Investor expectations in private markets are shifting from aggregate, fund-level reporting toward granular, exposure-level insight. Limited partners increasingly seek data on underlying positions, sector and asset-type exposures, and evolving risk profiles, rather than relying solely on net returns and high-level statistics. This reflects a desire to understand how external shocks - such as trade policy changes, geopolitical events or market dislocations - might transmit through specific holdings and affect portfolio volatility and performance.
This approach supports more forward-looking risk management, where detailed exposure data enables investors to anticipate stress points and adjust allocations proactively. Not all investors prioritise this level of detail, but for those that do, exposure transparency has become a key differentiator in manager selection and ongoing oversight.
Access to detailed portfolio data is necessary but insufficient for robust due diligence and ongoing monitoring in private markets. Data across managers and platforms is often non-standardised, requiring significant effort to normalise and interpret. Beyond the numbers, understanding the narrative behind performance and risk metrics - through direct conversations with general partners and underlying management teams - remains central to forming a complete view of asset quality and trajectory.
In secondary transactions, this dynamic is particularly visible: buyers can observe historical portfolio construction and company-level performance, yet still rely on manager insight to explain deviations, operational challenges and strategic responses. The combination of standardised exposure data and informed human judgement supports more disciplined underwriting, clearer identification of blind spots, and more effective engagement with managers throughout the investment lifecycle.
General partners are increasingly expected to operate with “open kimono” transparency toward limited partners, supported by advances in data infrastructure and artificial intelligence. Investment managers are integrating multiple data and AI providers to harmonise information across platforms, accelerate transaction analysis and shorten reporting cycles. These capabilities aim to deliver more timely, consistent and customisable insights to investors while maintaining appropriate controls over confidential information.
At the same time, the use of AI in due diligence and portfolio monitoring raises confidentiality and NDA considerations. Managers are adopting approaches that anonymise outputs and restrict data flows to ensure sensitive company-level information is not inadvertently disclosed. Within these guardrails, technology is positioned as an enabler of deeper, faster and more personalised reporting, rather than a substitute for disciplined underwriting and risk management.
Non-standardised data formats
Portfolio and risk data are presented differently across managers and platforms, requiring significant normalisation work before they can be compared or aggregated meaningfully.
Over-reliance on spreadsheets without context
Financial KPIs and static data extracts provide only a partial view; without narrative explanation from managers and management teams, it is difficult to assess why performance is deviating from expectations or how risks are evolving.
Behavioural and process signals during diligence
Responsiveness, openness and the willingness to address difficult questions during due diligence serve as early indicators of future governance and transparency, yet these qualitative signals are not always systematically captured or weighted alongside quantitative metrics.
Improved data availability and faster reporting are expected to enhance decision quality and risk management in private markets, but are unlikely to eliminate the illiquidity premium relative to public markets. More granular and timely information supports stronger underwriting, earlier identification of stress, and more disciplined portfolio construction, which may reduce losses and volatility over time.
At the same time, structural reasons for a liquidity discount persist, including the inability to trade positions intraday and the inherent complexity of private assets. Better data is therefore viewed primarily as a tool to justify management fees and demonstrate value added through superior analysis and judgement, rather than as a mechanism to fully close the gap between private and public market valuations.
“Transparency for us is absolutely key… if we can look in the weeds to see the data and the underlying positions and the underlying trends, then that means that we have the ability to play offence in our portfolio instead of just playing defence.” Caroline Lovelace, PRESERVER PARTNERS
“Secondaries… is a great way of complementing a private market strategy because you’re effectively mitigating heavily the blindfold risk… you actually see how they have constructed their portfolio.” Joaquin Ardit, ALLIANZ GLOBAL INVESTORS
“To our private investors it’s open kimono… we want to be ahead of the curve all the time… It’s quite embarrassing when one of our LPs goes before us and asks, ‘Have you done an analysis on… the amount of software you have in your portfolio?’ You have to be there faster than them asking the question.” Rafael Calvo, CLEARLAKE CREDIT