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#207 Data Driven Venture Capital with Andre Retterath, Partner at Earlybird VC

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Content provided by DataCamp. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by DataCamp or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

As we close out our focus on how the venture capital industry identifies and decides which future companies to fund, it might be easy to fall into the trap of thinking that the latest methods for discovering future unicorns are ubiquitous among all VCs. However, many VCs still work ‘the old way,’ using data to back up human assumptions. But what happens when a data engineer pivots to VC? What does a data-driven, data-first approach look like, and how does it compare to the incumbent processes?

Dr. Andre Retterath is a Partner in Earlybird’s Munich Office, focussing on enterprise software with a particular interest in developer, data and productivity tools, alongside AI-centric products and robotics. Before transitioning into VC in 2017, he gained more than 5 years of experience as a process automation and predictive maintenance engineer at ThyssenKrupp and further insights as a management consultant at GE North America. Andre also has his own VC, AI & data newsletter, Data-Driven VC.

In the episode, Richie and Andre explore the concept of data-driven venture capital, the challenges of traditional VC and why digitization has had a huge impact on the industry, the data-driven VC process, the use of modern data and AI technologies in identifying potentially successful projects, the human element in VC, the challenges and opportunities of early-stage investments, the importance of early identification of these ventures, cultural and organizational indicators and much more.

Links Mentioned in the Show:


New to DataCamp?


Empower your business with world-class data and AI skills with DataCamp for business

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231 episodes

Artwork
iconShare
 
Manage episode 418610017 series 3497889
Content provided by DataCamp. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by DataCamp or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

As we close out our focus on how the venture capital industry identifies and decides which future companies to fund, it might be easy to fall into the trap of thinking that the latest methods for discovering future unicorns are ubiquitous among all VCs. However, many VCs still work ‘the old way,’ using data to back up human assumptions. But what happens when a data engineer pivots to VC? What does a data-driven, data-first approach look like, and how does it compare to the incumbent processes?

Dr. Andre Retterath is a Partner in Earlybird’s Munich Office, focussing on enterprise software with a particular interest in developer, data and productivity tools, alongside AI-centric products and robotics. Before transitioning into VC in 2017, he gained more than 5 years of experience as a process automation and predictive maintenance engineer at ThyssenKrupp and further insights as a management consultant at GE North America. Andre also has his own VC, AI & data newsletter, Data-Driven VC.

In the episode, Richie and Andre explore the concept of data-driven venture capital, the challenges of traditional VC and why digitization has had a huge impact on the industry, the data-driven VC process, the use of modern data and AI technologies in identifying potentially successful projects, the human element in VC, the challenges and opportunities of early-stage investments, the importance of early identification of these ventures, cultural and organizational indicators and much more.

Links Mentioned in the Show:


New to DataCamp?


Empower your business with world-class data and AI skills with DataCamp for business

  continue reading

231 episodes

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