Large data mining makes PE/VC more efficient LinkedIn as the best tool

According to foreign http://www.aliyun.com/zixun/aggregation/31646.html "> media reports, as a data scientist for the Silicon Valley venture company Greylock, DJ Patil (DJ Patil) is aware of how flood data can change the way VCs operate. The former chief scientist at LinkedIn said the venture is still a courageous and intuitive business, but data-mining technology can help VCs become more efficient.

In an interview with the Wall Street Journal about how to use large data to capture the next big trend, the following is an interview summary:

Q: Can data help identify the companies that stand out?

A: We've found a lot of interesting signals that people can use to understand larger trend changes.

One example is the distributed code hosting site GitHub. This service allows people to collaborate on programming. From here we can see the GitHub community's attention and use of technology. Take the new programming language Scala for example. If you look at the total number of GitHub Scala rallies and the use of Scala language, it's obvious that it has great potential for growth. Once we see how popular it is, we will tell the customer the company's problems directly: they cannot scale this technology. Finally we helped set up the Typesafe company, making it easier to use Scala to develop the software.

Q: What are the challenges facing investors with data mining?

A: One of the challenges is that VCs really focus on the team. You are not investing in that field (or technology), you are the person who invests in that field.

This kind of human-related stuff can't be captured in the data. You must find the right environment. There are a lot of experienced people there.

Q: What data is very useful to investors?

Answer: One of the most powerful tools is LinkedIn. Before you go to a meeting, you can get an idea of a person's information. Others can also learn about your information, which will help you reach a more equal level of information with others.

Q: Michael Lewis's "Penalty Gold", a book about the use of data by the Oakland Sports Club to recruit players, is there any reference to the VCS?

A: I agree with the "penalty gold". They don't rely entirely on data throughout the process, and I think that's what we're doing. The difficulty is not all the data-related problems of a certain period of time. If you want to look for the next Zuckerberg, that's the real difficulty.

Q: Why is this difficult?

A: There is no leading signal. Discovering the next major founder, we usually don't have data signals. Even if it is to discover the next 15-20 years of excellent four points, it is also difficult things.

Q: How does the data make the investment faster and win the time?

A: The best vcs don't get someone first. What you need to do is realize when the market winds are changing or what opportunities people have not yet realized its value. VCs are not trading like hedge funds, which simply says, "Let's develop a super intelligent algorithm." "Hedging deals are time-sensitive, but VCs don't work that way," he said. VCs pay more attention to collaboration and the whole process is longer.

Q: What are some of the unrealistic data sources that are most useful in analyzing the company's process?

A: a stable signal that measures people's skills and passions. It can find people who make the whole team run. It is very difficult to find a professional who is in line with the company culture.

(Responsible editor: The good of the Legacy)