The Data-Driven Investor of Today And Tomorrow

This article originally appeared in Forbes.

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The term "data-driven investor" might sound pedestrian—it’s hard to point to an industry that hasn’t become more data-driven over the past two decades. But in recent years, the private capital industry’s adoption of data-driven insights represents a very real and material shift in how firms are now operating.

As a proxy, one can look at the hedge fund industry for a historic example of this strategic shift. Up until the 1980s, hedge funds were mostly run by Wall Street veterans who used their intuition and connections to make investment decisions. But as more data became available, a new breed of funds emerged that combined quantitative models with human judgment to drive better decisions and reduce cognitive biases. Venture capital and private equity are at a similar inflection point right now.

Data-driven investing is the idea of using data to augment human decision-making and automate away human data entry from every aspect of a firm’s operations—from sourcing and deal management to portfolio collaboration, fundraising and investor relations. It means using everything from a firm’s private data (communications, deal data and performance data) to third-party data (about startups and founders, alternative signals and job movement) to make better decisions, faster.

Taking A data-driven approach to finding and investing in companies

Deal sourcing is the most obvious pillar going through a data-driven transformation because of the sheer volume of information available on companies and founders—even “proto-founders” like great engineers or PhDs who don't yet realize they want to start a company.

From job change signals, company growth signals and website traffic signals to communications and activity on X and GitHub, everyone and everything has some kind of digital footprint now. The opportunity lies in collecting every relevant data point from that footprint to develop a superhuman coverage of your investing thesis.

Data-driven firms can effectively analyze all that data to identify earlier when a signal becomes interesting and when to reach out to turn the opportunity into a deal. They use automation to automatically update CRM records, log interactions and keep all that deal data up to date.

When it’s time to get in touch, they use relationship intelligence to generate critical contextual insights like: "Who has the strongest relationship with someone at this startup we want to talk to?" "Who knows this specific founder, CEO or CFO?" and "Have we already talked to them?"

This helps the data-driven investor source the highest quality opportunities faster and pursue them in a way that maximizes their chance of winning the deal.

The impact of AI on the data-driven investor

The advantages of data-driven investing are significant today—and they’re increasing all the time. AI is quickly transforming how productive the data-driven investor can be in at least two important ways.

First are the more basic productivity use cases of AI for streamlining the manual, repetitive tasks that we all do. From writing outreach emails and intro emails to producing market maps, industry reports and thought leadership, many firms are adopting AI-assisted research and writing to increase their productivity.

The second cluster of applications is using AI to generate data and uncover new signals, further putting otherwise manual data entry on autopilot. Today, for example, most investors still summarize notes, update deal statuses and owners, and create follow-ups by hand. Much of this is the work of investment associates and analysts—work that AI is increasingly taking on end to end. At the same time, with the explosion of available data and signals for sourcing, VC and PE investors are deploying AI to sift through the information to uncover great founders and startups before their peers.

The next frontier for the data-driven investor

Putting to use the volumes of data, automation and AI capabilities investors now have access to is fundamentally changing how private capital operates today. With how rapidly these technologies have been developing, I believe we’re on course to seeing AI copilots capable of helping investors with investment decision-making itself.

Soon, this will mean taking all of a firm’s historical performance data across every phase of the investing value chain—sourcing, screening, deal flow—and feeding it back into AI models that learn and self-improve. Such a copilot would be capable of answering important questions like "Which deals did we miss?" and "What signals were there that this was a good investment?" It’s not hard to imagine these agents automating, testing and optimizing a firm’s investment hypothesis much more quickly than a human team.

For many in private capital, data-driven investing is here already. It’s a question of when, not if, it becomes the norm across the industry.

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