Actionable Primary Source Information is Non-Existent

The root of inefficiency in private markets is the lack of actionable and curated information. It is near impossible to discern the quality of early-stage companies on the internet. Although digital profiles exist, they lack actionable and accurate information on companies. As a result, market participants effectively throw darts until something sticks. For example, when we asked a Silicon Valley VC how he makes decisions about which companies to engage, here’s what we heard:

 

“I see about 30–40 companies online a day and to move through them quickly, I first look at what they’re doing, then what market they’re in, then the background of the team, then if they raised funding and if so, the quality of their investors. So 1) website, then 2) LinkedIn, then 3) crunchbase. This process takes me 2–3 minutes [each].” Based on this, I’ll either reach out for a call or pass.”

The lack of actionable information in one click, coupled with noise, hinders efficient deal-sourcing, talent placement, customer engagement, and other entrepreneurial activities. It impacts every market participant, including founders, investors, talent, corporate innovation executives, and other service providers. Simply put, objectively high-caliber founding teams are at greater risk than ever of being buried in the noise.

 

 

To be clear, we aren’t the first to explore a software solution that promotes efficiency in private markets via data. Several technology platforms operate in this sphere.

 

 

• Data Mining: Companies including Crunchbase, CB Insights, Pitchbook, and Tracxn brand themselves as the “Bloomberg of Private Companies.” They provide users with data sets, research tools, and high-level analytics based on web scraped data.

 

• Networking & Fundraising: Platforms like NFXSignal, Gust, AngelList, and VCWiz offer investor discovery and engagement products. These products gather data on investors and aspire to create a democratic approach to fundraising for founders. Additionally, emerging crowdfunding platforms have recently created new paths to fundraising for founders.

 

• In-House Predictive Analytics: A handful of VC’s have built in-house prospecting engines. They extract data from the sources above, combine the data with manual inputs, and generate predictive insights and leads. Firms like Correlation Ventures, Quake Capital Partners, and 645 Ventures are pioneers here.

 

The platforms discussed above have each enabled a greater degree of private market efficiency in their own right. However, each of the above relies almost entirely on scraped data.

As a result, although the concept of harnessing data to make smarter investment decisions, like we’ve seen in stock market investing over the past decade with machine intelligence platforms such as Kensho Technologies, is logically on the horizon, venture capitalist Lisa Edgar explains the primary hurdle:

“The current talk about machine learning and AI tools running on top of these databases is absolutely nonsensical, unless there is keen focus and incentives on getting the underlying data right.”

Former AngelList employee David Booth also describes this constraint on efficient venture capital activity:

 

“The thing restraining innovation in VC isn’t so much “what tools help X do Y” — as it is where does the data come from that actually makes those tools useful. The next wave of innovation will come on the back of [digitizing the right foundation of data] and developing mechanisms for alignment of interests between participants in the ecosystem.”

If curious about nuances of the product landscape, a few contemporaries have covered the tech-stacks used by today’s VC’s and hypothesized ways for software to revolutionize venture capital.