????伊恩·西格羅就是懷疑論者之一，他是風(fēng)投公司Greycroft Partners創(chuàng)始人兼合伙人，在紐約和洛杉磯設(shè)有辦事處。當(dāng)然，西格羅不懷疑技術(shù)本身，Greycroft Partners的主要投資對象就是互聯(lián)網(wǎng)和移動公司，他懷疑的是這種思路，也就是用數(shù)據(jù)來預(yù)測10年后的情況。西格羅說：“沒有人具有那樣的預(yù)見性，你既無法預(yù)測創(chuàng)業(yè)者會怎樣掌控自己的公司，也無法捕捉到優(yōu)秀的風(fēng)投戰(zhàn)略合伙人帶來的價值。無論誰提供資金或者誰參與其中，想憑數(shù)字來解決這個問題的科學(xué)在我看來都有頗多疏漏之處。”

????和瑟斯頓不同，西格羅認(rèn)為定量分析無法取代風(fēng)投，就算很久以后也是如此。不過，他確實覺得數(shù)據(jù)有可能幫助風(fēng)投資本家進(jìn)行決策。舉例來說，Greycroft Partners在過去的兩個夏天都聘請了一位數(shù)據(jù)科學(xué)本科生。她的研究課題之一就是利用該公司軟件的機(jī)器學(xué)習(xí)功能對公司進(jìn)行盡職調(diào)查，并對效能和創(chuàng)新進(jìn)行算法分析。她的另一項工作是對Greycroft Partners的一些投資對象進(jìn)行數(shù)據(jù)分析，以判斷市場變化是臨時現(xiàn)象還是會較為持久。西格羅通過實例介紹說，最近有一家公司上門自薦，稱自己的App連續(xù)幾周都高居App排行榜首位。他說：“問題在于這家公司是下一個Snapchat，還是曇花一現(xiàn)？我們考察了這個App的持久性和使用數(shù)據(jù)，并從單次使用時長和使用頻率角度考察了用戶行為……我們的結(jié)論是，它不太可能成為下一個Snapchat，原因是在它的生命周期中并沒有展現(xiàn)出類似于Snapchat的特質(zhì)。”

????毫無疑問，數(shù)據(jù)分析已經(jīng)滲透到了風(fēng)投資本中——Google Ventures通過某種算法來協(xié)助進(jìn)行投資決策，一家名叫Correlation Ventures的硅谷公司就建立在基于算法的投資策略之上。但詳細(xì)研究并由人做出判斷的老式做法仍將存在很長一段時間。問問Lux Research的人就會明白這一點，這是一家設(shè)在波士頓的新興科技咨詢機(jī)構(gòu)。10年來，該公司那些理工出身的分析師一直在為新建立的科技公司進(jìn)行廣泛的商業(yè)環(huán)境評估，他們采訪這些公司的員工，并把成功或失敗的公司慢慢納入自己的數(shù)據(jù)庫。它通過九項關(guān)鍵因素來評估這些公司，并在自己的網(wǎng)站上用一篇名為《衡量和量化成功創(chuàng)新》的報告公開介紹了這些要素。該公司還通過《Lux觀點》報告來介紹所評估公司的情況，它的評級分為幾等，從“非常樂觀”到“非常謹(jǐn)慎”。

????最近，Lux Research回顧了過去五年的評估對象，發(fā)現(xiàn)在獲得“樂觀”評級的公司中，有一半發(fā)展順利。Lux Research對“順利”的定義是首發(fā)上市、獲得收購或者獨立經(jīng)營并實現(xiàn)盈利。考慮到新公司通常的生存率，Lux Research的工作看來大大提高了評估初創(chuàng)公司的可靠性。對首席研究官克里斯·哈茨霍恩來說，該公司的高準(zhǔn)確率得益于兩個因素，那就是業(yè)務(wù)能力和方法。Lux Research的分析師都是擁有博士學(xué)位的科研人員，很熟悉自己專業(yè)領(lǐng)域中最先進(jìn)的技術(shù)。用哈茨霍恩的話說就是：“如果一家初創(chuàng)公司試圖打破熱力學(xué)定律，他們也明白是怎么回事。”說到方法，10年來他們一直用這九項要素來進(jìn)行評估，因而可以按同樣的標(biāo)準(zhǔn)來比較評估對象。

????人們經(jīng)常說到創(chuàng)新在經(jīng)濟(jì)增長中的重要性。商業(yè)咨詢機(jī)構(gòu)Economic Innovation Group最近在民意搖擺不定的州進(jìn)行了一次調(diào)查，75%的受訪者都認(rèn)為美國需要更多的創(chuàng)業(yè)者和投資者，以便解決長期存在的經(jīng)濟(jì)問題。哈茨霍恩認(rèn)為，這就是說采取行動的時間到了，“創(chuàng)新經(jīng)濟(jì)在信息方面存在問題。左右創(chuàng)新經(jīng)濟(jì)的信息質(zhì)量不高。一個國家怎樣才能更有效地成為創(chuàng)新經(jīng)濟(jì)體呢？它得更好地為能實現(xiàn)增長并推動就業(yè)的初創(chuàng)公司提供資金。如果用錯了地方，資金就會產(chǎn)生不利影響。而它本該發(fā)揮重要作用。”

????大數(shù)據(jù)也許真的能為人們提供幫助，但它更有可能成為拼圖中的一塊，而不是解決方案。比如說，學(xué)術(shù)研究表明，過去曾取得成功的連續(xù)創(chuàng)業(yè)者更有可能在新公司干出成績。這就意味著回顧過去的情況對預(yù)測未來有一定的指導(dǎo)意義。不過，哈佛商學(xué)院投資銀行學(xué)Jacob H. Schiff講席教授喬希·勒納認(rèn)為：“創(chuàng)業(yè)的本質(zhì)總是在不斷變化。在相關(guān)文獻(xiàn)中，大多數(shù)預(yù)測創(chuàng)業(yè)能否成功的回歸方程的擬合優(yōu)度（即R平方）都非常低，這表明在這個領(lǐng)域，《點球成金》中的那種方法有局限性。和預(yù)測哪些棒球運(yùn)動員將表現(xiàn)出色相比，預(yù)測哪些初創(chuàng)公司能獲得成功要難得多。這就好比棒球規(guī)則每年都會以無法預(yù)知的方式發(fā)生改變一樣。”（財富中文網(wǎng)）

????譯者：Charlie

????校對：詹妮

????Count Ian Sigalow among the skeptics. Sigalow is a co-founder and partner at Greycroft Partners, a venture capital firm with offices in New York and Los Angeles. Sigalow, of course, isn’t skeptical of technology—his firm invests mainly in Internet and mobile companies—but of the idea of using data to judge what’s going to happen in 10 years. “Nobody is that prescient, where you can figure out how an entrepreneur is going to pivot his or her business,” he says. “Nor can you capture the value of having a good strategic partner in your VC. Any science that tries to reduce this to a number—regardless of who funds it or who’s involved—I think is actually missing a lot.”

????Unlike Thurston, Sigalow doesn’t see quants taking over VC, even in the distant future, but he does see the potential of using data to help venture capitalists make decisions. Greycroft, for example, has employed a graduate student in data science for the past two summers. One project she’s working on is performing due diligence on companies using machine learning as part of their software, analyzing algorithms for efficacy and novelty. Greycroft is also having her analyze data from some portfolio companies to asses whether changes in the market are temporary or more lasting. More specifically, Sigalow explained that a company pitched his firm recently, touting that its app was at the top of app charts for a few weeks. “The question was: Is this company the next Snapchat or the next dud?” he says. “We looked at the persistence, the usage data, the behavior in terms of session length and frequency…and came up with the conclusion that this was unlikely to be the next Snapchat because it did not exhibit the characteristics that Snapchat did at that time in its lifecycle.”

????Data analytics are undoubtedly creeping into venture capital—Google Ventures uses an algorithm to help with investment decisions, and a Silicon Valley firm called Correlation Ventures is built upon an algorithmic investing strategy. But the old-fashioned process of detailed research and human judgment still has a lot going for it. Just ask the people at Lux Research, an emerging-technology consulting firm in Boston. For the past 10 years, Lux’s science-trained analysts have been scouring the business landscape for new technology firms, interviewing employees of those firms, and slowly compiling their own database of companies that succeeded or failed. Lux rates each company it profiles according to nine key factors, which are available to the public on its website in a report called “Measuring and Quantifying Success in Innovation.” The result of that rating is a company profile with a “Lux Take,” which ranges from “strong positive” to “strong caution.”

????The company recently looked back at five years worth of profiles and found that 50% of the companies that earned a “positive” rating went on to be successful, an outcome which Lux defines as an IPO, acquisition, or transition to standalone profitability. Given the usual odds of new business survival, the Lux system seems to inject a significant amount certainty into the process of evaluating startups. For Chris Hartshorn, Lux’s chief research officer, the company’s high rate of accuracy is attributable to two things: capability and methodology. Lux’s analysts are scientists with Ph.Ds who are familiar with the most advanced technology in their area of expertise. In other words, “they understand if a startup is trying to break the laws of thermodynamics,” Hartshorn says. And the Lux methodology, which rates companies on nine factors, has been in place for 10 years, allowing the firm to compare the companies it profiles in a consistent way.

????People talk a lot about the importance of innovation to economic growth. In a recent survey of voters in swing states by the Economic Innovation Group, 75% of those surveyed agreed that America needs more entrepreneurs and investors in order to improve long-standing economic problems. Hartshorn considers this a call to action. “The innovation economy has an information problem,” he says. “The information that drives it isn’t good. How can countries become innovation economies in a more efficient way? Let’s get better at funding the startup companies that will grow and drive employment. For every dollar that goes in the wrong place, that’s a shitty dollar. And it should matter.”

????Big data may indeed be able to help, but it’s more likely to be a piece of the puzzle, not the solution. For instance, academic studies have shown that serial entrepreneurs successful in the past are more likely to do well in new ventures. That implies there is some explanatory power in looking backwards for guidance on what’s ahead. “But the nature of entrepreneurship is always changing,” says Josh Lerner, the Jacob H. Schiff Professor of Investment Banking at Harvard Business School. “Most regressions predicting entrepreneurial success in the literature have very low goodness of fit (R-squared), which suggests the limits of a ‘Moneyball’ approach here. Predicting which startup is going to be successful is much harder than [predicting] which baseball player is. It is as if the baseball rules are being changed every year in unpredictable ways.”