Data Quality for Private Markets: A Checklist for What You Need to Know
September 26, 2025
Last Updated: September 24, 2025
Read Time: 4 minutes
Authors: Shobhit Sheel
Data & Governance
Private Markets
Authored By
Shobhit Sheel
Shobhit is a financial technology leader with 16+ years of experience across private credit, structured credit, and CLO operations. At Arcesium, he serves as a Forward Deployed Solution Architect, enabling clients with product capabilities and tailored technology solutions to drive efficiency and innovation.
High-quality data helps fuel success in private markets. Low-quality data? This, on the other hand, brings the potential to insert risk, damage relationships, slow down operations, and create several challenges for your business.
Naturally, every firm aims to work only with high-quality data. Yet that’s often easier said than done, especially when it comes to the nuances of private markets. With bespoke deal terms, growing deal volumes, stringent reporting requirements, and a diverse set of stakeholders, private market managers continuously face manual processes, varied data formats, and a lack of standardization across mounting volumes of data when it comes to running their business.
As a resource, the information below covers key considerations designed to help your firm identify data quality challenges and take action. We'll also outline technology considerations and ways to help improve data quality, with the goal of better supporting your firm’s operations, efficiency, and growth.
What are the signs that data quality might be an issue?
From a business perspective, data quality is more than just about operations or compliance. It has the potential to impact decision-making, slow growth, or even take up precious resources if your internal teams are spending a lot of time manually aggregating or cleaning up data.
Ask yourself:
Does your operations team often need weeks to respond to custom reporting requests?
Do clients frequently question the information provided in their reports?
When working with data, do you find missing values or duplicate information?
Do you know where your data is coming from, who’s using it, or how it may have changed over time?
Can your systems handle growing data volumes and complexity?
From new and expanding asset classes to diversified distribution channels, private markets have experienced incredible growth and transformation over the past decade. At the same time, many firms are still relying on a patchwork of systems, fund admins, manual processes, and spreadsheets, lacking a unified data fabric to connect the various elements of their business together for scale.
Manual work introduces the potential for error, and maintaining high-quality data is going to be challenging if the right systems aren’t in place. Modern platforms can be available as a solution and help better embed data quality capabilities into a firm’s overall ecosystem.
Here are key components of a platform to consider:
Data collection and ingestion capabilities to unify data from various sources and formats, particularly when one deal has multiple names across sources.
Data cleansing and normalization to standardize data formats and identifiers.
Data governance and quality checks to help ensure compliance across data accuracy, completeness, and validity.
Data analysis and reporting from a golden source of truth to maximize data potential and streamline reporting requests, whether scheduled or ad-hoc.
How can data quality be evaluated?
Private market firms deal with incredible amounts of complex data. As firms grow and scale, even small inaccuracies can have big consequences. These inaccuracies will also be increasingly difficult to spot through manual efforts, or without a robust system. Modern data platforms can be designed to help ensure data quality, preventing inaccuracies from becoming pervasive throughout your organization.
Here are a few questions to consider across six dimensions of data quality:
Accuracy: Does the data reflect reality? Are there typographical errors from manual processes, or discrepancies as data moves from one system to another?
Completeness: Does the dataset have all the required information? Are data fields left blank, or missing?
Consistency: Is the data synchronized across the organization? Do you have standard definitions for data coming from different sources or formats?
Timeliness: Is the data available when needed? Or are you faced with latency issues?
Validity: Is the data in a specific format, follows business rules, and can be used with other sources?
Uniqueness: Is the data only recorded once in the dataset? Are you missing unique identifiers or dealing with duplicate data?
What are other ways technology helps to manage data quality?
As a modern data platform and the data quality dimensions covered above form an important foundation, private market firms may also want to explore additional elements to enhance their data quality journey. Here are a few considerations that can make the process easier and better aligned to the unique needs of your business:
Pre-built connections to financial and market data sources that streamline ingestion, reduce manual work, and build in data quality parameters as part of the intake process.
AI-powered assistance that allows users to state requirements in natural language, with technology translating them into data quality rules.
Data quality rules out-of-the-box with the option to customize them as needed to streamline and shorten time to value.
Data lineage capabilities to help identify, diagnose, and troubleshoot data quality issues, as well as identify the origin of data, and understand if or how the data changed.
Conclusion
High-quality data is crucial to success for private market firms, supporting not just technology and operations, but the business as a whole. From client relationships and growth to overall efficiency and better decision-making, strong data quality can have a meaningful impact across the firm. By investing in the right technology, firms can maximize the potential of their data and unlock greater value.
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