How Asset Managers Scale Data Quality Across Valuation, Close, and Reporting

Asset management firms run on reused data. Portfolio company financials, valuation inputs, ownership structures, cash movements, and reference data are pulled into investment analysis, fund accounting, finance, investor reporting, and regulatory filings—often multiple times, across multiple systems.

The consequences of that data degrading are not isolated. Small breaks compound as information rolls up from portfolio companies to funds to enterprise reporting. When they do become visible, they surface as NAV restatements, delayed closes, investor questions, or audit findings.

The underlying problem isn’t that controls don’t exist. It’s that most controls continue operating on data that has already changed. Traditional data quality processes detect issues after aggregation, reconciliation, or reporting, when remediation is slow and costly.

Asset managers use Qualytics to shift data quality upstream, validating data continuously before it feeds data systems, analytics, and AI workflows. Below are nine common ways firms operationalize data quality with Qualytics.

Maintaining Portfolio Company and Legal Entity Integrity

Asset management firms depend on accurate representations of portfolio companies, funds, legal entities, and ownership hierarchies. These entities drive consolidation, exposure analysis, fee calculations, and investor reporting.

In practice, entity data is fragmented across portfolio company submissions, administrators, accounting platforms, CRMs, and third-party sources. Naming variations, missing identifiers, and hierarchy breaks are common—and difficult to detect once data is aggregated.

Qualytics is used to continuously validate entity data across systems. Entity resolution aligns records representing the same real-world entity, while completeness and cross-record consistency checks ensure relationships between portfolio companies, funds, and reporting structures remain intact.

Detecting Quarter-End Submission Changes Early

Quarter-end is one of the highest-risk moments for asset managers. Portfolio companies submit preliminary financials, followed by revised and final versions under compressed timelines. Even small changes between versions can materially affect reported performance and internal decision-making.

Manual spreadsheet comparisons and one-off scripts struggle at scale and often miss subtle but meaningful differences.

Qualytics automates version-to-version data comparisons, identifying record-level changes as soon as updated submissions arrive. Teams can immediately see what changed, where it changed, and whether the impact exceeds defined thresholds.

A global alternative asset manager uses Qualytics to reconcile 40,000+ CSV files per day across a multi-day quarter-end submission window. End-to-end data diff execution completes in under two minutes per run, and submissions that pass defined checks often require no manual review at all, significantly accelerating close. 

Validating Valuation Inputs Before NAV Calculation

NAV accuracy depends on the quality of pricing, position, and valuation inputs sourced from portfolio systems, custodians, pricing vendors, and fund accounting platforms. Issues often stem from missing prices, stale valuations, or incomplete position coverage—not calculation logic.

Asset managers use Qualytics to validate valuation inputs before NAV calculations run. Freshness and completeness checks confirm data readiness, while reconciliation and aggregation comparison checks align values across sources.

By validating inputs early, firms reduce the risk of restatements and protect investor confidence while maintaining trust in preliminary numbers.

Aligning Holdings and Position Data Across Platforms

Holdings and position data is aggregated across trading, custody, portfolio management, and accounting systems. Breaks between these sources distort exposure views and can ripple into valuation, risk analysis, and reporting.

Qualytics enables continuous alignment of holdings data using existence, reconciliation, and aggregation checks. These controls ensure positions are consistently represented and correctly rolled up across portfolios and funds.

As data volumes scaled beyond 4 billion records growing 10% year over year, one firm relied on automated reconciliation in Qualytics to maintain holdings integrity without expanding operational teams.

Controlling Risk from Third-Party Data Feeds

Third-party data concentrates risk at the source. Market pricing, benchmarks, reference data, ESG metrics, and portfolio company files are reused across valuation, performance, and reporting processes.

Late deliveries, partial populations, schema changes, or definition drift can weaken multiple downstream controls at once.

Asset managers use Qualytics to validate third-party data at ingestion with freshness, volume, completeness, and schema drift checks. Time-series metrics surface abnormal distribution shifts before data is consumed broadly.

Scaling Operational Reconciliation Without Adding Headcount

Capital movements, allocations, and intercompany activity depend on consistent calculations across systems. Small discrepancies can materially affect reported results and LP communications.

Qualytics replaces ad hoc, manual reconciliation with continuous validation at scale. Automated checks surface mismatches early, allowing teams to resolve issues before close, audits, or investor reporting.

Despite operating at enterprise scale, one asset management customer manages data quality with just 1.4 FTEs at 75% allocation, while expanding its program 4x in 18 months across new reconciliation and financial use cases.

Strengthening Regulatory Reporting Inputs

Regulatory reporting depends on accurate aggregation of data across portfolio management, accounting, valuation, and reference systems. Incomplete populations or misaligned classifications can undermine filings even when reporting logic is correct.

Qualytics is used to validate completeness, consistency, and aggregation integrity before regulatory calculations begin. Schema drift detection highlights changes that could invalidate reporting pipelines.

Customers have seen an estimated 18x ROI in the first year by using Qualyitcs to replace reactive, manual data quality processes with automated, continuously enforced controls.

Retaining Audit-Ready Evidence of Data Controls

Data quality controls often exist, but evidence of execution is fragmented across scripts, logs, and spreadsheets. During audits or investor diligence, teams scramble to reconstruct proof under time pressure.

Qualytics retains centralized, traceable evidence of control definitions, results, failed records, and remediation actions. Time-series views demonstrate control effectiveness over time.

Enabling Shared Accountability Across Finance, Investment, and Data Teams

In many firms, data quality accountability sits primarily with technical teams, even though finance and investment teams own valuation and reporting outcomes. This misalignment slows remediation and creates bottlenecks.

Qualytics enables shared ownership by allowing business users to define and refine checks while data teams maintain centralized governance and scalable frameworks.

One customer expanded to 15,000+ production rules, 96% automated, enabling 50+ business users to co-own data quality. Qualytics outputs now reach approximately 300 business users across the organization.

Asset Managers Use Qualytics to Enforce Trust Upstream

Across asset management, data quality failures rarely announce themselves. They quietly weaken valuation, reporting, and regulatory controls while automation and analytics continue operating as if nothing has changed.

Asset managers use Qualytics to validate data before it is consumed—protecting valuation accuracy, reporting integrity, and regulatory outcomes as complexity and automation increase.