Dave does a nice job of articulating some of the forces changing VC. We are a data company that sells data to many of the top VCs so here are some observations based on how we see them work.

First, there are a good # of VCs who are evolving. Not sure they are trying to scale in the way Dave talks about, but they're evolving how they run their business. We do see many using data in very interesting and systematic ways – analyzing industry trends including financing flows & exits, improving their deal flow management practices, understanding investment patterns of peer investors, etc. In many instances, they’re taking data feeds from us and others and vacuuming them into their dealflow management systems to aid in day-to-day operations. That's suggestive of process which again suggests some desire to scale, make things repeatable.

But these investors are not the norm. I think Dave and his team have talked about Moneyball in investing, but a quantitative approach to venture capital may be difficult. Some of this difficulty is driven by data limitations, of course, in terms of sheer quantity vs. something like public markets, but a lot of the reasons that data is less used are structural or behavioral in nature. While there are many reasons data is not used by a lot of VCs, a couple of these reasons are detailed below. Pls note that these are generalizations. As mentioned above, we see and know many VCs taking a very progressive attitude towards the use of data, but they’re the exception and not the rule.

First, VC is a long game – Unlike public markets or baseball where you have an immediate scorecard to tell you how you’re doing, VC doesn’t have that. As a result, the desire/need for data is less clear especially when you won’t know for many years if your investment thesis was correct or crap.

Also, although there is a lot of talk about one's investment thesis, there are again, some firms who stake out a thesis and invest in it and many others who look to invest in similar companies invested in by Union Square or Andreessen Horowitz or Sequoia.

Next, despite investing in the next big thing and often being exposed to cutting edge innovation via their portfolio, the process VCs use to find deals is hopelessly antiquated. This myth of proprietary dealflow has been so ingrained into the VC ethos that using any type of scalable method (or attempting to) is almost anti-thetical to how “VC is supposed to be done.” The secret, of course, is that outside of ~20 firms (I'm being generous), most VCs don’t have real proprietary deal flow. But instead of using data, most would prefer to throw bodies at the problem expecting them to find hidden gems. Or they attend demo days of accelerator programs and the like which offer them no preferential, proprietary access to dealflow.

VCs often view their job as being good "pickers" of companies but the new model suggests that picking is important but ongoing support (recruiting, technical, etc) from investors can help change the outcome of their portfolio companies. Google Ventures adheres to this philosophy.

Again, the above are for the majority of firms. There are some who are quite progressive. And of course, there are new models a la 500 Startups in the micro-VC category which are trying very different things.

Ultimately, given the returns in the industry have been subpar and the decreasing allocations of LP money to the venture capital asset class overall, a rethink of process might be forced upon VCs as doing more of the same with the expectation of a different result defines insanity.

Another great discussion on this can be found on Matt Turck's (Bloomberg Ventures) blog here - http://mattturck.com/2012/07/20/data-driven-venture-capital/ Many of my comments above I left on his post as well.