How to Start Your Data-Driven Journey, Tool Trends, Biggest Challenges, Measuring ROI, & More
Insights, Learnings, and Expectations from the Data-Driven VC Landscape 2024
👋 Hi, I’m Andre and welcome to my newsletter Data-driven VC which is all about becoming a better investor with data & AI. Every Tuesday, I publish “Insights” to digest the most relevant startup research & reports. Every Thursday, I publish “Essays” that cover hands-on insights about data-driven innovation & AI in VC, and every Sunday, I publish “Picks” to spotlight the hottest Stealth, Early, and Growth Startups. Follow along to understand how startup investing becomes more data-driven, why it matters, and what it means for you.
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Following the successful launch of our Data-Driven VC Landscape 2024 early May, I sat down with one of our partners Affinity to provide my view on findings, learnings, and future expectations here. Today, I’m happy to share the full interview with you below.
How can VCs become more data-driven, and what’s commonly overlooked?
Studies have shown that two thirds of venture capital value is created in the sourcing and screening parts of the process. It’s a game of finding and picking the winners. This is why VCs typically start their data-driven journey with deal flow generation, deal flow screening, and deal flow management. Data can help identify new opportunities, enrich them with additional context, and prioritize the great ones to find the inflection point that tells you it’s the right time to engage.
Where I see VCs overlook data-driven opportunities is with portfolio value creation. Most tools focus on the first stages of the value chain (sourcing and screening) and few firms have built tooling to support portfolio value creation.
There’s an opportunity here to help portfolio companies achieve their goals. Consider what’s involved in portfolio management—things like supporting introductions to potential customers, talent, investors, and even acquirers down the road. With the data on your firm’s network of relationships you can build something like a LinkedIn on steroids. Whether portcos are looking for talent specific to industry, specialism, or location, you can help find and match the best people.
And then there’s other components like competitive landscape benchmarking. Firms already do a competitive landscape analysis during due diligence, but these insights can also be really helpful for portfolio companies—giving them a bird’s eye view of their market that they can keep tracking and benchmarking themselves against with metrics like headcount growth, website traffic, and funding raised.
What are the biggest challenges that data-driven investors face?
There are two main challenges that data-driven investors face: product-related, and cultural.
#1 Technical challenges
VCs have to decide what to build versus buy off-the-shelf. There’s not always a need to reinvent the wheel, but you do need to be able to identify opportunities where you can make a difference and create a competitive edge.
Once that’s decided you also need to think about talent and team structure. You need to solve technical issues related to data collection, entity matching/deduplication, all the things that it takes to make data actionable. Once you have the full cycle of investment data in the system, that’s when you can start to build a data flywheel.
#2 Cultural challenges
Cultural issues can be broken down into two categories: top down buy-in and teamwide aversion to change.
The first relates to General Partners, those who eventually own the budget for data-driven initiatives. It’s a question of whether the firm’s leadership is truly bought in on the approach, or if they’re window dressing to keep up with the competition or appease LPs. Ultimately, are they fulfilling an intrinsic or extrinsic need? Because those carry different perspectives and that impacts the available budget and how you’re able to think about innovation.
The second part of cultural challenges comes down to this: we work in the financial industry and many people were trained in very traditional institutions—investment banks, consultancies, and other places with traditional processes. Since venture capital began in the 1950s, the only real innovation has been from pen and paper to computers. People are used to working in a manual way and thinking they can increase the output by increasing the input. But that doesn’t apply in VC.
We need to culturally change how people interact with their daily tools, and how they think about time allocation. Getting investment teams to adopt a new platform is the biggest area where I hear firms are struggling.