How is Data Science improving Venture Capital?

Jon asks Peter everything about data science. What data sources are they currently using? And how much will it disrupt the functioning of Venture Capital firms.

Some Lies/ Points  Covered in This Episode Include:

1. Would you say data science is changing things?
2. What Data Sources are University Growth Fund are using at this point?
3. For seed and early-stage firms, when data is almost non existent, will it be a barrier
4. A venture capital deal pipeline has three key elements: sourcing, benchmarking and value-add. Can Data science help on all three fronts?
5. What are some of the other challenges you foresee?
6. It’s a very intuitive field, do you think backing it with numbers and data will give VCs clarity while making decisions or make them second guess their intuition on                                                things that really matter- word of mouth, integrity, past personal or peer experiences.Things that data can’t support.
7. Often, data from sources such as Twitter, LinkedIn, Pitchbook, Crunchbase, and AngelList are obtained and then pooled and organized. The organization and manipulation of third-party data can be time and labor-intensive. Pooled third-party data that is improved and arranged in a customized manner can eventually become proprietary in nature. Your thoughts?
8. Once VC ventures become data backed, they will need to hire or re-organise teams that can collate and work with such data. How soon do you see that shift coming

PS-  it may call for a different talent sourcing model and organizational structure, with resulting implications for the structuring of compensation and incentives.

1. For example, the venture capital firm Social Capital has built an automated system to invest in startups without meeting them. Companies upload data about themselves, and if the firm’s algorithms score the companies well, the firm backs them with an investment. The process was designed to keep bias from entering the equation. By mid-2018, the firm had assessed over 5,000 startups and invested in 60. Most of the investments were in companies based outside the major venture capital markets of the Bay Area and New York, and many were based overseas. About 80% of the companies featured non-white founders and 30% featured female founders. Do you see them as outliers or can that be the upcoming trend?

Let us know your thoughts on data science/ AI changing the Venture capital? What should we talk about next? Give us a follow and leave us feedback.

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