Venture Capital (VC) is one of the most powerful mechanisms in modern society for catalyzing innovation – particularly in technology – however, ironically, the VC industry itself is not particularly innovative. The truth is that the VC’s have been slow to adopt the AI technologies they help fund.
VC is a business that historically, and even now, is deeply rooted in personal relationships, intuition, reputation and a very analog flow of ideas and information. 95% of VC investments are currently sourced through traditional channels. However, the dominance of analog, purely intuitive approaches to early stage investing might be at an end. With the AI tools at our disposal, even now, we can eliminate major areas of uncertainty that currently – frankly – require guesswork, and which slow down decision making unnecessarily.
Implementing Data Science for VCs
Fundamentally, our business is sourcing and securing the most promising investments, betting on the right horse is what motivates us VCs and watching that investment go on to flourish is why we do, what we do. But intuition cannot alone predict success, henceforth, we must branch out.
Incorporating data science holistically across the VC process can enable us to make more informed decisions, faster by assisting us in better identifying promising areas of growth, for example. Algorithms can efficiently evaluate categories or trends that are gaining momentum and find potential companies within those spaces, or identify the fastest growing companies that are flying under the radar of institutional investors. In fact, in my experience companies sourced with data science significantly outperform companies from other sources, both in terms of valuation uptick and likelihood to raise. Then, with our target marked, we can use the algorithms we’ve built and nurtured to analyze companies against their competitors, to ferret out which have the most potential, and if the timing is right to spark a conversation.
Optimizing Investment Opportunity
Right now, roughly 90-95% of decisions happening in venture investing are human, but by 2030, that will drop significantly – I’d estimate to 50-60%. Reason being that as AI becomes smarter, it will be a key differentiator for how VC firms operate – widening the gap between those using it to great effect to those who are not.
In an industry that has largely depended on who you know and good word of mouth for finding opportunities on the brink of disruption – data science sharpens that lens and makes the process of pinpointing a target not only faster, but more comprehensive. In short, making more informed decisions, more quickly. Algorithms at this stage can be used in a myriad of ways – whether it’s identifying an emerging category that is on a growth trajectory, to identifying competing companies in a particular space, or even if a company is at the right stage for investment. Data Science insights offer the ability to “travel in time,” analyzing the past and current state of a potential company – alongside millions of others – to better predict how it’ll look in the future.
By using data science to firstly identify and then validate prospects, VCs are better equipped to make decisions quickly and confidently – and for investors, this instills trust in the choices put before them, knowing that the promise of these prospects is rooted in hard evidence, instead of just impassioned sales pitches.
Data Science Can Modify the VC Industry
Venture Capital is a business of people. That won’t and shouldn’t change, after all, it’s the connections sparked between passionate and purpose-driven entrepreneurs, and inspired investors that will always make great ideas a reality. However, with the technology we now have at our disposal, those sparks will become less of a moving target, and a greater certainty – using data to mine out opportunities, and then build the right ecosystem around them.
About the Author
Jonathan Serfaty leads Telstra Ventures’ growing data science unit, which is revolutionizing the way the firm identifies investment opportunities and the microtrends driving industry change. He was previously a data scientist at LinkedIn, focused on improving B2B sales and marketing outcomes through the use of machine learning.
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