How data augments the investor

Dealmakers have historically relied on personal networks, gut intuition, and industry knowledge to make investment decisions. But times are changing. As technology adoption continues across the investment world, big data has become pervasive—and so has the usage of that data in all stages of the investment lifecycle. 

Data science in VC and PE: an example

According to Affinity’s 2023 investment predictions report, investors spend, on average, 34 hours researching each deal. During that process, 75% of investors report using at least four data sources to enhance their research and findings. That’s a good start, but the process can be even more efficient. 

For example: 45% of all deals still come from connections in a dealmaker’s existing network. That means they’re known contacts, sourced manually using traditional networking techniques. 

What if, instead of sourcing deals from each person’s individual networks, the firm was able to combine everyone’s networks into a single data pool? The result  would give full visibility into the connections that every team member has, allowing teams to identify trends, opportunities, and streamline outreach processes across the super network. 

That’s a whole new deal pipeline that didn’t exist before, thanks entirely to data-driven investment processes. 

This is just one example, but there are multiple benefits associated with using data to drive decision making in venture capital and private equity. 

Let’s dig deeper.

The growth of data-driven investment: a discussion 

To gain further insights, Affinity sat down with three experts to discuss the role that data currently plays at their firms—and how they intend to use it in the future. 

The panel featured: 

• Rachel Feely-Kohl with F-Prime Capital, a firm that creates and invests in healthcare and technology companies that impact lives all over the world.
• Aga Szefer with Bain Capital, one of the world’s leading private investment firms.
• Orla Browne with Dealroom.co, a global data platform for intelligence on startups, innovation, high-growth companies, ecosystems, and investment strategies .

Below, we go into detail on two key questions about data. First, how firms are using data today. And second, how they're solving the challenge of balanced gut-driven and data-driven investment styles.

How firms are using data today 

The presence of data in a deal pipeline or CRM doesn’t, in itself, guarantee success. Instead, dealmakers need to be strategic and tactical about how, where, and when they use data. 

For Aga Szefer of Bain Capital, that means using data to identify exceptional founders and investors in their investment domains. Their goal is to bring more confidence and objectivity to the investment process. 

“More data and more signals help our investment teams make better decisions,” explains Szefer. 

To do that, Szefer’s team uses existing data sources—and creates and finds new ones—to help them create a fuller and more accurate picture of their deal flow. 

Rachel Feely-Kohl’s team at F-Prime Capital uses data a little bit differently. For them, data is a tool to create more efficiency in their internal processes. It helps to inform where and how automation can be introduced, helping the firm become faster and more resilient. 

“VC is about getting in the room first,” explains Feely-Kohl. “We provide our investment teams with the tools and processes they need to engage with and find data signals to complete tasks that they would otherwise do manually.” 

As an example, data management helps the F-Prime Capital team better understand and use their network, connect with the right people, and incorporate customer interactions into their decision making. 

Orla Browne with Dealroom.com adds that her clients are using data to discover startups and investment opportunities that aren’t currently on their radar. 

“There’s a trend in recent years toward earlier and earlier investing,” explains Browne. “This means that investors want to cast their net a little bit wider and discover companies that aren’t necessarily in their current network. In some cases, that means finding companies before they’re even companies.” 

Onboarding and incorporating a variety of data sources across industries and geographies in their workflows is one way that Dealroom’s clients are casting that wider net. For example, Dealroom actively sources and integrates data about fledgling university research and technical teams that have the potential to turn into companies in the future.

Balancing gut-driven and data-driven investment styles 

Investment has traditionally been more of a gut-driven practice than an objective science. And, there are benefits to the traditional way of doing things—especially when it comes to relationship management. 

Because of that, the growing use of data in dealmaking is raising questions about how to balance traditional investment and data-driven styles to ensure that dealmakers get the best of both worlds. 

“Both are very valuable, and we’d like to marry both approaches,” says Szefer. “We want to leverage the expertise and experience of our inventors in their domains, and use data to enhance it.” 

Feely-Kohl agrees. Her team at F-Prime Capital uses data to replicate and enhance the thought processes and execution of what their dealmakers do manually. That includes not only deal sourcing and outreach, but also portfolio and relationship management. 

Data, therefore, is an extra tool that creates opportunities to enhance knowledge, automate processes, and improve the overall impact of the firm’s existing operations. 

Similarly, Dealroom’s clients primarily use data to keep tabs on trends in the industry to help them source deals better. But, explains Browne, that’s not at the expense of strong relationships and networks. 

“You’re going to be working with [founders you invest with] for years,” says Browne. “So you’re going to need to build a relationship with them and not just focus on understanding the numbers.” 

Both investment styles have their place. The goal is to find opportunities to enhance the overall investment lifecycle by leveraging traditional and data-driven strategies where and when they make the most sense. In all cases, these two styles should each improve the other, and create an overall better investment strategy. 

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