enormous currency, helped by the “big data” transformation of nearly every field of technological and scientific endeavor, and high-profile data-driven successes in other fields, most prominently Nate Silver’s nearly perfect prediction of the outcome of the 2012 national election.
It should come as no surprise to see these experiments in the venture industry, though many investors are skeptical, particularly around whether the data available to grind through the algorithms is meaningful.
“I’m a huge fan of data-gathering efforts on patterns of startup success and failure over meaningful time series and sample sizes… but since most seed-stage investing is done before you have much data to work with, I continue to believe that it’s a high-touch process that lends itself to human intelligence (especially emotional intelligence/’people sense’) more than algorithms,” says Chris DeVore, general partner at Founders Co-op in Seattle, in an email. “As the age-old analytics adage goes, ‘garbage in, garbage out’ and in a data-poor environment like seed/early-stage investing it’s easy to convince yourself you see ‘patterns’ that don’t really exist.”
On a recent visit to Canada, Tim Porter, managing director at Madrona Venture Group, heard another pitch about the Moneyball approach to VC. Madrona tries to be “as data-driven as possible in our investment evaluation,” he says in an email. “I think the main difference here is what happens after the investment—at Madrona we feel it’s important to invest more than money, and really help to be company builders.”
Porter also echoes DeVore’s concern about data availability particularly for early-stage companies. “This approach to investing likely works better for (a) later stage investing or (b) follow-on investing in an existing portfolio where you do have more performance data,” he says.
Efforts such as the Startup Genome project are helping make more data available. Thurston has his own approach to solving the problem, detailed below.
He does not pitch his data-driven approach as a wholesale replacement for the VC investment process, but rather as a tool to be used in concert with human skills.
“Sometimes computers can be really good at finding precisely the types of patterns our brains are ill-suited for. Therefore the right pairing between our brains (for some patterns) and advanced technologies (for other patterns) holds tremendous potential for the future of business, strategy and innovation,” Thurston wrote recently on his Growth Science blog.
“Technology doesn’t go to the birthday party of your top customer’s daughter or share ideas over a beer with your team. By the same token, your brain doesn’t accurately detect subtle probabilistic interdependencies while crunching a terabyte of data through complex algorithms with perfect fidelity.”
Thurston has encountered fear from entrepreneurs who
