What is Data-driven VC?

04 June, 2023  — Currently, VCs can be classified into three groups:

a) Old-school VCs — 0 Engineers* in the team; focused on manual workflows, a simple tool stack with a CRM system like Salesforce, basic Email, Slack/WhatsApp for communication, etc.

b) Productivity VCs — 0 to 1 Engineer* in the team; successfully took the first leap to a modern off-the-shelf tech stack with VC-focused CRM systems like Affinity, automated workflows with Zapier, the use of Notion for knowledge sharing, etc.

c) Data-driven VCs — 1 or more Engineers* in the team; develop their own scalable solutions to capture data, automate workflows and bring the core of their business in-house.

If we define Data-driven VC as a VC that has at least one engineer in the team working on internal data initiatives & one DDVC community nomination and truly develop internal tooling in at least one segment of the VC value chain, today globally exist ~150 Data-driven VC firms.

Data-driven approaches and AI improve the investment efficiency impacting on some weak points of the investing process.

Uneven capital allocation results in suboptimal returns, with significant funding disparities across regions. Data-driven initiatives work on increasing inclusiveness as well, helping to reduce geographical and gender biases, make better and fairer investment decisions providing more opportunities for everyone.

Leveraging data-driven approaches and AI optimizes deal sourcing and screening processes as well, taking them to much higher level. Machine learning approaches outperform human investors, increasing the effectiveness of investment decisions and reducing miss-rates.