👋 Hi, I’m Andre and welcome to my weekly newsletter, Data-driven VC. Every Tuesday, I publish “Insights” to digest the most relevant startup research & reports, and every Thursday, I publish “Essays” that cover hands-on insights about data-driven innovation & AI in VC. Follow along to understand how startup investing becomes more data-driven, why it matters, and what it means for you.
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Data Engineering is no longer just a backstage player but is taking center stage in the VC space. It is about constructing and maintaining the architectures (think databases, large-scale processing systems) that allow for data availability—a cornerstone for insightful analysis and well-informed decisions.Turning raw data into usable outputs and insights has great use cases for deal sourcing, due diligence, or portfolio monitoring. Dive deeper into this video, where we break down the impact of data engineering on the VC industry.
The Big Unbundling
In the past decade, tool stacks emerged from a single, functionally very limited OS (like MS Suite) into a mess of multiple, individually really powerful best-of-breed point solutions - the big unbundling of the tool stack. I described the 4 different waves of Investment Tech in this post before.
Today, the best-of-breed landscapes are not only difficult to navigate but also painful to set up and synchronize. Most solutions are hardly compatible creating friction, inefficiencies, and data siloes across the stack.
In this post, I’ll explore where the friction comes from, how it can be released, and why everyone is desperately looking for a “Tech Stack In A Box” as the silver bullet. While I’ll describe the example of an investor tech stack, it equally applies to any other professional software stack too.
Teaser: Sign up here to join 200+ firms for the closed beta of “VC Tool Finder”. More details at the end of this post.
Root Cause Of Pain And Friction
We can slice and dice tool stacks in many different ways but I prefer mapping it alongside the VC value chain.
#1 Sourcing
Identifying startups as early as possible, collecting all available data, and merging it into a single source of truth. Related episodes below. Tools focused primarily on this part of the value chain include signal providers such as Harmonic, Synaptic, Specter, and others.
“How to not miss an investment opportunity anymore” = sourcing approaches
“Best startup databases” (we’re updating our 2020 study and will publish it soon, sign up here if you’re interested) = data sources
“How to scrape alternative data sources” = data collection
“How to create a single source of truth” = entity matching/deduplication
#2 Screening & Due Diligence
Cutting through the noise and prioritizing the right opportunities at the right point in time. Related episodes below. Tools focused primarily on this part of the value chain include data & research platforms such as Crunchbase, Dealroom, Pitchook, Tegus, and others.
#3 Portfolio Value Creation
Following the investment is where the real fun begins - supporting the management across hiring, sales, strategy, follow-on funding, and more. Leveraging our networks is crucial for introductions as much as for awareness across trends and competitive dynamics. I’ll write more about this part in the future. Tools focused primarily on portfolio monitoring and support include platforms such as Carta, Vestberry, Tactyc, and others.
Horizontal Layer
In addition to the above-mentioned best-of-breed tools, there exist solutions that intersect across the value chain including systems of record/CRMs like Affinity, productivity tools like Calendly, or workflow automation tools like Zapier and Bardeen.
While all of these tools are individually powerful, we face a lack of standardization and proper interface to synchronize them:
Webapp Wonderland: As most VCs are just migrating to best-of-breed stacks and have not yet started to streamline different solutions, they interact through web apps. Taking notes during your Zoom call, copy&pasting them into your Affinity CRM system, moving on to research via Dealroom and Tegus, to then write your investment proposal with ChatGPT, and interact with the founder in SuperHuman via email. Context switching and inefficiency at its best.
Data Siloes: In line with the web app wonderland, most best-of-breed providers have the ambition to create their own single source of truth. Consequently, there exist multiple database structures that are neither standardized in terms of communication/APIs nor for variable names, data types, etc. Synchronizing them is either impossible or a big pain.
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