S&P Global, Cambridge Associates and Mercer have launched a joint private markets performance analytics product, the first commercial release from a collaboration announced last year. The initial datasets cover private credit and real assets, with private equity to follow later this year.
For institutional data teams, what’s interesting about this launch isn’t so much the dataset itself, but the infrastructure underneath it, and where it’s heading.
The product is built on iLEVEL, S&P Global’s portfolio monitoring platform, which already serves roughly 700 clients. Of those, about 250 are investment consultants, limited partners and other asset owners. Through its managed data services operation, iLEVEL collects and processes GP-to-LP reporting – financial statements, quarterly letters and supplemental materials – on behalf of those investors. The result is a data footprint that touches approximately 16,000 funds across the market.Chris Sparenberg, Head of Private Markets Strategy at S&P Global Market Intelligence, says the collaboration grew out of the realisation that iLEVEL had organically built something with much broader potential. “We realised, in conversation with Cambridge Associates and Mercer, that we had essentially built a clearing house for the industry without really trying to,” he tells Market & Alt Data Insight. “We serve a function in the industry to normalise data on behalf of our clients, but there is much more we can do, and the industry needs it.”
Taxonomy first, dataset second
The initiative has two components, and the order matters. Before building the dataset, the partners created a new private markets taxonomy, a standardised classification framework for fund strategies, private asset types and transaction types.
The impetus, according to Sparenberg, is that existing classification frameworks have failed to keep pace with how the industry has evolved. “In private credit, for example, a lot of industry sources stick with what you might call the big three – direct lending, mezzanine and special opportunities – but there is a range of sub-strategies within those that aren’t reflected in other datasets,” he says. The consequence is that managers struggle to find meaningful peer cohorts for performance comparison, and LPs end up with large portions of their portfolios grouped into categories too broad to be analytically useful.The new taxonomy uses a nested hierarchy with up to four levels of classification at the fund level. Rather than applying all four levels uniformly, S&P Global developed a proprietary process that examines fund offering documents and holdings, and assigns classifications as deeply as they are relevant. “Some funds are diversified direct lending funds covering investment grade and sub-investment grade with broad sector exposure, and there’s a way to designate that accurately,” Sparenberg says. “Others are highly specialist – doing leasing and yield strategies within opportunistic credit, for example – and you can go quite granularly into those as well.”
The same approach has been applied both at the asset level, covering corporate assets, credit instruments, real estate, infrastructure, natural resources and energy, and at the deal level, where the challenge is less about classification and more about normalising what already exists.
For iLEVEL clients, the taxonomy operates as a third layer of enrichment. The platform collects data as reported by managers, maps it to clients’ own proprietary classifications, and then applies the S&P Global–Cambridge Associates–Mercer framework on top. Clients can use any of these views interchangeably.
The data: primary sources, with GP consent
All of the underlying data is derived from GP-to-LP reporting – the quarterly reporting packets that managers already provide to their investors. S&P Global’s managed data services team collects this on behalf of its LP and consultant clients, which is how the superset was assembled.
Critically, the product was built with active GP participation. S&P Global solicited consent from contributing managers rather than passively repurposing data already in its systems. “Managers were informed throughout the process, and we’re actively working with the GP community to expand participation and strengthen the breadth of the dataset,” Sparenberg says. The collaboration includes a give-to-get model designed to provide differentiated value back to participating managers.
The first release focuses on fund and deal performance for private credit and real assets, with additional datasets and asset classes to come as the initiative evolves.
Anonymisation: stringent, but navigable
Private markets data products live or die on the balance between anonymisation and analytical utility. Sparenberg acknowledges the tension directly: the product promises deal-level and asset-level insights, but protecting GP confidentiality places hard limits on how granularly that data can be presented.
“We’re mindful of confidentiality and the data and metrics are designed so that individual constituents cannot be identified or reverse-engineered,” he says. Where sample sizes at the most granular level become too small, the platform allows clients to roll up one level, aggregate vintage or deal years, or navigate the data dynamically to find the right level of insight. “That’s an industry expectation now – that data is fluid and that you can navigate it dynamically to create the right level of insight.”
Positioning: complementary, not competitive
The private markets data landscape has consolidated significantly in recent years: Burgiss is now part of MSCI, Preqin sits within BlackRock, and PitchBook is owned by Morningstar. S&P Global is not positioning this product as a direct competitor to any of them.
“We think about this as being a new category of data,” Sparenberg says. “Getting this level of granularity, especially at the asset and deal level, is fairly unique.” He frames the product as complementary to existing industry offerings – including policy benchmarks – rather than a replacement. “We’re pursuing a data analytics product that is reflective of the total market and is not opinionated.”
That positioning is deliberate: Cambridge Associates’ own benchmark business is a major franchise, and the collaboration would not have worked if the new product encroached on it.
The platform trajectory
The press release mentions that data feed APIs and integrated software solutions will follow. S&P Global is building an AI-first interrogation layer on top of the dataset, due for release later this year.
“As we think about the evolution of this product, it’s not just about expanding the dataset and releasing new commercial data products,” Sparenberg says. “It’s also about building a layer on top that allows clients to dynamically interrogate the data for their own needs.”
The platform is being designed to meet clients at different levels of technical maturity. Some institutions have already built their own agentic workflows and need a reliable data feed or MCP server to consume; others want analytical tooling to query the dataset directly and generate tailored reports, charts and sector analyses. Sparenberg describes the forthcoming release as “software that helps clients interact with the data more deeply,” developed, he confirms, as an AI-first solution.
For the private markets data ecosystem, the implication is that the value in this initiative may ultimately sit less in the dataset itself and more in the taxonomy that structures it and the platform that delivers it. If the S&P Global–Cambridge Associates–Mercer classification framework gains adoption as a de facto standard, the commercial leverage extends well beyond a single data product.
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