Private Markets Data Expansion: What LPs and GPs Need to Know

The Unprecedented Growth of Private Markets

Over the past decade, private markets have undergone a structural transformation, moving from a niche allocation to the cornerstone of institutional portfolios. However, the data infrastructure required to manage that capital has struggled to keep pace.  

From Alternative to Essential: The $20 Trillion Market

The scale of private markets today reflects a fundamental shift in how institutional capital is deployed. 

J.P. Morgan Asset Management's most recent Global Alternatives Outlook projects that private markets have exceeded $20 trillion in assets. This expansion isn’t just a function of rising asset prices but reflects a deliberate and durable reallocation of institutional capital. McKinsey's 2026 Global Private Markets Report notes that private equity is now a mainstream industry, with fundraising and deployment reaching new heights in 2025 even as market conditions grew more complex. 

This growth has a direct operational consequence for investment committees and chief data officers. More capital means more funds, more managers, more reporting obligations, and a greater volume of data to aggregate, reconcile, and act on. 

The Convergence of Public and Private Markets

However, this is about more than just scale, and the blurring of boundaries between public and private markets is reshaping how institutional portfolios are built and monitored. This convergence is partly driven by the rise of semi-liquid fund structures. 

Evergreen funds, which continuously accept capital and have periodic liquidity, held nearly $700 billion in assets under management as of late 2024 and are expected to account for 20% of total private markets AUM within the next decade. On the secondary market side, deal volume reached a record $226 billion in 2025, reflecting deepening liquidity and pricing transparency across private asset classes.

For institutional allocators with portfolios spanning both public equities and private fund commitments, it’s essential to have unified data visibility across asset classes. LP-level portfolio reporting, risk aggregation, and performance benchmarking all require the ability to consolidate disparate data formats and timelines. 

While the growth of private markets has created a historic opportunity, it has also exposed that the data infrastructure supporting these markets was built for a different era of smaller allocations, fewer managers, and lower expectations for transparency.

The Historical Data Problem in Private Markets

In the wake of private markets growth, institutional allocators are trying to address the data management challenge. Every time they add a new fund commitment to their portfolio, the operational burden of manual workflows, fragmented systems, and a lack of reporting standards becomes a bigger issue.

The Cost of Opacity and Manual Extraction

Private markets reporting remains manual at many institutions. Analysts will typically review quarterly fund reports delivered as PDFs, then will extract key figures by hand and consolidate them into spreadsheets. It’s a process they have to do for dozens of fund companies, four times per year, and a bottleneck that makes it hard to keep up with reporting cycles or scale exposure monitoring. 

Because reports are typically spread across emails and portals, no two fund managers report in the same way. As a result, inconsistencies and human error can degrade trust in the data before it even reaches an investment committee.

Additionally, when data is late, inconsistent, and requires hours of reconciliation before it can be used, the quality of portfolio-level decisions may suffer. Risk exposures may go unmonitored between reporting cycles, and capital pacing decisions are made on old figures. Liquidity planning is also constrained between the time the events occur at the fund level and the time the information reaches the allocator’s systems.

Siloed Intelligence and the Benchmarking Challenge

These structural issues make it nearly impossible to have meaningful performance benchmarking across managers and strategies. 

In public markets, standardized data feeds and independent verification make peer comparison easy. However, private markets have no readily accessible comparable data, and are widely considered the worst-performing asset class in terms of data and transparency. As each GP reports on its own timeline, format, and definitions, the data arriving at an LP's office is not comparable in any meaningful way. 

The Institutional Limited Partners Association has worked to close this gap by creating a reporting template to promote more uniform reporting practices related to fees, expenses, and carried interest. While some of the market has adopted the template so far, there is growing pressure for others to follow.

Until there is universal adoption, allocators still have to reconcile data sets. This has notable implications for risk management, as a portfolio’s true concept risk, exposure, and sector weighting cannot be assessed accurately when the underlying fund-level data are so inconsistent. It’s not just an inconvenience; it may end up being a governance risk. 

Key Drivers of Data Strategy Modernization

While these data infrastructure challenges have persisted for years, there’s growing pressure to resolve them. An expanding regulatory disclosure environment and the structural shift towards broader investor access now make modernization a board-level priority.

Regulatory Pressures and the Demand for Transparency

As U.S. regulators raise the reporting obligations for private fund managers, the data infrastructure demands are being felt across the industry. Form PF is the confidential reporting form filed by SEC-registered investment advisors to private funds. It requires all private equity fund advisors to file quarterly event reports on triggering events, such as GP removal, fund terminations, and adviser-led secondary transactions. Private equity fund advisors with at least $2 billion in private equity AUM are also subject to additional annual reporting rules about strategies, borrowings, and clawback activity.

Quarterly event reporting within 60-day windows, annual strategy disclosures, and granular fund-level data may become increasingly operationally challenging to be produced manually from spreadsheets and PDFs. For private equity fund advisors operating across multiple fund vehicles, the cost and compliance risks scale with portfolio size. 

The “Retailization” of Private Markets

Private markets are also expanding beyond traditional institutional allocators to high-net-worth individuals and, in some cases, retail investors. Retail capital flowing into alternative structures in the United States reached $204 billion in 2025, more than double the $92 billion level in 2023. 

Regulatory reforms are also increasing access internationally. For example, the UK announced pension fund programs that set targets for private asset allocations by 2030, while the European Long-Term Investment Fund framework has materially lowered barriers to retail participation across the continent. 

However, serving retail and high-net-worth channels requires navigating periodic liquidity, conducting more frequent valuations, and operating at a larger scale with institutional-grade compliance, data, and operating infrastructure. Each of these demands places greater strain on data systems designed for a smaller, more homogeneous institutional LP base. This has a compounding effect of more investors, more fund strategies, more jurisdictions, and more reporting obligations. 

How Technology Is Transforming Private Market Data

While private market allocators face operational and transparency challenges, there are solutions. A data infrastructure built on cloud architecture, API-driven integration, and applied machine learning can help allocators move from reactive, manual processing to continuous, portfolio-wide intelligence.  

Cloud Platforms and Unified Data Ecosystems

Where private market data once lived in a fragmented collection of portals, PDFs, and spreadsheets, cloud-native platforms are now enabling institutional allocators to consolidate it all into a single, cohesive environment. 

This offers many practical implications. As cloud-native platforms can support data from multiple administrators and vendors, it supports greater portfolio transparency. API-driven data is central to this model and can reduce reliance on periodic, file-based transfers with continuous, standardized data feeds that automatically update as source data changes. This moves reporting from a static environment to a dynamic data infrastructure. Instead of sampling a portfolio view once a quarter from disparate sources, investment teams may be able to query a centralized data environment in real time at any point in the reporting cycle.

For investment operations teams, portfolio intelligence that once took days of manual reconciliation may now be available on demand. 

The Role of AI and Machine Learning in Due Diligence and Portfolio Monitoring

Applied AI offers new capabilities in data extraction and analysis in unstructured documents. It can help firms analyze lengthy documents and financial information in minutes, even from funds with unique reporting formats and methodologies. 

Machine learning models trained on private market document types can identify and extract fields across formats that vary by manager, jurisdiction, and asset class. This is not used as a replacement for human analysis and judgment, but rather as a mechanism to ensure that analyst time is spent on interpretation rather than data extraction.

AI is also being applied to portfolio monitoring. It can cross-reference data across multiple accounts and identify inconsistencies or risk indicators that manual reviews may miss. Predictive analytics tools may assist in flagging early warning signals in portfolio company performance. 

While the technology is growing rapidly, it cannot implement itself. The firms that extract the most value from cloud infrastructure and apply AI have a common foundation. They have a deliberate approach to data architecture, e-governance, and integration. 

A Step-by-Step Framework for Data Integration

Many investment committees and chief data officers are struggling with where to begin. The key is to assess where your organization stands and implement the right architecture.

Assessing Current Data Maturity

Private market data operations at institutional allocators typically fall into one of three stages.

The first stage is manual, where fund-level data from GP portals and quarterly reports is extracted by hand and consolidated into spreadsheets. Reporting is slow, error-prone, and dependent on key personnel. Benchmarking across fund managers is effectively impossible, and manual data processing creates a growing operational burden. 

The second stage is automation, where data is ingested from multiple sources and systematized through structured pipelines. Reporting timelines compress, error rates fall, and investment teams gain access to current data. 

The third stage is AI-driven and moves beyond automation with continuous intelligence. 

Metadata catalogs, lineage tracking, and real-time health dashboards offer full visibility into every ingestion, transformation, and delivery step. Meanwhile, access controls and audit logs ensure data is trustworthy, compliant, and easy to govern.

Investment committees and CDOs must ask themselves how many manual steps are required to produce a portfolio-level performance report. How quickly can the team respond to an investment committee request? Can the organization benchmark fund commitments against peers without a multi-week data gathering exercise?

Implementing Scalable Data Architecture

Moving up the maturity curve isn’t just about adding new tools, it’s about the architecture. 

The first thing to think about is taxonomy and establishing consistent naming, asset class classification, and a metrics framework. Without a shared taxonomy, automated pipelines will just replace inconsistencies, only faster.

The next decision is about an integration standard. API-driven integration is the typical approach for choice in organizations seeking a real-time portfolio of intelligence. Managed services are also helping private-market firms overcome resource constraints by outsourcing data integration.

The final decision is about build vs. buy. While proprietary data infrastructure offers maximum customization, it has significant ongoing maintenance costs and technology risk and is typically used by the largest institutional asset allocators. Third-party platforms built specifically for private markets data management offer fast implementation, standardized data models, and vendor-supported compliance workflow.

Ensuring Governance and Security

Data infrastructure that cannot be audited also cannot be trusted and therefore carries a governance risk. When data arrives in a consistent format, platforms can build optimized, automated pipelines that ensure validation, automate pipelines, and lower the total cost of ownership. 

Governance frameworks built on the standardized foundation address data lineage, role-based permissions, and audit trails. For institutional allocators subject to AIFMD II, ILPA reporting obligations or fiduciary standards, defensible data can help meet compliance requirements. Adopting advanced data capabilities also requires unified, high-quality data and human oversight to ensure accuracy, mitigate bias, and meet regulatory standards.

Turning Data into a Competitive Advantage

Data modernization in private markets isn’t just about technology; it’s about strategy. Over the next decade, institutional investment performance may be shaped by the ability to view portfolios clearly, report on them accurately, and respond to changing conditions before they appear in quarterly reports.

As private markets continue to evolve in scale and complexity, many institutions are reassessing the scalability of highly manual processes. Regulatory requirements, broader investor access, and rising reporting expectations are increasing the need for more consistent, comparable, and auditable data. Meanwhile, the benchmarking gap continues to make it harder for investment committees to evaluate performance, risk, and allocation decisions with confidence.

The technology to help close that gap now exists. Cloud-native platforms, API-driven integration, and applied machine learning have moved from experimental to operational. For allocators looking to bring structure, consistency, and analytical rigor to their private markets data infrastructure, Nasdaq eVestment™ offers a practical starting point through a centralized ecosystem of private- and public-market intelligence, including GP profiles, performance analytics, methodologies, and fundraising calendar data.

Private Market Data FAQs 

What is private market data?

Private market data includes the financial, operational, and performance information in investments in asset classes that are not publicly traded. Unlike public market data, which is standardized, continuously updated, and governed by exchange and regulatory requirements, private market data arrives through many sources and in many formats. The fragmented information requires significant effort to normalize and make useful for portfolio-level decision-making.

Why is data transparency becoming critical in private equity?

Data transparency is becoming critical because transparency obligations, investor expectations, and the scale of institutional allocations have all increased. New regulatory frameworks are taking effect and establishing minimum standards for fee, expense, and performance disclosure. Meanwhile, investment committees require consistent, comparable data to assess fund-level performance, monitor risk, and meet their fiduciary reporting obligations. 

How can AI improve private market investing?

AI improves private market investing by automating specific, high-volume data tasks that previously required significant manual effort from investment operations and analyst teams. Machine learning models can extract structured data from unstructured fund documents (such as quarterly reports, limited partnership agreements, and capital account statements) more efficiently than traditional manual processes in certain circumstances, reducing the lag between data availability and decision-making. AI-driven monitoring tools can also cross-reference portfolio data across multiple reporting sources to surface inconsistencies and flag early warning indicators against sector benchmarks before they appear in formal quarterly reporting.  

© 2026 Nasdaq, Inc. The Nasdaq logo and the Nasdaq ‘ribbon’ logo are the registered and unregistered trademarks, or service marks, of Nasdaq, Inc. in the U.S. and other countries. All rights reserved. This communication and the content found by following any link herein are being provided to you by Nasdaq, Inc. and/or certain of its subsidiaries (collectively, “Nasdaq”), for informational purposes only. Nasdaq makes no representation or warranty with respect to this communication or such content and expressly disclaims any implied warranty under law. At the time of publication, the information herein was believed to be accurate, however, such information is subject to change without notice. Nothing herein shall constitute a recommendation, solicitation, invitation, inducement, promotion, or offer for the purchase or sale of any investment product, nor shall this material be construed in any way as investment, legal, or tax advice, or as a recommendation, reference, or endorsement by Nasdaq.