Leila Sadiq
Private markets are at an infrastructure inflection point. Private investments have become a core component of institutional portfolios, often representing a third or more of total assets. As allocations have grown, so have expectations from investors and regulators for better data, reporting and analytics.
This is where the challenge begins. While there are significant data flowing about private investments, from general partner reports and net asset value statements to manager communications, data are not immediately accessible. Beyond that, publicly available data such as company disclosures, M&A filings, registry data and market intelligence are growing. However, this raw information takes significant manual effort to normalize and structure.
The most significant challenge in private markets is not just obtaining the right data; it is transforming disjointed, inconsistent information into a coherent, actionable view of exposure and risk.
Blind Spots
Private market data are rarely shared in consistent forms. They come in different formats, at different frequencies, with inconsistent naming conventions and uneven levels of detail. The same underlying risk exposure can also surface differently across funds, structures and manager reports. Before analysis can be relied on, teams must first reconcile, normalize and interpret the data. Too often, time is spent translating information instead of using it to drive decisions.
The result is that front-, middle- and back-office teams are often working from different representations of the same exposures. That fragmentation makes even basic risk questions difficult to answer with confidence: What is our true exposure to a single borrower? How concentrated are we across sectors when we look through multiple funds and managers? Where are risks building beneath the surface?
I’ve found that this data disconnect can create the following blind spots:
- Exposure Overlap and Concentration: The same borrower, sponsor or deal can appear under different names or structures across funds, making it difficult to identify true aggregate exposure.
- Counterparty Visibility: It can be difficult to determine what entities ultimately sit inside investment vehicles and how relationships connect across portfolios, limiting visibility into interconnected risk.
- Valuation Inconsistencies: Without shared reference points, identical positions may be priced differently across vehicles, making anomalies harder to identify and govern.
Addressing these blind spots requires a data foundation that can connect funds, entities and exposures in a consistent, interoperable way across private and public investments alike. In public markets, decades of standardization around identifiers, taxonomies and structured data delivery made it possible to aggregate exposures and build reliable investment workflows. Private markets never developed that same infrastructure. What was once an operational inconvenience has become a strategic liability.
Building a Connected Data Foundation
Building that foundation is now one of the central infrastructure challenges for private markets. In a recent survey of 38 senior operators conducted by the Private Markets Forum, 45% of respondents identified building a unified data model across systems as their single biggest operational challenge.
That priority reflects a broader shift in the market. As public and private markets continue to converge, particularly in credit, investors increasingly need to benchmark private credit terms against public credit spreads, compare fund and manager performance across standardized cohorts, and reconcile exposure, risk and valuation across public and private holdings.
Supporting that type of analysis requires more than aggregating data from different systems. It requires a connected data foundation that can link the same investment across vehicles, map relationships across entities, and preserve consistency as information moves from reporting to risk to valuation workflows. That type of connected data foundation depends on three core master records designed to work in concert:
- Fund Master: A consistent view of private funds across private equity, venture capital, private credit and real assets, including the relationships between sleeves, feeders, parallel vehicles and share classes. This creates a consistent way to aggregate exposure across related vehicles and fund structures.
- Entity Master: A persistent way to identify and connect private companies, borrowers, sponsors and related counterparties, even when names vary across documents or when corporate hierarchies evolve over time. This is essential for building a complete and durable view of counterparty risk.
- Investment and Exposure Master: A standardized representation of positions, whether equity stakes, loans or tranches, that links back to both the fund structure and the underlying entity. This is what makes it possible to measure overlap, compare valuations on a like-for-like basis and understand where exposure truly resides.
When these three layers are connected, consistency and data interoperability becomes possible. Investors can ask a fundamental question such as: “What is this exposure, and what is it ultimately tied to?” and get the same answer, regardless of which system, workflow or report they are using. That is the shift private markets now require: not more data, but a data foundation robust enough to turn fragmented inputs into a coherent view of ownership, exposure and risk.
How AI Magnifies the Cost of Data Fragmentation
Along with private investments representing a larger share of portfolios, firms need to modernize their technology stacks to deploy artificial-intelligence-driven analytics. In many ways, this makes the underlying data fragmentation challenge more consequential.
Firms with accurate, time-stamped, machine-readable data can begin applying AI to accelerate document extraction, identify exposure overlap, flag valuation anomalies and improve portfolio surveillance across funds and entities. Firms without that foundation will continue to spend disproportionate amounts of time reconciling inputs before any model can be trusted. A unified data model matters here not just because it improves operations, but because it creates the interoperability upon which AI depends across systems, workflows and tools.
This, in turn, raises the stakes for data quality. If connected data are missing, stale or inconsistent, firms do not simply get imperfect outputs—they risk scaling flawed interpretations across investment, risk, valuation and reporting workflows. In that sense, the competitive advantage in private markets will not come from having more AI. It will come from having a connected data foundation robust enough for AI to produce insights that are explainable, auditable and defensible.
Private Markets’ Infrastructure Moment
A familiar inflection point is here. Public markets did not become transparent overnight; shared identifiers were built because the industry recognized that interoperability was essential to scale.
Private markets are now at that same moment. As private and public exposures increasingly need to be understood together, the firms that lead will be those that can connect fund structures, entities and underlying positions into a single, usable view. That is what will make possible better risk judgments, stronger valuation governance, more credible reporting and more effective AI.
Leila Sadiq is global head of enterprise data content at Bloomberg.
This feature is to provide general information only, does not constitute legal or tax advice, and cannot be used or substituted for legal or tax advice. Any opinions of the author do not necessarily reflect the stance of ISS STOXX or its affiliates.
Tags: concentration of risk, Data Governance, financial markets disclosure, market data, Private Markets
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