted that telling people they were in the study probably changes the results.
莫里尔说道：“人们会这样说：‘我想过创业，但我从来没有告诉过任何人。’在所有人都毫无察觉的时候，有人便选择相信你——这种事真的很酷……他们虽然一直坚持自己的梦想但从未认真考虑过，而你的信任可以强化他们的梦想。”莫里斯承认，告诉人们他们被研究选中，可能会改变最终的结果。

Roy Bahat, who leads Bloomberg Beta, was pleased by the diversity of the group. “The data doesn’t discriminate,” he says. “A lot of the people, this was the first time they ever got tapped on the shoulder for something like this.”
Bloomberg Beta负责人罗伊•巴哈特对于最终结果的多样性感到欣慰。他说：“数据不会有任何偏见。其中很多人有生以来第一次被赋以这样的期望。”

Whether any of Bloomberg Beta’s potential founders have actually founded a company yet is another story. (It’s only been a few months; Bahat says “a bunch” are in the process.) Likewise, the project has not resulted in any deals for Bloomberg Beta. (“It was expected to be a long term process of getting to know people, so even if we fund zero people for the next two years, that’s fine by me,” he says.) But using data creatively to get a leg up on deal flow will only become more common. Mattermark re-ran a blind version of its study and found its model has a 25x better chance of predicting a founder.
Bloomberg Beta找出的“潜在创始人”以后是否会创建公司，这还有待考证。（虽然仅仅过去几个月时间，但巴哈特表示“一大批人”已经开始了创业。）同样，该项目也没有给Blommberg Beta带来任何交易。（他说道：“了解一个人是一个长期的过程，因此，即使在未来两年我们没有对任何人进行投资，我也可以接受。”）但通过创造性地使用数据，在交易流程中占据先机，这种做法将变得更为常见。Mattermark重新进行了一次匿名研究，结果发现，其模型预测创始人的成功率比先前高出25倍。

This is one way to boost venture investing with data. Another way? Add a robot to your board of directors, like Deep Knowledge Ventures, a firm in Hong Kong. The firm’s robot board member uses machine learning to predict the best life sciences deals, taking historical data sets to reveal trends that aren’t so obvious to human VC investors. As senior partner Dmitry Kaminskiyexplained to Betabeat, the robot takes emotion out of the process:
这是利用数据促进风险投资的方式之一。另外一种方式是什么？在董事会中增设一名机器人。正如香港创投公司Deep Knowledge Ventures的做法。该公司的机器人董事会成员，使用机器学习预测最佳生命科学交易，利用历史数据来预测对于人类风险投资者来说不太明显的趋势。正如德米特里•凯明斯基向美国科技网站Betabeat所解释的那样，机器人在这个过程中不带任何情绪：

“Humans are emotional and subjective. They can make mistakes, but unlike the machines they can make brilliant intuitive decisions. Machines like VITAL use only logic. The intuition of the human investors together with machine’s logic with give a perfect collaborative team. The risk of the mistake will be minimized.”
“人类是情绪化的，带有主观性。他们会犯错误，但与机器不同，人类也会做出明智的直觉决策。与VITAL类似的设备只能使用逻辑。人类投资者的直觉与设备的逻辑，绝对是完美的组合。犯错误的风险将被降至最低。”

Sure, it’s novel. But why not? “Whenever people are skeptical that you can use data to do something that previously only people had done, that makes us want to try it,” Bahat says. “When Bloomberg rolled out its first product, people were saying, ‘No, human beings have to be the ones to price bonds.’ Turns out a computer can do some of those things better.”
当然，这种方式有些大胆。但为什么不试试呢？巴哈特说道：“当你用数据完成之前只能由人类完成的事情时，总会有人持怀疑态度，这反而让我们更想进行尝试。彭博资讯推出第一款产品时，人们说：‘不，只能由人类对债券进行定价。’事实证明，计算机做某些事情会做得更好。”