Data-Driven VCs: What founders need to pay attention to in order to get an investment

For the third year in a row, Andre Retterath has published the Data Driven VC Landscape Report. And for the third year in a row, the report begins with the words: "It is manual, inefficient, non-inclusive, and biased." This refers to the investment process of venture capital firms. Retterath is a partner at Earlybird in Munich and a pioneer in the field of AI in the VC business . He has been driving the topic of Data-Driven VC (DDVC) forward for years. In the report on the global " Data Driven VC Landscape 2025 ," he examines the current status and developments of data-driven VC investors. To be a data-driven VC, the firm must have at least one programmer on its team in addition to investors and portfolio managers and must demonstrably develop tech tools in-house that improve the investment process.

If the investment process is "(still) broken," as the report states, what has changed in the last three years? How do DDVCs operate today, and what do founders need to prepare for? We spoke with Retterath about current developments.

"Everyone has now understood that private market investing no longer works without data and AI," explains the Earlybird partner. In the past, however, the data-driven approach was often viewed critically: Skeptics complained that qualitative aspects couldn't be represented in data models and that important data wasn't freely available. Things are different today. With the help of artificial intelligence, the internet can be systematically searched, and relevant information can be pooled and evaluated. "Everyone leaves behind digital footprints that we can use to identify exciting companies at an early stage. Accordingly, we are increasingly contacting entrepreneurs, rather than the other way around," explains Retterath.