Data-driven PE: value creation with repeatable analytics and ML

For an investment firm, applying data analytics and machine learning (ML) can unlock new value, growing capabilities in portfolio companies and within our own. But as highlighted by Casado and Bornstein, developing these approaches brings challenges, in part because business problems and data inputs vary across companies — unlike traditional software development in which problems and inputs are often more easily defined.

This adds up to increasing cost and development time to maintain analytics, reducing their adoption and robustness, and increasing the time to value. Ultimately, we see a decline in return of investment, compared with traditional software where there is typically a low marginal cost (and high margin) once products are developed. As Casado and Bornstein note, the reduction in margins gets worse given additional cloud costs and requirements for on-going support and maintenance. (Although there are product companies that develop analytics products for more specific or narrow use cases, these products still require configuration and customisation where data or problems change slightly.)…