In a study by Gartner they forecast “by 2025 more than 75% of venture capital and early-stage investor executive reviews will be informed using AI and data analytics.” AcuityKP found that only 4% of the private equity sector’s leaders currently use machine learning or artificial intelligence. “Firms over time are going to have to have these capabilities,” says Berkshire Partner’s VP of Data and Analytics, Adam Nahari. “In five or ten years, this is just going to be table stakes.” How is your firm integrating data science in private equity?
Venture Capital Data Science Classification
A study by Andre Retterath for DataDrivenVC.io classified VC firms into three groups:
- Old-School – 0 Data Scientists. These firms are focused on manual workflows, use a simple tool stack for CRM like Salesforce, have basic email and Slack/WhatsAPP for communication.
- Productivity – 0 to 1 Data Scientists. Successfully moved to modern off-the-shelf stack with VC-focused CRM systems like Affinity, automated workflows with Zapier and use Notion for knowledge sharing.
- Data-driven – 1 or more Data Scientists. They develop their own scalable solutions to capture data, automate workflows and bring the core of their business in-house.
Retterrath went on to say they found that data science activities scale efficiently as assets under management and the number of data scientists exponentially correlate. Data science in private equity still focuses primarily on the early stages of the venture capital value chain, specifically sourcing and screening followed closely by portfolio value creation and due diligence, and younger and smaller firms more broadly build tools across the value chain than their older and bigger peers. Some of this is due to problems of migrating data, changing process and just plain cultural reluctance.
Where to Focus Data Science in Private Equity
Use AI for target screening. AI can analyze unstructured data on the target including product reviews, news articles, press releases and internal publications. Data science in private equity can be used to train an AI algorithm on the history the firm has with startups and founders and use that algorithm to predict or assess the future success of a given startup. Given enough data AI can detect patterns or behaviors that a human eye can’t. The algorithm can also be constantly updated as new information becomes available, keeping it current for everyone across the organization.
A more common and easier approach is to use data science in private equity to focus on post-acquisition. Data is more accessible after acquisition and therefore easier to incorporate with existing databases. The focus will be on improving company performance and value creation, and using AI to track the key performance indicators (KPI) that are most correlated with the business’ success has a high payoff.
A September 13, 2023 Wall Street Journal article reported that “small and midmarket firms, such as Access Holdings Management and Frazier Healthcare Partners, have been adding data science experts to their ranks, while larger firms and early movers, such as EQT, Partners Group and Two Sigma Investments, have built sizable teams. Since it launched its Motherbrain data analysis and machine learning platform in 2016, EQT has built a team of more than 40.”
Is your firm keeping up?
