Investor trust, reputational risk, and regulatory scrutiny

Lack of conscientious data quality within the overall data management umbrella (they are inextricably intertwined) is unacceptable and unaffordable in the private markets environment. It is reliant on organized information to tame structure, strategy complexity, and the enormous volumes of data. Above, we did not list a couple of overarching items – ramifications that would be bulleted under every one of the six dimensions of data quality: reputational damage and trust.ⁱ

A bad reputation in the eyes of investors and regulators

When we talk about reputational damage, we are not necessarily referring to a lurid PR crisis that dominates the national financial news for an afternoon, although that is possible in large firms. I am talking about the people who keep the lights on. The delivery of error-ridden and/or late NAVs to LPs is bad enough. Throw in the optics of overstated NAVs and other NAV distortions, and the firm has created a credibility problem with investors and a loss of trust that could lead to difficulty in fundraising.

Our Aquata data platform’s data integrity functions, including automated data quality tools, were engineered to prevent the kind of liquidity mismatches and valuation challenges that a manager may grapple with in scaling or adding strategies like ABF. For a deep dive into the challenges of daily NAVs and simplifying accounting methods for ABF, see our earlier article: The New Engine of Private Credit: Why ABF Demands Better Infrastructure.

Meanwhile, regulators and auditors will take notice and sniff around for a firm’s shortcomings in internal controls, data governance inadequacy, and lack of automation. Moreover, with the recent US policy change opening the door to greater retail involvement in private markets through retirement plans, firms will soon encounter bursts of data volume and severe accounting complexities, if they are not prepared.ⁱⁱ

With so much on the line in terms of reputation and risk management in marshaling the six dimensions of data quality, our engineers designed our AI Copilot to be an accelerator of data quality oversight. The days of manual review of spreadsheets in search of exceptions or trying to find out by whom, where, and when a data value was amended have to end. Doing quick casual numbers crunch, a hypothetical $50B private-markets manager’s systems might process up to 4 TB of data - higher if it ingests large unstructured datasets in a typical day.

The Aquata AI Copilot is embedded into both rules management and exception management. Data scientists – as well as non-technical business users across departments - can use the copilot to create, maintain, and iterate data quality rules for operational and data workflows. Out-of-the-box data quality tools automates governance and data lineage to check detect, diagnose, and resolve exceptions, before it is distributed to users. For more information on how AI agents are enhancing data quality, automating tasks, and driving operational efficiency, see our previous article The Agents Are Coming to Finance.