AI is Exciting, but Data Science Remains Imperative to the Evolution of Venture Capital

‍This guest post comes from our CTO, John Harris, who holds degrees in Quantitative and Computational Finance and Electrical Engineering. He is at the forefront of noteworthy progress that BIP Ventures is making with emerging capabilities.  

Is data science the game-changer for Venture Capital (VC)? Based on how we use it across the firm and with our portfolio companies, we believe so.

First, an explanation of this sophisticated ecosystem. Data science is an interdisciplinary field. It merges statistics, data analysis, Artificial Intelligence (AI), and its subset, Machine Learning (ML).  

The principles of data science have been around for centuries, but its tools (e.g., AI and ML) have proliferated more recently thanks to the growth of digital computing. In the 1950s, the finance sector became one of the earliest to adopt data science, using statistical methods and computer simulations to manage financial risk and optimize returns. Nonetheless, it wasn’t until the 2010s that data science gained real adoption among VC firms.  

‍The Four Main Ways VC Firms Use Data Science

The VC industry has grown out of a legacy of human judgment and relationships. With that in mind, it's worth considering how effective data science can be for improving decision-making, portfolio management, and investment outcomes.  

We think the answer is very effective.

Leading VC firms – BIP Ventures included – are using data science to manage investments and produce the best possible outcomes for portfolio companies. That makes sense, given that data science is a proven resource for utilizing information from historical events to help clarify and manage potential future outcomes. The resource has created a vital edge across our investments in healthcare, media, strategy, payments, and many other verticals.  

Specifically, we believe that data science is crucial to improving four areas of portfolio, investment, and firm management:  

1. Deal Sourcing and Due Diligence  
2. Portfolio Management  
3. Business Operations  
4. Exit Strategies  

Deal Sourcing and Due Diligence

Across the portfolio and investment sides of the firm, managers use data science to gain efficiency, visibility, and risk management capabilities. Firms use data science to analyze a broad range of micro- and macroeconomic signals like patent filings, social media sentiments, and signs of industry disruption. By filtering through massive amounts of data, VCs can identify startups earlier in their lifecycle and more accurately spot the most promising ones. Once identified, firms can use automated tools and ML models to assess factors like market size, competitive dynamics, and team quality to form a prediction of company success. And on the investment side, data science resources support more thorough due diligence, which helps to reduce risks associated with investments.  ‍

Portfolio Management

Once a VC makes an investment, data science is a valuable tool for managing the portfolio. Specifically, it's a tremendous resource for ongoing assessment of performance indicators. The data can identify headwinds and tailwinds, forecast capital allocation strategies, and call attention to when to begin planning for an exit.

Many VCs use data science to monitor how well their portfolio companies are performing against Key Performance Indicators (KPIs). They also keep tabs on the market conditions that could impact a portfolio company’s competitive and sales positioning. By monitoring data in real time, the VC can flag issues early and act on them before they become serious threats to the business.

In addition to providing financial visibility and scenario planning, high-quality data analysis can be used to provide value-add to portfolio companies. For example, some VCs are using data science to help portfolio companies with tasks like product development, marketing, and sales that are shown to help portfolio companies to grow and achieve their goals faster. ‍

Business Operations

Data science is widely used for improving operational efficiencies and process optimization, both for portfolio companies and across a firm. By automating routine tasks related to deal sourcing, due diligence, and even some aspects of decision-making, VCs streamline investment processes and improve operational efficiencies. And by identifying business process inefficiencies within portfolio companies, teams can help the company become more operationally efficient and potentially more profitable.

‍Exit Strategy

Just as important as data science is on the front end of an investment and during the maturation of a portfolio company, it also is a key asset in exit planning. At some point in an investment, the VC and portfolio teams begin to consider ways to reallocate capital or create liquidity for investors. Detailed scenario planning and the practice of regularly monitoring key market and business trends enable teams to make swift, confident exit decisions. They also help with evaluating potential buyers or IPO opportunities and with optimizing the timing and exit structure for maximum returns.

‍Spotting Effective Data Science Use

BIP Ventures is in great company with firms that are at the forefront of using data science. As it becomes even more prevalent among VC firms, we expect to see even more innovation.  If you are a founder or investor who is assessing how well your VC partner is putting powerful emerging AI technologies resource to work, consider the four primary uses we’ve outlined above as well as some tell-tale indicators of innovation in the space:  

• The firm has a team dedicated to using data science to identify promising startups, assess the risk of investments, and support portfolios with talent, customer acquisition, and regulatory considerations.  
• The firm is using data science to identify trends and find startups that are well-positioned to succeed.  
• The firm is using ML to analyze massive stores of public and proprietary data points to identify patterns and trends that indicate market shifts and investment opportunities.  
• The firm takes an algorithmic approach to VC, analyzing multitudes of data from different sources to make investment decisions.  
• The firm employs predictive analytics and information from current and historic data sources to make investment decisions that are free of traditional bias risks.  
• The firm takes a hands-on approach to using data science to guide strategic decisions for portfolio companies.  

Used properly, data science is a powerful tool that can help VCs improve their investment process and produce better outcomes. Especially as AI and ML proliferate across every operational and strategic component of the firm and its portfolio, an understanding of how data science can and should inform portfolio and investment management will differentiate the good VC firms from the great ones.  

If you have questions about data science, or how ML and AI are working to make VC an even more powerful resource for the Innovation Economy, reach out.

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