5 takeaways from 30 data-driven VC investors, data leaders, and hackers.

It would be hard to find a private fund manager that isn’t at least thinking about what it means to be “data-driven” or “AI enabled” right now. If a firm has more than 15 or so people, there’s usually at least one investor or platform leader, motivated by curiosity and maybe a little impatience, who has taken the initiative to become an expert at using data, automation, and AI to find and diligence deals.

Last week Foresight and New Enterprise Associates (NEA) hosted around 30 of these folks from most of the top venture firms in New York City to give 5-minute “lightning talks” about a data-driven challenge or initiative they’re working on. While their backgrounds and day jobs within their firms vary, they all have one thing in common: they’re all experimenting with data and AI and building tools to apply it – often as a nights-and-weekends project – and they’re seeing results.

What follows are a few top line observations we made from what we heard during the talks and the discussion that followed.

1. Investors have become hackers, and hackers have been become investors.

The profile of new investors has changed. There are still plenty of GSB grads with expansive rolodexes, but the newest generation of deal team members are people who have grown up using data and technology. It’s second nature for them, and when they show up at a firm that doesn’t provide the best data and analytical tools, they take matters into their own hands.

2. Everyone has the same data challenges.

Different facets of truth about private companies are scattered across and siloed within alternative data vendors, CRMs, shared drives, Slack channels, cap table vendors, KPI collection point tools, and accounting systems. This lack of unified data limits experimentation to narrow use cases or demands months of engineering that even the biggest firms either don’t have the capacity or desire to tackle. Every attendee at the event has spent time on this problem while also agreeing that solving it does not create competitive advantage; solving data unification and governance is a gateway to truly differentiated, proprietary in-house innovation.

3. Everyone is hunting for the right data.

There was a time when the only private market data vendors were Preqin (founded in 2003) and Pitchbook (2007). Now there are dozens of subscription-based alternative data vendors and even more new and novel sources of data about private companies, like Chief AI Officer, Finance YCombinator's Hacker News, and Swarm. Each source has a different facet of truth about a private company or a potential founder, and these alternative 3rd party data sources are the most valuable when they’re integrated with 1st party data (eg CRM, notes, Slack, email). It's telling that, following the lightning talks, attendees divided into topical discussion groups, and alternative data was the single biggest group.

4. Build vs Buy is now Build and Buy.

There are some categories of software where the classic build vs buy decision is binary. No one ever built half a CRM and bought the other half from Affinity.co. But data and analytics are different. Most of the attendees struggle with people and private company entity resolution and de-duplication (i.e. Foresight’s infrastructure), which takes different bits of data about the same company or person and unifies it for a single source of truth.  Everyone wishes they could focus more on building cool tools and custom workflows rather than “getting their data clean.” Building agentic workflows and data-driven sourcing requires an off-the-shelf-data governance and infrastructure platform to serve as a foundation and fill the gaps.

5. No one has built the same thing.

Madison Faulkner and Ashley Jepson at NEA are leading a project to build a multi-agent system for ranking and categorizing deals sourced through a wide variety of alternative data sources. Lexi Quirk and Britt Binler at 645 Ventures built a monitoring tool to source and route companies based on thesis and industry to relevant deal team members. Gomathi Ramalingam at Left Lane Capital lane has led the charge to bring data-driven sourcing and AI into the firm. The head of research at a venture fund legendary for its use of data built a system that creates digests of each deal team members’ meetings to ensure the richness and intimacy of subsequent meetings. A portfolio operations leader at one of the largest funds in venture built an AI service that helps to solve inbound information triage by categorizing emails and saving attachments to company folders and the firm’s CRM. Foresight’s head of research, Erica Young, has developed a way to rank the influence of co-investing firms on one another within investment networks.

Everyone who attended our gathering has built different data-driven capabilities based on a combination of their unique native talent and their pain points, so no firm has built the exact same thing, which created a collegial, collaborative environment. We felt an appetite among our guests not only for data and technology innovation but also for more in-person ways to share and compare notes. We plan to host more of these events, and our next one is happening on February 27th in San Francisco. If you’re a technical investor, a data leader, or a builder at a Bay Area venture manager, please apply at this link. We look forward to seeing what you’re building!

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