Investor Productivity Tool Stack
DDVC #48: Where venture capital and data intersect. Every week.
👋 Hi, I’m Andre and welcome to my weekly newsletter, Data-driven VC. Every Thursday I cover hands-on insights into data-driven innovation in venture capital and connect the dots between the latest research, reviews of novel tools and datasets, deep dives into various VC tech stacks, interviews with experts, and the implications for all stakeholders. Follow along to understand how data-driven approaches change the game, why it matters, and what it means for you.
Current subscribers: 12,310, +200 since last week
Brought to you by VESTBERRY - A tool designed specifically for data-driven VCs
Vestberry not only provides a vehicle for your portfolio data but also helps you understand the data and how to handle it. We aim to improve your portfolio monitoring workflow so you can spend more time leveraging data insights rather than creating them. Let's have a quick chat about how your portfolio data can work for you.
Following last week’s “Second Brain” post on my knowledge management system, I received tons of creative replies with new ideas and tools I will definitely try out. Thank you so much for your input! Additionally, 87% of you asked me to share further insights on my personal tool stack via the poll.
I heard you - so here we go :) Today, I share a visual summary of my personal tool stack as a VC investor. Note that although there’s a strong overlap with our VC firm tool stack, there’s still a difference due to non-investment-related departments such as finance, investor relations, marketing & comms, HR, engineering, etc. Moreover, every team member has her own stack, none looks the same.
If you’re interested in VC/PE fund-level tech stacks more broadly, you might want to check out the “Data-driven VC Landscape 2023” as it covers 400+ tools used by 150+ investment firms.
Overview of my tool stack
For the sake of this article, I sat down, went through all my apps, tools, and tabs, and created a comprehensive list. After finishing this exercise, I was actually surprised by the high number of tools and the level of fragmentation🤯
In ended up with around 80-100 individual tools, depending on what we classify as a “tool”. I use approximately half of them (see visual above) on a daily basis and while this sounds still a lot, my biggest insight is that the greatest efficiency gains come from combining different “best-of-breed” solutions via APIs and automation tools.
Hereby, the majority of these solutions become rather a passive component of my stack as they get automatically triggered via APIs and other automation tools without me noticing it in the day-to-day. In the remainder of this article, I dissect some frequently occurring workflows into their individual stages and depict the tools that I use to execute them. All of these flows are connected via automation tools that I highlight at the end of the article.
Exemplary workflows and preferred tools to execute them
