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The Role of Data Science in Private Equity

By April 14, 2022May 30th, 2022No Comments

The proliferation of the internet and connectivity has brought the ability to store exorbitant sums of data. This has propelled us into the age of big data and the development of the data science field. As the Data Science in Finance Conference draws to a close in Amsterdam, we can’t help but notice that the Private Equity industry is still in the early stages of data science adoption. While major players are rushing to squeeze every last ounce of predictive insight from their deep coffers of proprietary data, many smaller players have been relegated to the sidelines. The private equity industry is nearing the transition point between the “awareness raising” and implementation phase. In the world of today, it is no longer uncommon to see Python coding as the required talent for any investment analyst role.

The key data science use cases in PE can be divided into prediction and detection. The use of analytical engines to provide accurate outcome scenarios is one of the major areas data science can build upon. Large firms with robust datasets can feed the information into analytical engines in order to predict success rates or identify firms that have comparable traits with successful companies. Although larger firms do have an advantage in this use case, data is becoming more commoditized. Smaller firms  can still generate robust models by acquiring and consolidating  firm level data.

The second major use case is in anomaly detection for investment screening. The due diligence phase offers the unique opportunity  of combining traditional evaluation methods with anomaly detection algorithms. By using these data science based methods PE firms can simultaneously spot both positive outlier opportunities as well as avoiding loss by highlighting problematic data points before an investment is made.

Due to the opaque nature of private equity investments, there is often a lack of  data points to generate “traditional metrics” during company valuations. In such cases the use of AI or machine learning processes can help uncover the true value drivers and create hybrid models that are more relevant and better fit the valuation scenario at hand.

From predictive models, performance monitoring, and insight generation, the possible applications of data science in private equity are endless.  We believe that investors that can integrate data science talent with investment acumen are bound to have an edge against their traditional counterparts.