Unlocking the Private Markets Opportunity with Data Enablement

What is happening in Private Markets

As 2024 concludes, private capital markets have rebounded strongly, marking a year of recovery and strategic innovation following the challenges of 2023. Stabilising macroeconomic conditions, moderating inflation, and moderating interest rates have fuelled renewed M&A activity. Dry powder remains at historic highs of $3.9 trillion globally, yet Limited Partners (LPs) are increasingly pressing General Partners (GPs) to accelerate capital deployment and deliver returns from legacy investments. This momentum has pushed global private capital assets under management (AUM) past $12 trillion, underscoring sustained investor interest in asset classes such as private equity, venture capital, real estate, and infrastructure to create alpha.

Source: https://pitchbook.com/newsletter/toward-20-trillion-in-private-capital-aum

The democratisation of private markets is transforming the investment landscape. Regulatory changes and digital platforms are broadening access, enabling high-net-worth individuals, family offices, and retail investors to engage in opportunities once reserved for institutions. Recent examples of private market dynamism include BlackRock’s acquisition of Global Infrastructure Partners, creating one of the largest infrastructure investment platforms globally.

Value creation is becoming a top priority for GPs with a focus on operating levers (good old working capital, digital, op model, costs and M&A) than historic approaches of financial engineering. With limited IPOs and traditional deal structure, the growth in secondaries markets is projected to be robust over the next three years. GPs need to play an ever more direct role in portfolio management and measurement to deliver the required returns.

Gen AI adoption in private markets is a real opportunity to create efficiency and deliver insights through the investment lifecycle. AI can enable firms to harness deeper insights and predictive modelling to identify opportunities, improve due diligence and risk assessment and formulate value creation strategies. ML algorithms can help automate valuations, enhance ESG compliance and provide enhanced portfolio oversight. Unlike the incremental evolution seen in Open Banking or FinTech, AI is promising a transformative impact.

The focus on market intelligence datasets, data platforms and AI solutions which enable LPs and GPs to harness the growth in private market asset classes, distribute to a much broader investor base including retail and leverage data and AI at scale has reached a critical mass.

BlackRock (NYSE: BLK) and Partners Group (SIX: PGHN) have teamed up to launch a multi-private markets models solution set to transform how retail investors access alternative investments. The solution will provide access to private equity, private credit and real assets in a single portfolio – currently not available to the U.S. wealth market – managed by BlackRock and Partners Group. Sep 12 2024

BlackRock has agreed to acquire Preqin, a UK-based independent provider of private markets data for £2.55bn ($3.2bn) in cash, combining Preqin’s data and research tools with Aladdin’s workflow functions into a single platform.

Abu Dhabi sovereign investor Mubadala Investment Company will participate in a $25 billion private credit, direct lending programme announced by Citigroup and alternative asset manager Apollo. Sep 27 2024

J.P. Morgan launched its Private Markets Data Solutions offering for institutional investors, available through Fusion by J.P. Morgan. This is a data management solution for private assets that enables investors, both General Partners (GP) and Limited Partners (LP), to analyse and gain transparency into their complete portfolio across public and private holdings and eliminate the manual processes of managing this operational workflow at scale.

Despite all this growth and promise there are significant impediments to Private Markets truly achieving the scale and opportunity which it promises.

What is the barrier to scale in Private Markets?

Lack of trusted centralised datasets and industry standard approaches

Unlike public market data, which is generally structured and standardised, private market data is incomplete, deemed proprietary, and inconsistently applied across participants in the value chain. In most instances, the absence of centralised data management frameworks means transaction granularity is often lacking, making it challenging to accurately analyse deal terms, valuations, and performance.

Data collection is fragmented, with limited transparency on capital flows, pricing dynamics, or asset-level specifics. Each institution produces information on their own basis, time periods and criteria. This is further compounded by diverse reporting standards, varying compliance requirements, and the manual processes prevalent in private market transactions.

Complexity in asset classes

Complexity in asset classes arises from the need to model diverse assets consistently across both public and private markets. Each asset class often has unique characteristics, valuation methods, and performance metrics, complicating standardised modelling. Furthermore, the integration of public and private data is essential to provide a holistic view of portfolios but presents significant challenges due to differences in data quality, reporting standards, and granularity.

Legacy investment management process & discipline

Complexity in asset classes arises from the need to model diverse assets consistently across both public and private markets. The end-to-end lifecycle from fund-raising, capital deployment, portfolio monitoring, portfolio administration and value creation ranges from ad hoc to sophisticated at many firms. This is partly due to the lack of trusted data and process challenges above but equally due to the investment discipline and focus across GPs and LPs.

Many GPs still think it is acceptable to provide historic low quality information, standard NAV statements, IMA summaries which don’t allow any sort of detailed attribution, forecasting, reporting and risk transparency that investors want and deserve; the detailed costs, exposures, mandates, fees, ESG tracking, transactions and activities of the underlying funds, portfolios, and transactions.

If GPs provide higher quality data, this will benefit both GPs and LPs – LPs in being able to be proactive to monitor investments, manage risk, take decisions on the basis of target returns and adjust allocations, and GPs to get closer to the value creation agenda and realise the investment opportunities.

The day-to-day consequences and risks to the system

These barriers have direct impacts:

⚫ Reducing the GPs ability to report performance to LPs, eroding trust and investor confidence

⚫ Lack of data integrity and a robust performance management process leads to inaccurate performance reporting, delays and errors in portfolio analysis, resulting in sub-optimal investment or financing decisions

⚫ Investment operations being labour-intensive and focused mostly on data extraction, and transformation with manual approaches create a challenge to produce frequent and detailed regulatory reporting

⚫ Operating model complexity in participants with fragmented and inefficient workflows and delivery structures, which are exacerbated by heightened M&A activity. The unit cost of servicing every additional $1Bn AUM and new integration required is not sustainable

Addressing these challenges requires an ecosystem-wide shift towards more cohesive data practices, leveraging technology to standardise inputs and improve accessibility whilst balancing the need to protect proprietary information and competitive advantage. Let us see how.

Leading Point’s Perspective: How can GPs and LPs take clear steps to build the foundational data layer and processes

We believe firms need to invest in creating capabilities and practices in five main areas.

Step	Objective	Actions
1. Data standardisation and mastering	Achieve consistency, accuracy, and reliability of data across supply chain participants.	– Implement a mechanism to collect and aggregate data from managers, funds, administrators, and portfolio companies – a universal identifier and robust data dictionary is a pre-requisite – Create capability to extract data from diverse formats, de-duplicate, validate across sources, and standardise into a unified structure.
2. Business roles and rules for collaboration	Streamline workflows and enhance collaboration among supply chain participants.	– Define roles across your supply chain – Define business rules and agreements you’d like to enforce including permissioned access and level of detail sharing – Automate processes for data handling, validation, and reporting – Establish and monitor performance benchmarks
3. Technical stack	Technology to support growth, manage data complexity, and ensure high performance.	– APIs for seamless data integration and accessibility – Store standardised data in scalable, high-performance databases – Maintain a full audit trail for data provenance and source traceability – Compute framework for valuation and exposures based on underlying transactions, funds, portfolios and prices incorporating various contracts, agreements and terms based on the counterparties
4. Integrated platforms and ecosystem collaboration	Enable seamless interaction among participants through a shared, integrated infrastructure.	– Develop a data ecosystem for mutual benefit among participants – Integrate with service providers and third parties
5. Analysis, selection, and management of investments	Utilise high-quality data to inform investment decisions and optimise portfolio performance.	– Optimise portfolios using advanced analytics to identify opportunities and manage risks – Integrate decision-support tools and models based on deep-data to forecast drawdowns, returns and attribution

Firms need to actively consider their op model and be willing to entrust service providers who offer one or more of these five capabilities rather than attempt to build in-house. A robust data-enablement framework to manage and orchestrate the key inputs, outputs and oversight the firm needs is a pre-requisite before any outsourced service agreements. (See Leading Point’s Data Enablement Framework below).

In the section after, we highlight examples (non-exhaustive) ranging from innovative Fintech platforms to global scale infrastructure providers.

The Leading Point Data Enablement Framework

We create the foundation for an enterprise to harness its data assets and make them integral to its business ops. Data becomes readily accessible, well-managed, and is used to drive decision-making and innovation.

Example Solutions in the Private Markets Space

Clearwater Analytics

Clearwater Analytics provides compelling evidence for LPs and GPs to adopt robust data foundations and solutions through its cloud-based platform for investment accounting, reporting, and analytics. The solution consolidates disparate financial data into a single source of truth, enabling real-time visibility across asset classes and geographies.

Key benefits include significant productivity gains, with 91% of data auto-reconciled using AI and machine learning, leading to reduced month- and quarter-end closing times. The platform’s daily updated data and multi-currency reporting capabilities drive performance improvements and support expansion into new markets. Cost reductions are achieved through lower IT expenses and elimination of on-premises hardware.

JP Morgan Fusion Service

Fusion by J.P. Morgan offers a solution for institutional investors seeking a comprehensive view of their total portfolio across both public and private markets. This innovative platform addresses the longstanding challenge of fragmented and non-standardised private market data, which has historically limited investors’ ability to analyse across asset classes effectively.

By leveraging advanced AI/ML technology and proprietary algorithms, Fusion seamlessly integrates and normalises data from diverse sources, including J.P. Morgan Securities Services, multiple portfolio administrators, and leading data providers. This integration spans a wide range of asset classes, from public securities to private equity, venture capital, real estate, and infrastructure.

Allvue Systems

Allvue Systems provides a comprehensive software solution tailored for alternative investment managers in private equity, venture capital, and private debt. Their integrated platform streamlines operations across front, middle, and back-office functions, encompassing portfolio management, compliance, data management, fund accounting, and financial reporting.

For LPs, Allvue centralises fund and portfolio information, significantly reducing manual processes and enhancing data management efficiency. The solution automates data collection and reporting, allowing LPs to self-serve their data needs through customisable reports. It also features user-defined dashboards and interactive reports that facilitate quick insights while supporting ESG tracking and reporting at both the portfolio company and fund levels. This capability enables the creation and collection of unlimited metrics for informed investment decisions.

Additionally, Allvue enhances investor relations with an Investor Portal that provides secure access to shared documents, fund data, and portfolio company information, streamlining communications between GPs and LPs.

Byhiras

Byhiras is a technology company dedicated to improving transparency and accountability in investment management. Its platform enables organisations, such as pension funds and asset managers, to aggregate and validate granular data about their investment activities. By providing detailed insights into costs and outcomes, Byhiras helps institutional investors make informed decisions, report accurately, and demonstrate value for money.

The platform benefits all stakeholders in the investment ecosystem. Investors gain clarity on how their funds are managed, consultants access data to evaluate value for money, and managers showcase their performance while maintaining confidentiality. Byhiras’ proprietary technology supports unlimited data types, while its tools ensure users retain full control over what data is shared and with whom.

Conclusion: Building a Data-Driven Future in Private Markets

As private markets navigate an era of unprecedented growth and complexity, the need for robust data transformation has never been greater. Addressing challenges such as fragmented data systems, non-standardised reporting, and evolving investor demands requires a strategic shift toward digitalisation and collaboration. Innovations in AI, cloud-based platforms, and integrated ecosystems are reshaping the industry, empowering General Partners and Limited Partners to make informed, data-driven decisions.

To thrive in this dynamic environment, market participants must embrace foundational changes—prioritising data standardisation, optimising operating models, and leveraging scalable technology solutions. Collaboration across the value chain will be critical in driving efficiency, transparency, and long-term value creation.

The future of private markets lies in their ability to adapt and harness the power of innovation. By addressing existing inefficiencies and adopting forward-looking strategies, the industry can secure its position as a cornerstone of global investment and sustainable growth.

Actionable Steps for Private Market Participants

⚫ Prioritise data enablement to unlock value across the investment lifecycle

⚫ Collaborate on standardisation efforts to reduce fragmentation

Join Us to Lead the Data Revolution!

Join Leading Point’s Private Markets data event on 28 January 2025 at Rise London in Shoreditch to explore transformative solutions with industry experts. Discover how a data-first approach improves transparency, decision-making, and risk management across the investment lifecycle.