By Jason Rich, head of Singapore and Southeast Asia, and head of sales, Asia Pacific at State Street.
Private markets are entering a new phase of maturity. Now valued at over $13trn globally, they continue to attract capital despite macroeconomic headwinds. Yet, as the asset class scales, so does operational complexity, especially around data.
There are three core data challenges facing private markets.
First, there is a lack of a standardised data model. Private markets have long prioritised growth, often at the expense of the operational infrastructure. As a result, process and technology have lagged, creating a fundamental challenge: the absence of a standardised data model or process.
The push for standardisation is coming not only from investors, but also from regulators, including the Australian Prudential Regulation Authority and the Monetary Authority of Singapore, who are asking for more timely and accurate valuation of data. But how do you get monthly validation data when it’s not readily available? This has driven asset managers to search for answers, questioning if leveraging actuary models for “as at today” valuations could work, or whether they need to utilise proxies from the public world.
However, a “one-size-fits-all” approach does not work for private markets. Each asset class comes with its own unique data characteristics — ranging from availability and format to how the data is shared, adding to the complexities. The lack of structure and consistency creates significant variability, not just in data models, but also in how investors access and interpret information — making scalability and standardisation extremely difficult.
The second challenge concerns data collection and transparency. The rise of retail investors in private markets has brought new expectations for data quality, transparency and granularity. Investors want the same level of detail they receive with public market investments, including exposures, tenures and returns — and supplied in a timely manner.
Delivering this level of insight requires access to underlying investment data, which is often fragmented and manually sourced. This process often triggers data exceptions that demand expert review. Compounding the issue is the limited public disclosure of private markets data, which increases the need for aggregated data and services.
Finally, there are talent gaps in private markets data expertise. With so many nuances across the various private asset classes, it can be difficult to identify which data points truly matter and determine the required data model. The amount of manual data consumption adds extra complexity. If you are not an expert in that asset class, it can be overwhelming to sift through the data and understand what you need. Without a standard data model in place, each asset class cannot be managed in the same way.
In response, firms have shifted hiring strategies over the last five years, increasingly seeking subject matter experts with specialised knowledge of private markets. To maximise the value of these high-cost specialists, firms must reduce time spent on exception analysis and enable greater focus on capital deployment and strategic decision making.
Building a path to data accuracy and transparency
The consensus is clear: There is no silver bullet for solving private markets data challenges. Nevertheless, while a universal solution remains elusive, firms can still take practical steps forward.
One foundational move is defining the right operating model — a task complicated by the lack of data standards and the nuanced nature of private asset classes. This complexity is further compounded by a talent gap. Internal experts often possess deep asset-class-specific knowledge, but they may lack the cross-asset perspective needed to design scalable, multi-asset operating models. Such limitations can hinder their ability to identify the right data points and build systems that support transparency across diverse portfolios.
Addressing these challenges requires both domain expertise and an holistic view of operational design. Firms benefit from engaging with partners who can bring this cross-asset class experience and a structured approach to data architecture. These collaborations can help define scalable models enabling internal teams to focus on strategic priorities.
By aligning internal expertise with external insight, firms can accelerate their journey toward data accuracy and transparency, unlocking greater value from their private markets investments.
