The private equity industry has experienced substantial growth over the last 30 years. There are currently over 3,500 firms globally with over $1 Trillion in dry powder (nearly 2x the amount in 2012). We have witnessed the industry maturing and becoming more competitive than ever. The business model of the industry has proven to be very successful and lucrative, incentivizing bigger funds, the creation of more and more firms and thereby crowding the marketplace, intensifying competition for the most attractive targets.
In such a high stake environment, the traditional industry model is increasingly being upended. Less information asymmetry between investors and entrepreneurs, less reliance on financial engineering to generate returns, and more focus on differentiation and value add services. Firms must fight harder than ever for an edge. In an era where data is the new oil, I believe that tech-savvy, data-driven investors will gain an advantage over their peers.
PE is not unlike the legal or accounting industries. Partnerships build on trust and strong personal relationships cultivated over years. Competitive and dynamic, yet traditional in many ways. While many industries they have served have been disrupted or at least considerably modernized, the PE industry itself has been more modestly impacted by the rising tide of technology. Investors pride themselves on being forward thinkers and business leaders, but the majority of them mostly rely on the same tools they have used for two decades: Excel, PowerPoint and Google. In short, there is often a disconnect between the use of technology in the PE industry and in their portfolio companies. Think about it for a minute. When was the last time that partners at your PE firm discussed the tech stack of your organization? Is it a priority?
One would think that within PE, VCs would be at the vanguard of change. Yet, according to Knowledge.VC, fewer than 5% of US VCs have a full-time team member focused on technology. As they say, the carpenter’s house always needs work! While it may be the tip of the iceberg, I see a growing number of VC firms leveraging data as a source of competitive advantage. Groups like Signalfire, Correlation Ventures, Social Capital, Georgian Capital and Coatue Kona are at the forefront of this trend. I suspect that later stage PE investors will gradually follow in their footsteps.
The bar is being raised in multiple ways in the PE industry and technology is a key enabler of progress: execution speed, insight / competitive intelligence, value add, portfolio monitoring, risk management, security / confidentiality, organizational structure, and engagement and transparency with LPs, among others.
Why now? I have noticed several factors driving this trend: higher expectations from stakeholders, fierce competition among industry participants, and the need for better alignment with the changing needs of portfolio companies. In addition, technology is enabling this trend: the sources of alternate data are numerous and rapidly expanding (90% of the world’s data was created in the last 2 years alone), the cost of technology continues to diminish, and SaaS is providing infinite scalability. Moreover, several applications of artificial intelligence are very promising to further improve the productivity of the industry and free human resources from lower impact endeavors to higher value creation initiatives. There is an argument to be made that AI could contribute to making the PE industry more efficient by integrating previously hard to quantify/analyze unstructured data and favoring data over guts, among other benefits. The “Moneyball” approach has been adapted and applied to many fields. Why not in the evaluation of talent and potential investments?
In an environment awash with dry powder, where capital is often perceived by entrepreneurs as a commodity, I anticipate that higher expectations from employees, portfolio companies, and LPs will contribute to create a divide between firms and raise barriers to entry. Early technology adoption leaders have been spending millions annually on data acquisition only. I believe that we can expect that further technology adoption will lead to a shift in the budgets and resources allocated to more properly support the needs of the back / front office as well as provide the level of insight and assistance expected by portfolio companies.
This reflection led me on a journey to identify the best resources that I could leverage to improve my professional effectiveness and efficiency. I spent a considerable amount of time the last few months to learn from early adopters, research new datasets, and experiment with novel tools and solutions. My colleagues and I have identified over 200 resources. Stay tuned. I will share in my next blog post the highlights of our findings.