Sourcing, surfacing, and analyzing data for better dealmaking

Venture Capital (VC) and Private Equity (PE) firms are increasingly turning to data to inform their investment, deal sourcing, and relationship management strategy. 

As discussed in our article, How data augments the investor, data can be used to introduce objectivity and confidence into the deal sourcing process, and to automate and expedite repetitive, but important, processes. No VC or PE firm uses data in the same way, but most would agree that it’s become a necessity in 2023. 

How that data is sourced, validated, surfaced, and used in the dealmaking process is critical to the success of any data-driven investment strategy. 

We continued the discussion with our panelists Rachel Feely-Kohl from F-Prime Capital, Aga Szefer from Bain Capital, and Orla Browne from Dealroom.co to learn how they’re optimizing their data-driven investment strategies. 

Here’s what they had to say.

Sourcing and validating the right data

Not all data is created equal, and not all data is relevant to all businesses. Sourcing, validating, and actioning the right data is critical. 

Two key factors are taken into account when validating data at Bain Capital. First, the data needs to be relevant to the investment and industry domains in which they operate. Second, the data needs to be accurate and trustworthy. 

“We’re constantly making decisions about what data can be used together,” says  Szefer, Bain Capital’s Director of Data Science. “That means continuously reviewing and refining our data sources.” 

Here’s an example of how that process works. Investors at Bain Capital have an idea about what data could help close a specific deal, or branch out into a new domain. They bring that idea to the data scientists who then suggests what data sources could be used to support the request. 

Then, they audit those sources to determine data accuracy, the extent of coverage, and it’s trustworthiness. 

Trust is a big factor in validating data. At Bain Capital, investors and data scientists work together in the validation process, and exchange feedback on the information they use. That feedback helps to inform changes to the data sourcing and validation process going forward.

For Feely-Kohl’s team at F-Prime Capital—who focus on automation and process enhancement—ongoing assessment is also key to monitoring the impact of data in their overall system. In other words, continuously measuring  the quality of results from data versus the manual way of operation. 

This helps her team continuously track the performance of F-Prime Capital’s data and automation efforts, and how they can be improved. 

Seeing through the data noise and finding signals

“Big data'' got its name for a reason. There’s a lot of it, and that means there are big opportunities to be uncovered. But with an abundance of data comes the risk of getting lost in the noise and focusing on the wrong information for the wrong use cases. 

Sifting through data noise to find strong, relevant, and actionable signals is a significant part of the battle for data scientists. But by doing so, they ensure that they only introduce the highest-value data to their systems. 

Bain Capital’s data evaluation process balances ongoing feedback from investors with experimentation and results monitoring. “We regularly talk to our investors to understand what data is most relevant to them in the process,” explains Szefer. 

This includes identifying data sources that might not be on the investor’s radar, validating them, and incorporating that data into their systems. These might be data sources that investors haven’t had access to before, but which are highly relevant to their dealmaking. 

Validating new data sources, explains Szefer, includes three key variables: 

1. Coverage. Do they have a good representative sample of the data already? If not, is there an opportunity to increase coverage?
2. Accuracy. Is the data correct? Can they validate it? Is there a more reliable source?
3. Timeliness. Is this the most up-to-date information? Will it remain up-to-date?

Feely-Kohl and the team at F-Prime Capital take that one step further. Once data sources have been validated, they then get to work on finding ways to integrate that data into their existing workflow systems, and find ways to automate tasks that their dealmakers are currently doing. They also introduce a feedback loop.

“We spend a lot of time trying to integrate the best data sources in our process,” explains Feely-Kohl. “There’s a real focus on the infrastructure, and where we are in the data journey now.” 

By continuously onboarding, validating, surfacing, and testing data within their core systems, F-Prime Capital is able to continuously supply insights from their tech stack to the investment team, which they use to steer future decisions.

Surfacing the data to investors

Data is only useful if it’s actionable. For VC and PE firms, that means surfacing the right data at the right time for investors to make the best decisions about prospects and portfolio companies. 

For Feely-Kohl and F-Prime Capital, that means ensuring that their investors don’t need to change their workflows to access critical data. Their data frontend is Affinity. This is where their investors can access most data from a variety of sources, and where they can input their own to build out more detailed customer and prospect records.  

This centralization of data plays into F-Prime Capital’s goal of putting people at the center of their data strategy. They want to use their existing people, expertise, and networks, and enhance results using data. 

This includes: 

• Understanding who their people are connected with
• Ensuring the entirety of the firm’s collective network is accessible
• Ensuring that data is complete for all client files
• Untangling data and making it easier to use
• Ensuring a clean and useful interface to surface more and better data 

“Anything we can do to accelerate the process and make people more proactive, not reactive, will make our overall processes as efficient as possible for everyone,” explains Feely-Kohl.

Planning for the future of data-driven investing 

The key takeaway that all three panelists agreed on is that data-driven investing will become more prominent and important in the years ahead. This spells a big opportunity for firms who use data well, helping them extend their capacity and source deals more intelligently. 

Bringing data into the investment process early helps to drive more confident decision making by reducing bias and extending the knowledge of individual dealmakers. 

“Data is the ally of the underdog,” summarizes Browne. “In times of uncertainty, data becomes even more valuable.” 

‍Learn more about how data-driven investing can help VCs and PEs thrive in the future. Join us at Campfire Berlin for more in-depth panel discussions on data-driven investing.

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