Tech Stack In A Box: Your Silver Bullet To Cut Through The Noise
Join & Test "VC Tool Finder"
👋 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.
Curing The Pain In A Race For Value Capture
Thankfully, first-mover best-of-breed providers are listening closely to the users. In an attempt to cure the increasing pain, they formed partnerships to synchronize their solutions more seamlessly. A great example is the Affinity x Vestberry partnership, and I know from different sources that more alliances are forming.
While the outspoken ambition is curing the user pain, the natural strategy for every best-of-breed provider should be to own the user interaction and expand value capture across the stack. Initially through partnerships but longer term the question of make vs buy might become more relevant. The tide has turned and the bundling has begun.
Looking at potential scenarios for the bundling phase ahead, I see two options:
From Within: Existing best-of-breed providers form alliances and capture increasingly more value. It’s a nice win-win as customers from player A might become customers of player B and vice versa. The key question, however, becomes who owns the relationship going forward. Both? One? Who? In the “growth from within” scenario, this becomes key as customers seek to reduce interaction points.
From Outside: While existing players’ ability to integrate with other providers might be a secondary priority (vs their core best-of-breed value proposition) and constrained in terms of resources, there is an option for a new breed that is purely focused on horizontal integration. In the first place, these providers won’t compete with the best-of-breed as they tend to start as pure integrators with a focus on workflow automation. Once they provide sufficient value to the customer and own the interaction as a one-stop-shop, they might naturally think about make vs buy for the individual best-of-breed components.
In addition to the question of “who owns the user interaction”, LLMs and generative AI will play a central role in defining “how the user interaction looks like”.
While for some use cases, we might still require dashboards and traditional web apps, many tasks can be better served with an AI agent or ChatGPT-like interface.
Innovating the “how an interface looks like” with a thoughtful balance between web apps and AI agents might become the core driver to answer the question of “who owns the interface”. In my perspective, it’ll be the first player to bundle different value props into a seamless, unified experience.
The Silver Bullet: Tech Stack In A Box
Looking into the questions and comments that I have received from our DDVC community over the years, the majority center around the struggle to cut through the noise and find the right tools. With as little effort as possible.
In a perfect world, investors want a simple but powerful “Tech Stack In A Box”. Enter your firm info like fund size, target stage, geo focus, and ticket size, to receive a tool stack custom build for your needs. Plug&play, reducing the time to value from months to minutes. No research, no trials, just a stack that works.
Launching “VC Tool Finder”
Until someone develops this silver bullet off-the-shelf (no matter if “from within” or “from outside”), I decided to leverage the insights from our Data-driven VC Landscape survey and create a “VC Tool Finder”. It’s a community-driven platform that removes the need for tool research and helps you find the best available stack for your needs.
No research, no lists, no landscapes. Just the best stack for your needs.
How? I expanded my “list of 400+ tools” that I shared as part of the Data-driven VC Landscape 2023, collected input from 200+ leading VC firms on which of the tools they use today, and created a “calculator” to dynamically find the right tool stack for you, depending on your firm characteristics and the stacks of similar, tool-wise more advanced firms.
If you’d like to join the closed beta for “VC Tool Finder”, sign up here and forward this post to everyone who might be interested.
Stay driven,
Andre
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