Future of Alternatives 2025: Big Data Will Drive Big Changes in Fund Operations

Advances in data analytics and investor demand for more information will drive data-oriented organizational transformation

‘Big data’ is a catch-all term used to refer to large and complex datasets that often cannot be handled by traditional software such as Microsoft Excel. Such data might take the form of social media posts or geolocation data that cannot be organized into neat tables without some processing. In these cases, machine learning algorithms would be trained to read through the data to spot patterns and trends. At the heart of big data is the idea that as new forms of data become analyzable, firms have more tools to deploy to improve their decision-making.

While data-driven models have been used by many quantitative hedge funds for years to find new sources of alpha, and some private equity firms are also using big data to inform their investment decisions, this technology is still very new to most. A majority (59%) of fund managers polled by Preqin for Future of Alternatives 2025 do not currently harness big data in any way (Fig. 1). We will see an increase in the adoption of machine learning and other data-processing techniques over the next five years: 42% of respondents say they will use big data more by 2025.

Data Integration Efficiencies
However, two forces will push managers to develop clear data strategies going into 2025. First, investors are increasingly demanding more data from managers, and they want this information delivered to them faster, and in the exact format they require. Second, as funds come under more pressure to keep operating costs down, many aspects of fund administration and reporting that still rely on manual processes will have to be replaced with scalable and more efficient data integration and data management solutions.

At larger funds, talent with quantitative research and data science backgrounds will be in demand. Smaller managers may not have the resources to build their own analytics tools, and will look instead to outsourcing. Investment service providers and fund administrators are already investing heavily in various portfolio analytics tools and interactive dashboards. At Preqin, for example, we have used our expanding database of funds to develop a cash flow forecaster that enables LPs to model the exact timing and size of capital calls and distributions for a specific fund or collection of funds. This helps LPs monitor their liquidity risk, make better re-investment decisions, and visualize how returns will evolve over time. Prospective LPs also use Preqin’s J-curve and cash flow pacing benchmarks to screen for GPs that best match their performance, liquidity, and risk management constraints.

Outside of fund operations and portfolio management, investor relations are another area that could benefit significantly from the use of technology over the next five years (Fig. 2). Some respondents are planning to use AI to automate interactions with existing and potential LPs; the client insights they glean from models enables them to be more targeted in sales and marketing efforts. Some managers are using predictive algorithms to identify specific product cross-sell opportunities, while others have algorithms that can identify clients at risk of redemption for specific strategies, according to McKinsey.

We expect a gradual increase in the use of analytical tools to capture and present data more effectively. Key to the adoption will be the role of external providers, including technology firms, data providers, consultancies, and professional services firms, who have the scale and business model to develop practical solutions.

Download a data pack containing all the charts in our regional articles for Future of Alternatives 2025. For more predictions and projections on the future of the alternatives industry, visit Preqin's Future of Alternatives 2025 Content Hub.