Data optimisation is critical in enhancing private market operations. As many asset managers move to diversify and grow in private markets, firms will be grappling with an increased load of non-standardised and unstructured data to manage and store, heightened complexities in monitoring performance, and the challenges of seeking a holistic view of investments.
Private markets have steadily gained traction in investment portfolios over recent years, stabilising despite performance issues. McKinsey’s recent review shows that private market fundraising reached its global peak in all sectors in 2023.
And in 2024, there has been a notable rise in retail investor demand, despite the risks and regulatory concerns highlighted by the IMF. Lowering high minimum investment thresholds has further spurred activity, especially among individual investors. Recent partnerships between clearing houses and fintech providers have created a digital route for private market investors, reflecting the market’s intent to widen access and deliver cutting-edge solutions, including a nominee solution whereby managers can aggregate investors and support disaggregated reporting.
Efficiently managing private asset data is a significant challenge for asset managers. It requires substantial human resources to deliver a comprehensive front-to-back view of private asset portfolio data, encompassing cashflow schedules, timely valuation data, and risk analytics.
Integrating public and private market holding data within a fund is particularly complex due to the use of different vendors, providers, systems, taxonomies, and definitions across these two domains.
Optimising data processes is crucial for operational efficiency. The continued growth of private markets underscores the needs for firms to get the basics right, which includes seeking comprehensive data sets and ensuring that data is well-defined, transparent, and understood. Even if the data quality is suboptimal, consumers can gain confidence in the trustworthiness of the information provided.
Those managing private and alternative assets face considerable challenges due to limited electronic infrastructure and messaging capabilities. Valuation data in private markets is often not standardised, arriving in various Excel files and unstructured PDFs. Many staff at the coal face in asset management firms may feel left in the Dark Ages as they are forced to deal with converting paper copies of vital datasets into the portfolio reporting system infrastructure.
One of the main causes of this is due to the need to consume General Partner statements that provide look through data of the constituent assets as well as the performance of underlying holdings. This is important for understanding the underlying exposure and performance of the funds held, typically for fund of funds-type investments but also for direct holdings.
Tackling the pervasiveness of non-standardised and unstructured data across the investment lifecycle of a private market asset from deal creation through to disposal is crucial to an asset manager’s success. Investing in technologies to automate extraction and standardisation of data from diverse formats, as well as deploying advanced analytics tools, will enhance both decision-making and operational efficiencies.
For all managers, moving into new investment types requires handling new and often highly complex strategies that demand robust data support. Sourcing datasets for private market companies involves capturing diverse and often ‘hidden’ debt and credit market data from numerous sources, far exceeding the requirements of public markets. Implementing unified data platforms that integrate and normalise data from various sources can enable consistent and reliable performance monitoring and performance.
Secondary investments can introduce additional challenges, as there are multiple versions of returns and performance to consider depending on what context you are looking at with the data.
For example, when a fund is purchased from a GP, tracking performance and returns becomes complicated. The manager needs to monitor returns from the point of purchase, while the GP’s reporting may reflect data from the fund’s inception, leading to discrepancies that need to be reconciled.
This issue also arises with different vintages of funds, where the lack of comparable data for privately held assets poses challenges for data quality. Transparency in data usage and processes is crucial to address these issues.
Another example that multi-asset managers may find familiar is the complexity with infrastructure data. Managing direct and indirect hard assets that are privately owned introduces additional layers of complexity in operational, data and technology models, all of which require some form of standardisation.
For instance, valuing diverse infrastructure assets—ranging from solar panel farms to rolling stock and public schools—is challenging because each subclass has distinct characteristics with no common valuation framework.
Traditional asset managers, who are accustomed to investing in public markets, must adapt to these complexities when dealing with private, non-publicly listed infrastructure investments.
This adaption does not imply a one-size-fits-all approach. Instead, it is crucial to ensure that each private market asset classes’ technology and data requirements are met to achieve the business strategy.
The key is to design and implement a unified strategy across all asset classes, enabling data integration and allowing the extraction of value from all the company’s intellectual property across its investments.
Unstructured data, such as valuations, real estate lease agreements, and lending agreements, presents a unique challenge. To enhance enterprise-wide visibility and efficiency, it is crucial to extract and integrate data points from unstructured documents (e.g. PDFs) into treasury, finance, portfolio monitoring, and RE division systems. Various tools are available to streamline these processes and drive substantial efficiencies.
Asset managers are adopting various strategies to address the data challenges in private markets:
State Street’s 2021 survey highlighted the increasing need for data standardisation in private markets. Three years later, progress has been slow. However, key standards organisations like ISO, FIBO, and AIMA are making headway in data definitions and messaging protocols.
Automating valuation sourcing and standardising data could significantly reduce human error and resource costs. This shift will enable asset managers to switch the focus of their operating model from data collection to more control and governance activities. This will ultimately provide enterprise-wide transparency on valuation metrics and statuses, along with the current internally computed estimated fair valuation figures and the latest and historic actual valuations.
Optimising data is crucial for enhancing private market operations as asset managers diversify into this sector. The growing importance of private markets highlights the need for efficient data management to handle non-standardised and unstructured data, monitor performance, and achieve comprehensive investment views.
Addressing these challenges and complexities requires aligning business, data, and technology strategies and standardising processes. By integrating these elements effectively, asset managers can navigate the complexities of private market data, ensuring efficient data management and operational success.
Implementing these strategies can help your firm achieve better data quality and operational efficiency. For more insights and tailored solutions, explore our data practice area and see how we can support your data optimisation goals.