Can Data Answer the α-Question in VC?

Published on: January 26, 2024

Data and Artificial Intelligence (AI) have long been promised to be the magic potion for better returns, prompting even relationships-driven investors in private markets to consider something more than Excel and email for once.

Can Data and AI in Venture Capital (VC) make a difference? Will common data-driven VC programs yield any results?

Dealing with Uncertainty

It's no secret that few things in the world of business present as much of uncertainty as an early-stage startup. Whenever faced with uncertainty, an average investor's resolution protocol subconsciously reduces the problem to one of the following:

1. Rely on the "Rule of Thumb" and common patterns
2. Do what everyone else does
3. Find more data

This is just an oversimplified look at an average approach. Yet the average doesn’t get one far in the Venture Capital world of finding outliers.

Data-driven VC

Many of the modern "data-driven VC" initiatives reduce the problem to the discovery: be it discovery of the company itself or news about it, but the discovery nevertheless. Some go as far as to chase the speed of discovery too, as if it made a big difference in the illiquid and permissioned world of startups with an average of 5-7y learning period full of rollercoaster rides.

Of course, data-driven discovery can be a decent productivity helper in reclaiming the overtime spent on manual research, but it goes only so far in the race for higher returns or more sustainable portfolio construction.

More importantly, the sourcing challenge may simply be too temporal to pay so much resources and attention to, let alone making it a bedrock of a data program. The problem may simply disappear once the firm builds a brand powerful enough to reverse the search dynamics in the market. Besides, data gathering has been solved many times over by all the "data-scraping" database companies and various data as a service providers. So why do teams keep doing after it?

Navigating Uncertainty with Data

Well, one explanation could be that searching for more data is a controllable & familiar activity and hence comfortable to do. It's easy to hope that the answer is "out there", lying in plain sight somewhere in the data haystack. But is it?

Unlike more established businesses with a clearly defined market and a proven business model, which could in turn be defined by existing metrics and parameters with low variability, the information space of an early venture company often simply does not exist at all to be discovered. And its financial projections, estimations and plans are less predictable than Schrödinger’s cat state. To succeed, the startups would usually need to be uniquely differentiated from incumbents too, making it hard to find comparables. It seems that simply no amount of data can comprehensively define current or future state of a new venture or its trajectory in and by itself.

Moreover, forced attempts to codify the early guess-work – especially with demographics – may only lead to encroachment of pre-existing biases into the decision process. While increase of data points could lead to retrofitting and then "paralysis by analysis" later ,when attempts to make an informed decision get overwhelmed by the abundant noise delivered at the speed of fiberoptics.

What if the answer to the α-Question in VC simply isn't the data hoarding?

Then What?

The data-driven solution to the uncertainty problem in VC may exist. It just isn't likely to be in the amount, completeness or variety of the data itself, but rather in algorithms, models and methods of processing it as well as assumptions & hypothesis of the decision makers. Fundamentally, the uncertainty is also a mathematical problem rather than a discovery one, and it's continuous & dynamic by nature rather than discrete & static.

Beside from models and algorithms to support the decision, a good Venture Capital data program would aim at supporting product and execution teams in navigating uncertainty throughout the growth journey just as much as it helped the investment committee at the outset of their data-driven decision. The decision which can be nailed with a couple of models, or be off by a mile fully-backed by “million datapoints delivered at the speed of light”.

From Data Collection to Intelligence: The Resiliq Approach

Rather than simply collecting data, leading VCs use AI to transform information into actionable intelligence. Resiliq's platform goes beyond data aggregation – our AI agents analyze patterns, surface insights, and proactively alert you to opportunities and risks that matter.

This systematic approach helps VCs reduce time spent on manual research, accelerate deal evaluation, and improve investment outcomes. By combining rich market data with AI-powered analysis, Resiliq enables venture capitalists to make faster, more confident decisions backed by comprehensive intelligence rather than incomplete information.