Data science for private equity and venture capital

- [Host] At this point, you're probably convinced that data science is critical for any company, let alone private equity or venture capital. So a key question might be why are we specifically focused on private equity and venture capital firms being particularly well positioned to implement data science? There are a number of reasons. The first reason, which may be the most critical and unique is the portfolio effect. The portfolio effect refers to the fact that a venture capital or private equity firm has a portfolio of companies that they're invested in. As a result, they can implement cross-learning from one portfolio company to another. Additionally, they have a convening power to bring companies together as well as to draw key learnings that would be relevant for most of their portfolio companies. The second reason is that they have access to diverse data sets. Because they're involved in so many different areas, those data sets could span across different industries, markets, and functions. This offers the opportunity to understand a broader set of information and the need to be able to analyze that effectively. The third reason is embedded within the DNA of venture capital and private equity. That is these industries are dependent on data-driven decision-making, the need to use data to access financial performance, market dynamics and modeling growth potential is already embedded in these organizations using predictive modeling, machine learning and statistical analysis can be readily adopted to identify risks, predict market trends, evaluate investment opportunities, optimize portfolio performance, and even assess opportunities to exit. Another reason is the skill at executing playbooks. Private equity firms are very skilled at implementing procurement playbooks, where in the first 100 days or so, they quickly identify and implement ways to more efficiently and effectively procure goods and services. A data science playbook could be readily developed and implemented in a similar matter. Lastly, when we think about private equity and venture capital firms, we tend to think about the lifecycle of opportunities, with that lifecycle of opportunities, there are different data science opportunities at each stage, early exploration, due diligence, operation stage when you have access to a lot more data. And lastly, the exit stage. The take home message is very clear, the structure and operations of private equity and venture capital firms with its collection of portfolio companies naturally opens up greater opportunities for data science than if you only had a single company you were investing in.