A year ago, I wrote a post about ‘The Rise of Algorithmic VC’. Since then, a lot has been written and said about this shift in the VC landscape.

Data is starting to play a major role in VC and PE, and today I would like to give you an insider view based on my past experience working at a VC fund, and my current findings from running Crunchdex. (an alternative data company that tracks over 17 million companies to help world-leading investors and corporates identify the fastest growing ones)

When I was launching Crunchdex 4 months ago, I already knew there was a demand for alternative data in the VC space. However, I had no idea how big of a trend it actually was.

The first mention of quantitative approach to VC that I was able to find online is from November, 2012. Back then, only a handful of funds were actually doing something with data. Nowadays, the vast majority of bigger VC funds (i.e. >$1BN AUM) either already have a data strategy or are currently developing one.

Company data providers have been very busy in the past decade. Some managed to aggregate massive chunks of stale company data (e.g. Bureau van Dijk). Some were lucky enough to experience strong network effects (e.g. Angel List or Crunchbase). Some scraped the living hell out of the internet (not going to point fingers here 🤐) . And some got pretty good at estimating interesting growth signals (e.g. App Annie or SimilarWeb).

Almost every quarter, it’s the same headlines — “record amount of VC investments in Europe”; “CVC investment activity has quintupled over the past 4 years”. Record here, record there. You get the picture. However, more deployed capital = more (new) funds, and naturally = more competition. When I was working in VC, I would argue the biggest challenge wasn’t spotting good companies; the biggest challenge was getting into deals. Ask any VC.

I think VC has become the new investment banking. It’s shiny from the outside — it seems glamorous, it’s a hot topic, and if you are lucky enough, you get to meet and work with some of the most talented founders in the world. Nowadays, even mediocre funds receive hundreds of job applications for junior roles. The best funds receive thousands. And all these young, fresh-out-of-school VCs want to make a mark. Given their network is usually very limited, company databases become a natural starting point.

If you check articles about hedge funds from 2014–2016, you will find a significant amount of headlines about hedge funds hiring quants. Quant hedge funds (like Two Sigma, D.E. Shaw or Renaissance) have been around for a long time now. If you check Google Trends, you can see one massive spike in 2008. And then again, a small spike around 2016.

The Top 100 Venture Capitalists — CB Insights

This doesn’t apply only to VC, it applies to any industry. I can tell you with 100% confidence that 9 out of 9 the most successful VCs have already developed a strong data strategy.

How do I know? We already supply data to some of them, and we have been in touch with 7/9 of them in the past few months.

Namely; API integrations, Big Data tools, and database tools.

Source: Alternativedata.org

Source: Alternativedata.org

Source: Alternativedata.org

I am basing this part on our actual pipeline = 100+ funds that we actually spoke to. That doesn’t mean that one can draw conclusions about the entire industry, but I would claim it’s a pretty good proxy.

2. European funds are lagging behind 🇪🇺🐢

That was perhaps the biggest finding from the past 4 months. I am really surprised how many big European VC funds do nothing meaningful with data compared to their US counterparts.

3. Many VCs are in what I call the ‘BUY or BUILD’ stage 🧰

This is obviously a very biased view, due to the fact that the analysis is based on our own pipeline. However, again — if you check job openings across VC funds, you will be able to see quite a few data science positions.

4. Many new funds implement data right from the start 📈

Why? Because LPs frequently ask about it during fundraising. And again, data is a good starting point for deal flow — not only for new VCs, but also for new funds.

5. Unfortunately, many VCs are still not ‘data-ready’ 😢

Data has not been part of the core DNA of most VC funds that have been around for a while. Some of them have unsuccessfully tried to experiment with it in the past, which has left a bitter taste in their mouth. Some just want to stick to their classic ‘GMAIL+CALENDAR routine’. And some just don’t believe that data is the right path for them.

Source: Nanex

I always like to use the comparison to high frequency trading. Literally everything in our lives is becoming faster. From social interactions to food deliveries or payments — you name it, I guarantee it’s becoming faster.

VC and PE will be no different. Speed will play a major role. How do you get faster? Through information = through data.

And I am not saying this just because I run an alternative data company. Look at the world around us, even the dinosaurs like banks and business registers are becoming increasingly open. Most already (at least) offer some API integrations.

So the amount of data about private companies will very likely increase tenfold in the next decade. By that, I am not saying we will see high frequency trading for private companies in the next 10 years, simply because of issues linked to equity and liquidity (actually, on second thought, who knows). But I am almost certain that this industry will go through a massive shift towards quant. The same shift hedge funds underwent some time ago.

Speed will really matter. How fast can you spot new, up and coming companies and how fast can you react to growth signals about growth stage companies will be key to success.

Of course things like brand, network and experience will still matter. However, especially for very early stage opportunities — speed will be the #1 parameter of success. Which opens a window of opportunity for many.

And I personally cannot wait to see this happen…