Slowly but surely, data is helping VCs look beyond the network to find deals.


It has venture capital. It’s an industry that traditionally revolves around relationships. VCs invest in a startup idea but their losses come from the people behind it. This mostly makes sense because investing in a startup often involves getting into a long-term relationship.

But companies that fund based on the founder’s wishes aren’t always successful. Indeed, it often keeps investors tied up in companies that are destined to fail for one reason or another. And it limits the amount of startups an investor can consider as hot spots or networks, making them more sympathetic to founders who don’t have similar networks or come from traditional backgrounds.

A growing number of venture capital firms think the solution to cutting through the noise is to incorporate data science into their deal sourcing process. This is not a crazy idea in itself, as investors from other asset classes such as institutional investors, hedge funds and public market traders are embracing data-driven investing.

Our belief is that if you don’t start doing this, you will fall behind. Mark Sherman, Managing Partner, Telstra Ventures

A few venture capital firms, such as Correlation Ventures, SignalFire, and, have been taking this approach for a long time, but that number is likely to grow.

Change is in the air.

This week, Austin, Texas-based VC Socket Ensemble announced the closing of a $100 million seed fund to invest in early-stage startups using an data-driven approach based on the quality and depth of their entire team.

Ensemble founder and managing partner Colin West – of Correlation Ventures – told TechCrunch that the firm wants to support companies with a strong team, but it’s hard to track without using data science to benchmark the list.

“Using software, we can track all the people in all the startups, and that ends up being more information than any human brain can handle, and especially more information than any venture company,” West said. “By knowing which companies to focus on, we effectively categorize the industry by group quality and spend more time on smaller companies.”


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