The Modern Art and Science of Venture Capital

In my view, venture capital (VC) and Private Equity (PE) reflect the modern art of scouting great investments. It is modern art for a reason. While years of experience and good instincts are great, they may not be enough in a world with increasing amounts of available data and potential deals.

VCs are known as the financial engine for new technology companies/start-ups. These funds accept greater-than-average investment risk, mainly in the short term. To mitigate these risks VCs are using available data and information to increase the odds of higher, long-term returns. Extremely successful VCs gain large returns as compensation for their investment risks.

Yet, the rules of engagement in the investment arena are changing as technology evolves. The operational structure of the venture capital industry is changing. I can see more VCs using relevantly new functions such as business development and applied data science experts. In some cases, it is a combination of both, some sort of "business technologists".

In a big data world, it seems natural for VCs to consider adopting applied data science methodologies. According to Gartner, more than 75% of venture capital and early-stage investors will be informed using artificial intelligence and data analytics to make investment decisions by 2025.

It makes sense. Financial information, techniques, business signals monitoring, key performance indicators (KPIs), and much more data - all need to be processed and considered in the smart and best possible way.

Leveraging knowledge in a new way will allow VCs and PEs alike to validate their findings and enable more innovative investment decisions. Effective knowledge management is one important factor in that equation. The other requires a structure and new functions that are not so common or even intuitive. The main challenge is to implement all relevant techniques of data science for integrating it into VC processes.

How do I know it?

During the past years, I have gained experience as a business leader (3M, Unilever, AMEX) as a start-up business development expert, and even as a venture partner. Surprising as it may sound, I was fortunate to complete the IBM specialization and earned the Data Science Professional Certificate, a few months ago. It was great to demonstrate the ability to unlock insights from complex and diverse data and develop models to solve real-world business challenges. Based on my combined and diverse experience, I strongly believe that data science methodology could benefit VCs.

It may be considered a highly technical function, yet solid critical thinking skills and the ability to synthesize complex problems are not enough. This function is also required to effectively communicate the outcomes of the findings by telling and in most cases "selling" a concise business/investment story.

From deal-flow management through due diligence and investment decision, data science plays an important role. It can provide investors with more transparency and assist in avoiding costly mistakes. Investors want better returns, and data science can help them find those opportunities.

With increasing amounts of available data and potential deals, the use of applied data science methodology for investment could significantly help VCs achieve the desired large returns.