Here are 4 lessons I've learned from speaking to 200+ venture capitalists

It's been almost a year since we quietly opened up a private beta for Landscape - our new Full Stack OS for Data Driven VCs.

Since then, I’ve personally taken 200+ calls with venture funds from around the world, ranging from $5m solo GP funds, to $8b+ giants deploying globally from pre-seed to pre-IPO.

I've spoken with funds across every stage of the journey to become more data driven, from those that are just getting started, typically just aiming to get a better understanding of what this actually means, but also with funds that have spend $10m+ building sophisticated internal systems.

During the call, we typically discuss what Landscape can offer, what data driven activities they’re currently undertaking, and share philosophies on what the future of venture looks like.

#1 This is quickly becoming a priority

A rising number of funds are recognising that taking a more data driven approach to investing has huge merit, and are starting to actively taking the first steps integrating this approach into their fund.

Right now, in all honesty, the bar is pretty low. From my discussions, there are in the region of ~40 funds globally (that I know of) that are investing heavily into building sophisticated, proprietary data driven systems, and no more than 10 that are really pushing the frontiers of what's possible.

There is however, the growing acceptance that pretty soon this is going to become table stakes, and so it’s much better to start getting ahead of the trend, than playing catchup a few years from now.

#2 Data driven sourcing is the entry point

It's been pretty clear from conversations I've had, that most funds first priority when considering all things data driven venture, is in sourcing.

Most investors I speak with have ambitions of leveraging data driven sourcing to get to a position of "full market visibility", whereby they see everything that they feasibly could invest in.

The types of signals that a fund is looking for differs depending on the stage they invest, as do the data sources that produce these signals.

Early Stage funds are interested in signals like:

• Founders going into Stealth mode

• Companies being formed

• University spin-outs

• Fresh product launches

• Strong profiles leaving leading tech companies

Whereas later stage investors tend to be looking for signals like:

• Increases in headcount over time, or sudden spikes

• Impressive hires joining a leadership team

• Latest funding rounds

• Web traffic growth / App Install Growth

• Growing positive sentiment on review platforms

Obviously this is only a very small sample of the type of signals you can derive from a data driven sourcing.

My opinion is that over time, as more funds adopt a data driven approach to sourcing and "seeing everything", alpha will transition from sourcing, to screening.

#3 Building in-house is hard

Occasionally, I end up chatting with a fund that has attempted to build something in-house, but it hasn't quite worked out.

In-house builds that I've seen have ranged from associates gluing together some no-code tools, all the way to funds that have spent 6 figures+ building a proprietary in house platform.

Here are the most common reasons I have seen in-house builds stumble:

Lack of Development Resource

This is characterised by a fund that has all the right intentions of moving to a more data driven approach, and so makes the leap and hire an engineering associate / ML engineer, with the expectation of the individual leading on all things tech. The reality is that building out a full data driven solution is a tall order for a single developer to take on, and this individual is often left feeling overwhelmed by the amount of work that needs to be done, with progress inevitably being slow.

Lack of Usability

It is becoming a lot more common to see funds who have taken the first steps to become data driven, by stitching together various no-code tools that typically involve scraping various sources and outputting into spreadsheets.

While these MVP style solutions can feel like they move the needle, the main challenge becomes the usability. I've lost count of the number of junior members of an investment team that I've spoken with who are spending double-digit hours a week manually trawling through a spreadsheet.

Ultimately, if your system isn't easily useable, you're going to struggle to get consistent buy-in from the members of your investment team who sit at the top of the funnel.

Lack of Product Velocity

For funds that I have spoken with that have more development resource than most, the most common reason I see for what could be considered a "failed" in-house build really comes down to slow product velocity.

There are now numerous startups that are building solutions in this space, and they're shipping at an absolutely rapid rate. If your internal team doesn't have the startup mindset of "ship, ship,ship" it's very likely you're going to be overtaken by tools on the market.

I think the brutal reality of the situation is that the vast majority of venture funds are just not set up to build, ship, and maintain software.

#4 Sourcing is just the start

The most forward thinking funds have already recognised that getting to a position of "full market visibility" is awesome, but, presents a new set of problems!

It's all well and good seeing 10x more opportunities thanks to data driven sourcing, but you can't 10x the analysts at the top of your funnel.

This is where data driven screening comes into play, an area we've been thinking about a lot and shipping functionality within Landscape to support.

There's a whole bunch of every day investor activities that we think data driven venture will end up driving efficiencies in - watch this space 😄

That's all for now

This is a rapidly growing, and super interesting space to be operating in.

If you're a fund manager looking to take a more data driven approach at your firm, we should chat!