Valuations errors lead to liquidity mismanagement
Each investment strategy, vehicle, and structure brings unique factors in valuations, investment lifecycle events, liquidity, and risk. In private debt strategies, the value of the assets used as collateral is a critical factor so firms need to ensure assets are accurately valued and whether their value is increasing, decreasing, or stable. The monthly or daily generation of accurate NAVs is reliant on precise borrowing base calculations to determine the amount of money the lender is willing to give a borrower and how much available credit a borrower has. For example, a private markets firm heavy into asset-based finance or other pooled loans that makes a mistake inputting a borrower code will be under the mistaken impression that they have allocated more or less to a certain sector.
Regular monitoring and airtight data quality are essential to maintaining the integrity of the collateral. Missed data quality issues can result in mispriced funds or portfolios, incorrect management fees, and distorted return calculations. GPs and LPs need platforms that can automate complex data-based workflows, harmonize intricate datasets from disparate sources, and power the production of real-time valuations. A firm’s data platform should automatically pull in data and validate it from several sources when dealing with multiple borrowers and data for the underlying collateral, saving enormous amounts of time.
Investor reporting demands transparency and precision
Aside from losing millions of dollars and potentially catastrophic liquidity planning, data quality errors can damage relationships with LPs and regulatory bodies. A 2025 CFA Institute survey revealed that the frequency and accuracy of valuation reporting is the #1 concern of investment managers when it comes to private markets, followed closely by the frequency, comparability, and accuracy of performance measures. Further, 37% called the frequency and accuracy of valuation reporting a substantial problem or total failures.iv
Private markets managers are striving to keep investors and internal stakeholders happy by serving up visually appealing reports with up-to-the-minute, reliable data for investor statements, daily NAVs, trial balances, and cash flow/activity reporting. Manual workflows are woefully insufficient to meet the reporting needs of in-house operations teams, investors, and regulatory bodies. To make this workable, firms can deploy platforms with automated, scheduled reporting operations with reusable templates and self-service functionality, as well as data lineage and integrity functions. The ability to track data history is vital for mitigating operational risk and regulatory risk.
Data lineage is the backbone of compliance and auditability
Using a single golden source of data, managers can free up significant resources currently tied up in gathering data from fragmented sources by implementing automated reports and transparent disclosures that auto-fill forms such as the SEC’s Form PF. The same centralized single source of data also makes possible robust data governance tools to gain pinpoint visibility over lineage. The holy grail for regulatory compliance is observability and auditability when it comes to all data records.
Data lineage gives risk and compliance teams an in-depth understanding of the history and flow of data within their systems for items like journal entries, investor cash flows, and complex assets. And they can trace how a dataset was transformed and amended from the moment it was ingested through the present day. Automated data lineage tools ensure that all data flows remain auditable and allow users to identify, diagnose, and fix data exceptions. Even better, the best data lineage tools store information in multiple timelines with bitemporal “as-of and as-is" modeling, thus preserving reliable historical information for precise audit trails. When regulators call, these firms will answer promptly and build an aura of trust and reliability among regulators and investors.
Persistent, enterprise-wide data quality
Private markets firms can improve their value proposition by installing modern automated data quality solutions. Moreover, data quality automation is indispensable when rolling AI tools out enterprise wide. However, a firm has not achieved data quality until its controls become persistent and transparent, and anomalies can be swiftly resolved before causing problems.
Private market managers can go a long way in containing risks in their increasingly complex investment strategies with a data quality framework. Firms can tame the public-private asset convergence beast with buttoned-up data quality processes that prevent errors in all six dimensions: accuracy, completeness, uniqueness, validity, timeliness, and consistency. With transaction volumes and strategy sophistication growing beyond human capabilities, automation in data quality functions is mandatory.
