Why Data Quality Management Is Business-Critical in Private Markets
In our previous article, A Private Markets Data Quality Primer for Business User, we grazed the surface of what data quality actually is, its place in the data management paradigm, and how private markets firms might benefit from it. Let’s go a bit deeper this time to see how buttoned-up data quality practices improve cross-organizational functions that hit the bottom line from many directions. Data quality is a key pillar of data management, without which, unearthing operational efficiencies through digital transformation is impossible.
In part 2 of the previous article mentioned above, we offered examples of errors in each data quality dimension, using a hypothetical example of asset-based financing (ABF). ABF presents a real-time data challenge, with its complicated asset-level transparency, ongoing collateral monitoring, and bespoke structure tracking. Using the same examples from part 2, let’s see what the bottom-line consequences of the data quality errors net out to be.
Bottom-line ramifications of data quality errors in each dimension
Accuracy – degree to which data correctly represents real world values or entities
- A $100 million loan is reported with an interest rate of 12.5%, but the actual contractual rate is 15%. The servicer misapplied a promotional offer from a different tranche.
- The firm has not collected enough interest, resulting in $2.5 million shortfall – per year, if not corrected.
- Oh, and the firm will be sending out errant NAVs that result in incorrect valuation reporting and performance metrics.
- Things do not improve from there. The damage ripples out into mispriced risk and leverage and liquidity planning.
Completeness – presence of required data
- Several loans totaling $5 billion in the pool are missing borrower National Association of Insurance Commissioners (NAIC) codes, making sectoral exposure assessments impossible.
- If 20% of these loans were, for example, in the renewable energy sector, and that sector suffered defaults in a downturn, that is a sizeable loss of $1 billion.
- Regulatory and investor reporting comes out askew, so redemptions and enforcements might follow.
- And, again, the future health of the firm is affected as portfolio strategy planning and valuations are put into motion using bad information.
Uniqueness – degree to which data is allowed to have duplicate values
- Two separate loan IDs are assigned to the same small business borrower for the same original loan, due to a duplicate ingestion error.
- This is about double counting; aside from the obvious problem, management fees also get overstated.
- Double counting may breach exposure limits, leading to unnecessary hedging or capital allocation shifts.
- Stress testing and risk-weighted asset figures would be inaccurate.
Validity – data conforms to the defined domain of values in type, format, and precision
- The maturity date of a small business loan is populated as “2024-02-31” — which isn’t a real calendar date.
- The February 31 snafu results in misstated loan terms in amortization schedule, cash flow modeling, and compliance reporting.
- Repayment schedules are wrong and valuations could be under- or overstated.
- What follows is NAV reporting, cash flow projects, and liquidity planning missteps.
Consistency – consistency of records and their attributes across systems and time
- The borrower for the loan is listed as Westside Logistics LLC in the origination system, but Westside Holdings Ltd in the servicing platform.
- Bad reconciliations and mismatches in reporting, monitoring, and servicing ensue.
- The system might see the borrower as two separate companies, reducing NAV, incorrect fees and AUM underestimated by hundreds of millions.
Timeliness – data is up-to-date and/or available when it is needed
- Daily update of delinquency status fails to arrive from servicer on time, and loans aging 60+ days are not flagged for workout or reserve provisioning.
- This tardiness of data availability means that credit teams lose valuable time to negotiate with the borrower or seize collateral; delays mean that the borrower won’t be able to pay back the entire amount.
- Mismanaged liquidity buffers could result, increasing the risk of a liquidity crunch.
- Again, NAVs will be overstated, and excess fees will be collected; clawbacks loom large for this firm.
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.
“The (private credit) sector faces significant data quality problems, characterized by a lack of universal identifiers for different market participants, the prevalence of unclean data and a multitude of data vendors, which leads to confusion over data ownership and a cluttered data environment.”
- EY The lender’s edge: data strategies for private creditiii
Don’t underestimate the ROI of operational efficiency
Without conscientious data quality management and infrastructure investment, middle- and back-office operations are an exercise in the big data chase. Without sound data ingestion, transformation, normalization, and standardization – capped off by scalable storage in a single source of truth, intelligent people are tasked with chasing down the information they need – it's a restricted access zone. In terms of data quality oversight, for example, staff must manually investigate and correct data when there are flagged anomalies — creating bottlenecks and extra compliance costs.
“In a world that is exploding with data, firms need to upgrade their operating models, and at the same time harness and optimize the increased amount of data going through their pipelines. The premium on good quality data is at an all-time high, given the volatility.”
- Ted O'Connor, Head of Sell-Side Business Development, Arcesium
The exercise of fixing errors in the six dimensions listed above is not a mere keystroke. It can be a multi-million-dollar time-killer. Staff may spend cycles reconciling mismatched balances. Legal disputes with borrowers and clawback disputes are pricey resource drains. Rectifying a data completeness problem such as chasing down missing NAIC borrower codes requires borrower outreach, data vendor costs, or manual research. In a large portfolio, cleaning data could result in millions in additional costs.
It is not inaccurate to assert that data quality and excellent data management are prerequisites to automation and modernization of investment lifecycle operations. Aside from preventing the litany of seemingly small errors that carry nasty ramifications, a data quality mindset also unearths operational efficiencies, preserves trust with key stakeholders, and redirects of human ingenuity toward driving returns instead of manually rifling through paper searching for the answer.
5 Key Takeaways
Q1: What happens if accuracy fails?
A misreported loan rate means lost interest, misstated NAVs, and liquidity mismanagement — costing millions annually.
Q2: Why does completeness matter?
Missing borrower data blinds sector risk analysis, hiding concentration risk that can trigger billion-dollar losses in a downturn.
Q3: How do duplicates (uniqueness) hurt?
Duplicate loan IDs inflate AUM, misstate fees, and distort stress tests — creating regulatory and reputational exposure.
Q4: What’s the danger of invalid or inconsistent data?
Impossible dates or mismatched borrower names break cash flow models, valuations, and reconciliations, leading to hidden exposure.
Q5: Why is timeliness critical?
Delayed delinquency updates stall workouts and reserves, overstating NAVs and fees — and worsening recoveries in stressed conditions.
Authored By
Ankit Jain
Ankit Jain is a seasoned Solutions Architect with over 12 years of experience in building product-driven solutions for the investment management industry. With a unique blend of product and technology expertise, he partners with asset managers, CTOs, and business operations leaders to solve complex data challenges. At Aquata, he helps clients transform data into actionable insights, driving efficiency and innovation across their organizations.
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[i] The Accounting Review (2018) 93 (1): 317–333. The Credibility of Financial Reporting: A Reputation-Based Approach, https://publications.aaahq.org/accounting-review/article-abstract/93/1/317/3940/The-Credibility-of-Financial-Reporting-A?redirectedFrom=fulltext
[ii] Investment Advisers Association. https://www.investmentadviser.org/events/access-to-private-market-investments-for-retail-investors/
[iii] EY, The lender’s edge: data strategies for private credit, December 19, 2024. https://www.ey.com/en_us/insights/wealth-asset-management/data-strategy-in-private-credit
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