A Private Markets Data Quality Primer for Business Users
Data quality might sound straight forward, but in practice it remains one of the most complex and consequential issues firms face. While IT teams and developers may focus on it, business users feel the impact most directly in their day-to-day work. Industry surveys reinforce this point, showing that the consequences of poor data quality extend well beyond IT and directly impact business performance.
In recent years, IT and data science leaders across industries have intensified their efforts to improve data management practices – driven largely by the demands of AI adoption. In this piece, we’ll explore how firms approach data quality and clear up common myths about what it means and how to achieve it.
Data quality’s place in the data management pantheon
What is data quality, part 1: building trust
“Data quality measures value provided by data. High-quality data provides value when it meets the business needs and expectations of its consumers.”i
Across the industry, there's so much just acceptance of bad data, with portfolio managers often taking their data with a grain of salt, even at leading asset management firms. Even before the onslaught of generative and agentic AI, buy-side firms were slogging toward digital and data transformation.
Before we break down how to achieve data quality, it’s important to define the real end goal: trust. Everyone in the organization needs confidence that the data they rely on is accurate and reliable enough to support decisions.
Trust in a firm’s informational integrity also flows from outside the organization. Gaining control over in-house data and improving its accuracy can enhance investor trust. Conversely, poor data diminishes it, especially as investors increasingly demand more frequent and transparent information.
Survey reports across sectors indicate the same basic premise. For a generation now, garbage in-garbage out has meant reputational risk, financial risk, or massive liability.
- Based on a survey of over 550 data and analytics professionals, 67% of respondents admitted they don’t fully trust their data when making decisions.ii
- A survey of CIOs/CDOs in the government sector revealed that 72% rely on reactive data quality management, lacking proactive strategies.iii
- PwC’s 2025 Digital Trends in Operations Survey indicated that operations and supply chain leaders cite integration complexity (selected by 47%) and data issues (44%) as the most common reasons why tech investments haven’t fully delivered the expected results.iv
- Research by Northern Trust showed a global drive to ensure that data is prioritized 72% of the 300 global asset managers who took part cited this as their top concern.v
What is data quality, part 2: useable information
“Data quality refers to the usability and applicability of data used for an organization’s priority use cases — including AI and machine learning initiatives.”
- Gartner
What is data quality, part 2: useable information
Hypothetical: Your firm has added asset-based financing (ABF) for small- and medium-sized business (SMB) to its private credit line. Each loan servicer like BlueVine, Lending Club, and SoFi has a different operating model, file formats, and methods of sharing data, all within a daily NAV environment. An SMB lending pool comes with its own set of risk management dilemmas, like more limited borrower and loan details, tracking borrower attributes and credit worthiness, and time series requirements to see how loan health evolves. Along with this new investment vehicle comes new market data feeds, counterparties, and so on. Data quality matters.
Ingest it. Connectors are valves that siphon currents of new structured or unstructured data flows from data pipelines into models. Securitized investments like ABF lack standardized elements and have dynamic terms and cash flows, making analyzing bid tape pricing and rates over time a challenging task. With some tools, you can create custom data pipelines to ingest datasets from new sources. In this case, high volumes of disparate data from an array of alternative lending platforms, originators, and servicers, which if not properly consolidated, could threaten a precise, real-time view of portfolio health.
Normalize it: In private markets, data infrastructure must normalize multiple views of the same models and map existing data structures to a unified framework. The process of modeling instruments and loan tape cracking at scale using unstructured and inconsistent loan data is particularly intimidating. The process begins with identifying common data entities and their relationships across systems and then ensuring that data is stored in a consistent format, enabling seamless integration, analysis, and reporting across systems.
Our AI-powered unstructured data processing integrated into (link to Aquata subpage when published or similar page--->) Aquata’s data quality framework automates extraction and analysis from PDFs, research reports, and regulatory filings to unlocks insights from previously inaccessible unstructured investment research and compliance documents.
Data quality it: Data integrity tools power automated data cleansing that runs quality checks and automated validation processes, cross-checking incoming data against predefined quality standards to detect and resolve problems and prevent damaging errors.
Govern it: Data governance is your policymaking process that gives everyone in the firm the right access, permissions, and entitlements. This usually falls into the purview of data science/IT people in which they enact clear guidelines and procedures governing the acquisition, storage, sharing, and disposal of data.
What is data quality, part 3: an oversight mechanism
If you search Google for the dimensions of data quality, you get a lot of different answers, some list out six, seven, and some even eight dimensions. We tend to boil it down to six dimensions of data quality: completeness, accuracy, uniqueness, validity, consistency, and timeliness.
Business users can think of data quality as an oversight mechanism to check and recheck to make sure that cleanly ingested, normalized data stays clean and useable. The objective is to detect, diagnose, and resolve exceptions — which are anomalies or mistakes — and flag incorrect data before it lands in the analysis of a risk manager or portfolio manager. What type of exceptions or anomalies should your system be checking for, exactly, when dealing with ABF for SMB loans?
Data quality dimensions you can’t ignore
Accuracy – degree to which data correctly represents real world values or entities
- A 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.
Completeness – presence of required data
- Several loans in the pool are missing borrower NAICS codes, making sectoral exposure assessment impossible.
Consistency / Integrity – consistency of records and their attributes across systems and time
- The borrower for Loan ID 8832 is listed as "Westside Logistics LLC" in the origination system, but "Westside Holdings Ltd" in the servicing platform.
Reasonability – data meets the assumptions and expectations of its domain
- A 24-month small business loan is reported as having a $5 million principal, whereas typical loans in this pool are $50k–$250k.
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—loans aging 60+ days are not flagged for workout or reserve provisioning.
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
Validity / Conformity – 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.
In these ABF-focused data exception examples, these are merely a few of the problems that can become snakebites for accurate performance reporting, cash flow management, and investor trust. These anomalies are not outliers; they’re real risks to capital structure integrity and compliance. How do you find such exceptions? Well, hopefully not by going line by line on an interminable spreadsheet. It’s all about the guardrails – the rules. You set rules for how data should look and then let your data integrity system do the leg work.
“Businesses should build robust data infrastructure to support the increasing demands of data processing, storage, and analysis. This includes adopting cloud-based solutions that offer scalability, flexibility, and cost-efficiency. Companies should also focus on data governance, establishing clear policies and procedures for data collection, management, and usage. Effective data governance ensures that data is accurate, consistent, and accessible, which is critical for making informed decisions.”vii
For private market managers, data quality oversight might include rules like:
- Valuation methodology consistency checks
- Investor allocation checks
- Servicer reported fields checks: e.g., coupon, borrower rate
- Duplicate hash detection
- Business logic thresholds
- Schema validations
If setting up data quality rules sounds imposing, take comfort in using a data management platform that comes with numerous pre-loaded rules to choose from. Aquata’s data integrity tools power automated data cleansing, quality checks, and data validation to detect and resolve inconsistencies, missing values, duplicates, and flag erroneous data.
Firms that deploy low-code and no-code data quality tools help business users help themselves. CTOs that implement self-service tools like our AI-powered Query Studio with Intelligent Suggestions enable a non-technical analyst, portfolio manager, or treasury manager to centralize and join complex datasets and publish them in the data visualization tool for immediate use. Check out our e-book, Discover How Modern Data Platforms Empower Private Market Firms, to learn how intuitive user experience and self-service tools can increase agility and reduce friction.
Automated data integrity: the role of AI and rules engines
If a firm’s data science and technology teams make data quality, governance, and integrity a priority, as many are striving to do, business users’ road to success becomes a straightaway instead of a bumpy highway of hairpin turns, no matter what department in which they work. These lucky business users are working in a firm across which validated information flows freely between departments, better serving the firm’s operations, investors, regulators, and other stakeholders.
We are still in a private credit boom and the public-private asset class convergence has thrown many institutions for a loop. Complexity is not for the faint of heart - or those in want of good data. No matter what ingenious structure, strategies, or vehicles a firm employs, modernized data infrastructure speeds it to market, speeds the reporting, speeds the reconciliation, and speeds everything in between. See our previous article for a deep dive into how data curation means turning investment data into actionable products.
5 Key Takeaways
Q1: Why should private market firms care about data quality?
A: Poor data risks reputational damage, regulatory breaches, and flawed decision-making. High-quality data builds internal and investor trust.
Q2: What defines data quality in practical terms?
A: Data quality is about accuracy, completeness, consistency, validity, timeliness, and uniqueness, ensuring usable, reliable information for critical decisions.
Q3: How does poor data quality impact alternative credit strategies like SMB ABF?
A: Disparate data formats and incomplete borrower info can skew risk assessments, impair cash flow modeling, and reduce transparency.
Q4: What tools and processes help ensure data quality?
A: Automated ingestion, normalization, integrity rules, and governance frameworks are essential, especially using AI-enabled platforms.
Q5: Who is responsible for data quality in a firm?
A: While IT leads implementation, business users depend on it daily. Data governance ensures aligned access, permissions, and trust across teams.
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] George Washington University, https://it.gwu.edu/data-quality
[ii] Precisely, November 5, 2024. https://www.precisely.com/blog/data-integrity/2025-planning-insights-data-quality-remains-the-top-data-integrity-challenges?utm_source=chatgpt.com
[iii] National Association of State Chief Information Officers (NASCIO) and EY US, October 2, 2024. https://www.theconsultingreport.com/state-cios-face-data-quality-challenges-amid-growing-ai-adoption-survey-finds/
[v] InvestmentNews,May 6, 2024. https://www.investmentnews.com/ria-news/asset-managers-are-focusing-on-quality-data-and-a-better-investor-experience/253016
[vi] George Washington University. https://it.gwu.edu/data-quality
[vii] The role of data science in transforming business operations: Case studies from enterprises, Computer Science & IT Research Journal
P-ISSN: 2709-0043, E-ISSN: 2709-0051, Volume 5, Issue 8, P.2026-2039, August 2024, DOI: 10.51594/csitrj.v5i8.1490, Fair East Publishers
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