Bridging the data gap: The core of private market intelligence 

As institutional capital continues to flow into private markets, the availability of data, tools, and a consistent approach in supporting those investment decisions could potentially struggle to keep pace. The challenge for investors is evolving beyond access to the opportunity and is increasingly a matter of access to high-quality, decision-ready data.

In public markets, investors arguably take for granted the availability of deep pools of data governed by standardized taxonomies that allow investors to benchmark performance, model risk and compare investments. In private markets, however, none of that is guaranteed.

As private market allocations grow and portfolios diversify, the challenges of delayed, incomplete, and inconsistent data are amplified. For an asset class that demands, on average, more data to enable optimal decision-making, in practice, private markets often can be the least served. Without reliable cross-market data covering both public and private exposures, Limited Partners (LPs) can be forced to make decisions with only a partial understanding of the impact on their portfolios' risk, performance, or liquidity.

Worse still, traditional systems weren’t built to scale across asset classes. Gaps between public and private datasets mean investors can’t easily unify performance metrics or automate reporting.

Meanwhile, factors such as, new fund structures, GP-led secondaries, and continuation vehicles introduce further complexity, and with it, a growing need for advanced analytics.

In a recent episode of Critical Thinking, Mercer’s podcast series exploring and debating the themes, trends and opportunities shaping markets, industry specialists from Mercer, S&P Global and Cambridge Associates examined one of the most pressing challenges facing private markets today: the growing gap between capital flows into private markets and the data infrastructure propping up the industry.

As allocations to private equity, private credit and other private assets continue to rise, the group highlights how the systems supporting investment decisions have failed to keep pace. What was once a challenge of access has evolved into a challenge of data quality, consistency and usability, with significant implications for how investors benchmark performance, assess risk and allocate capital.

^{*References to a "partnership" in the podcast are informal. The relationship described is a "collaboration".}

We believe a compelling solution to this persistent challenge lies in fostering collaboration among like-minded firms to aggregate and standardize General Partner (GP) data. GPs often hold the most current and granular information, which is precisely what investors value most in private markets.

The paradox is clear: while demand for this kind of data is near-universal, access remains limited as most market participants are reluctant to share their information, creating data silos.

We recognize the importance of safeguarding proprietary insights and protecting commercial advantage. At the same time, we see merit in a collaborative model underpinned safeguard to anonymize data outputs, which could help overcome these barriers and allow participants to access meaningful shared insights without compromising sensitive information.

Today, meaningful peer comparison from the perspective of private market funds can be difficult. Assessments are typically limited to headline performance and historic track records, which could fail to provide the necessary context or operational nuance.

For high-performing GPs, supporting greater transparency can offer a more robust framework for benchmarking differentiated results. For others, it may provide a valuable feedback mechanism to better understand relative positioning and identify potential areas for enhancement in an increasingly competitive capital-raising landscape. 

That is why we are working with S&P Global and Cambridge Associates on a clearinghouse approach to investment data, in which fund and asset data is centralized, validated and normalized, with a view to make it available in a "one-to-many" reporting structure in the future.