A popular meme in startup investing right now is on the increasingly data-driven nature of the industry.  More and more firms are employing developers and data-scientists internally to mine the trove of publicly available data to provide signals for companies that are exhibiting attractive momentum.  Here are a couple recent articles, and we are also seeing companies like Mattermark popping up specifically to assist both VCs and angels in this endeavor.

Call me old school, but I think the impact of quantitative approaches on early-stage investing is pretty over-rated and misunderstood.  Data is important and helpful (more on that later) but will not be core to what makes VCs successful, especially when it comes to identifying and sourcing the best early stage investment opportunities.

Here’s part of what informs my thinking.  My prior firm, Spark Capital, has had a number of pretty terrific exits recently (congrats guys!).  Namely:

• Tumblr: Acquired by Yahoo for $1B
• Adap.TV: Acquired by AOL for $400M
• Admeld: Acquired by Google for $400M
• OMGPOP: Acquired by Zynga for $180M

I often ask myself “how obvious was it at the time that these companies would be successful?  How attractive would these companies have been at the series A (and in some cases, the series B) stage based on trackable measures of rapid growth or momentum?”

Honestly, I think the answer is that while these companies had a lot of good things going for them, many data-miners would have been relatively unimpressed early on. These companies did have some solid metrics, but they did not see hockey-stick like momentum and there were still a hundred reasons why they could fail.

At the time, we also saw a number of other companies that seemed to have surprisingly remarkable surges in growth and usage.  These are the companies that quants would have identified as high-momentum opportunities. We didn’t invest in any of them. I don’t think many of them are still in business today.

I think ultimately, this is because early-stage investing is so much more about people, markets, and judgement than cold, hard, data.  These great investments were much more a bet on a team, a vision of the future, and an early product that seemed right, even if the quantitative evidence was still pretty slim. Maybe at the later stages, one can take a more quantitative approach to picking the best companies, but I wonder if that will really be true, especially since pricing at the later stages has been so astronomical in recent years.

Now, I don’t think quantitative approaches to VC are useless.  It’s an ever-changing ball game, and we at NextView employ data mining software for our purposes as well. But I think quantitative analysis and data mining is much less about finding the best companies or identifying hidden gems, and much more about understanding trends and market shifts.  I think VCs that use data well are much less likely to “source” their next hot company directly from a data signal, but are much more likely to make smarter decisions about investments and better help their portfolio companies because of the data they are tracking and analyzing across thousands of companies.