Slowly but surely, data is helping VCs look beyond networks for sourcing deals

Venture capital has traditionally been an industry that revolves around relationships. VCs invest in a startup’s idea but their conviction stems from the folks behind it. This largely makes sense because investing in a startup also usually entails entering a years-long relationship.

But backing companies based on the allure of the founder hasn’t always worked out. Indeed, it often gets investors tied up in companies destined to collapse for one reason or another. And depending on warm intros or networks also limits the amount of startups an investor considers, which further alienates founders who don’t have the same networks or hail from nontraditional backgrounds.

An increasing number of venture firms think the solution to cutting through the noise is by incorporating data science into their deal sourcing process. This wouldn’t be a crazy idea per se, as investors from other asset classes such as institutional investors, hedge funds and public market traders already embrace data-driven investing, but thus far venture has largely sat out of the conversation.

A few venture firms, such as Correlation Ventures, SignalFire and Rocketship.vc, have long taken this approach, but this number looks likely to grow.

Change is in the air

This week, Austin, Texas-based VC outlet Ensemble announced that it closed a $100 million debut fund to invest in early-stage startups using a data-driven approach that sorts and tracks companies based on the quality and depth of their entire team.

Ensemble’s co-founder and managing partner, Collin West — an alum of Correlation Ventures — told TechCrunch that the firm wants to back companies that have the strongest team, but that would be too difficult to track without using data science to pare the list down.

“Using software, we can track all of the people at all of the startups, and that ends up being a whole lot more information than any human brain can handle, and especially any venture firm,” West said. “We effectively sort the industry by team quality in a very objective way knowing which companies to focus on, and spend a lot more time on fewer companies.”