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In November, Allvue sponsored and attended PEI’s Private Funds CFO Europe Forum in London, getting the chance to mingle amongst operations and finance professionals across all private asset classes and discuss the top challenges and trends impacting the European and global private equity space.
While there, I had the opportunity to participate on a data analytics panel with Henry Kwok, Fund Controller at Orchard Global; Sahem Gulati, Head of Strategy and Consulting at M&G Investments; and Andreas Koukos, CFO and Executive Director at Brook Lane Capital.
Our discussion covered shared challenges of private equity firms when implementing an effective data strategy and the impact of unifying disparate data and embracing predictive analytics on operational performance. Here are our top takeaways from the state of data adoption in the private equity space.
The amount of data available to and consumed by private equity firms is ever growing. By putting that data to work in the right way, having access to these insights helps you make more educated decisions across the full fund lifecycle.
However, there are many barriers to making this data actionable, and the first step should include setting up the right infrastructure to serve as your data’s home. When looking to put your unstructured data into action, it’s key to start with a data lake or data warehouse model. By maintaining a dedicated environment for data to be scraped from documents (like spreadsheets or PDFs), cleaned, stored, and then carried in and out of the environment via API, you set your teams up for the ability to automate the access and analysis of this data, and turn it into actionable insights.
With this kind of infrastructure supporting the full firm, teams from the front to the back office avoid spending hours every week reworking data manually to access the insights they need to do their jobs.
AI is a hot topic across many industries at the moment, and private equity is certainly one of them. Our panel also spent time discussing how we’re seeing AI put to work by private fund managers in the meantime, and how we expect to see that role grow over time.
Allvue, for one, currently utilizes Ocular Character Recognition (OCR), a form of machine learning, in our Portfolio Monitoring product to speed up data collection. By feeding in static versions of private equity reports, the software reviews and extracts the data into the program’s existing fields. For example, if a portfolio company submits a statement, you can easily map that document according to your specified collection fields and avoid manually keying in the data. The use of OCR poses material time savings for fund operations professionals who currently spend too much time performing manual data entry, all while exposing their process to the risk of human error.
While data scraping might be a practical application of AI and machine learning that GPs can embrace today, much of the industry is looking ahead to see how generative AI will make its mark. The embrace of OpenAI’s ChatGPT has sparked enthusiasm for ways it boosts efficiency throughout the fund lifecycle, such as by providing content for completing due diligence questionnaires.
Generative AI may not be making a material impact on the private equity fund lifecycle today, but when it does emerge as viable, firms will want to be ready with a healthy data strategy and infrastructure to take full advantage.
When tackling a firmwide data strategy, it’s also important to find ways to measure success as the project is ongoing. Rolling out a data strategy is a multi-year process, and by proving value along the way, you enable organizational buy-in, allowing progress to continue. At Allvue, we push time saved as a key metric for our clients to document as they begin rolling out a broader data warehouse initiative.
In one example from my co-panelist Sahem, before launching an internal data strategy, his firm struggled to access an official dry powder figure accounting for each of the firm’s strategies. Without emailing and coordinating across multiple groups, gaining a realistic view of dry powder was impossible. But by deploying a firmwide data strategy, important figures like dry powder became possible and even efficient to access. When measuring and publicizing the data strategy adoption process, elements like these are critical. By making a point to document and publicize the time saved across groups by improving access to this key data, it’s easy to make the case for further data infrastructure investment.
In addition to defining metrics by which to measure success, it’s also crucial to keep the process flexible. Too often, we see firms partner with one inflexible provider in charge of the whole data strategy process, when in order to see broader adoption and impact, it’s best to find a provider that embraces flexibility and integrates with your other solutions, including the ingestion of their data. By hitching yourself to an inflexible provider and signing a multiyear contract, you risk falling into a multiyear rut and little progress to show for the investment.
For more information on other private equity events, including Allvue-attended ones, check out our round-up of private capital conferences.
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