Private markets are no longer a niche allocation. With global private assets under management sitting at approximately $14 trillion, up 27% since 2020 and forecast to reach $23.9 trillion by 2030, institutional allocators have made their conviction clear. The question is no longer whether to invest in private markets. It is whether the operational and data infrastructure underpinning those investments can deliver trusted, decision-grade intelligence at scale.
For most firms, that question does not have a comfortable answer.
The Poll That Should Bother You
At A-Team Group’s Data Management Summit in New York this past fall, industry practitioners were asked a simple question: how would you describe the state of private markets data transparency?
71% said poor. 29% said non-existent.
Not one respondent offered a positive view. When asked why, 80% cited the same trio of causes: lack of data standards, fragmented sources, and poor data quality.
This is not a fringe view from smaller operators. These are the people running data and investment operations at institutions with significant private markets exposure. The near-unanimous verdict points to something structural, not a collection of fixable edge cases.
The challenge compounds across every layer of the problem. Fund-of-funds structures, co-investments, and feeder vehicles make exposure difficult to trace. Reports arrive in different formats on different timelines. Manager-to-manager variation in fee treatment and valuation methodology makes cross-portfolio comparison laborious. And underneath all of it, private asset valuations frequently rely on Level 2 and Level 3 inputs, meaning judgment-based estimates with no observable market price to anchor them. The FCA and other regulators expect full auditable lineage for those inputs. Most firms cannot provide it cleanly – largely because the data needed to support that transparency remains fragmented across systems, documents, and silos.
The Cost Is Not Theoretical
Investment professionals are estimated to spend 60 to 80% of their time preparing data rather than using it. In a business built on analytical judgment, that proportion represents an enormous misallocation of the firm’s most valuable resource.
The commercial pressure is moving in the same direction. According to research in our recently commissioned white paper, 68% of LPs now rank operational clarity as more important than historical returns when evaluating managers. Boards and regulators are asking harder questions about how private assets are valued, what sits underneath them, and whether the numbers can be defended. Firms that cannot answer with confidence are not just operationally exposed. They are competitively exposed.
The firms winning in this dimension are not necessarily the ones with the largest teams or the most complex technology. They are the ones that made a deliberate decision to treat data and intelligence infrastructure as a strategic investment rather than a back-office cost.
What Needs to Change
The path forward requires more than centralizing data into a single repository. Centralization without standardization tends to move the reconciliation problem rather than solve it.
What is needed is a governed, connected data and intelligence model that connects private and public exposures, enforces consistent definitions, and maintains full lineage from source document to published figure. That is where platforms like Rimes’ investment intelligence platform, Matrix, play a critical role: providing a unified, decision‑grade view across complex, multi‑asset portfolios while preserving auditability, transparency, and control, particularly where private markets data is fragmented and judgment‑based.
That is a more demanding standard than most firms currently meet. The practical steps for getting there, covering valuation governance, ESG data requirements, ILPA standardization, and the role of AI in processing unstructured fund documents, are what our white paper addresses in detail: The Transparency Imperative – Forging Competitive Advantage in Private Markets Through Next Generation Data Management.
