How data maturity impacts IRR: The competitive edge in private equity

In a private equity market where access to capital is abundant and deal flow is increasingly commoditized, differentiated returns come from speed and specialization. At many mid-market companies, however, the velocity of deal execution and the accuracy of LP reporting are bottlenecked by a hidden operational drag: data wrangling.

Asset management businesses obsess over IRR, MOIC, and DPI. They model sensitivity tables down to the second decimal place. But most undercount the operational drag that erodes those numbers before a single investment thesis is tested.

Every hour an associate spends reconciling capital call data across three systems, every afternoon a VP burns normalizing vendor feeds against internal classifications, every fire drill before quarterly LP reporting are direct costs on fund performance that never appear in the fund model or show up in the carry calculation. Instead, they show up instead as slower deal execution missed screening windows, and valuations built on data that someone meant to double-check but didn’t.

This post quantifies that cost, explains why it increases as private markets grow, and describes what asset managers at the frontier are doing differently. Beyond making a generic case for improving data quality, the goal is to be specific about how data maturity connects to the metrics asset management professionals care about most.

The data wrangling tax

Asset management professionals spend a significant share of their time on data collection and management tasks rather than analysis and decision-making. This mirrors broader findings in data-heavy roles, where up to 80% of time can be spent collecting, cleaning, and organizing data rather than generating insights. At a firm with ten investment professionals and an average fully-loaded cost of $300,000 per year, even a conservative estimate of 20–30% translates to roughly $600,000–$900,000 annually spent on work that produces no analytical output.

The workflow will be familiar to anyone who has worked in the asset class. Portfolio company financials arrive as PDFs or Excel files with inconsistent chart of accounts structures, and NAV and position data from fund administrators use different entity identifiers than the firm’s internal portfolio management system. Third-party market data from Bloomberg, MSCI, or Preqin arrives in its own schema, requiring manual mapping before it can be compared against internal benchmarks. Deal pipeline data lives across systems, including the institutional memory of whoever ran the last screening process.

At most companies, none of this is handled by a dedicated data team. In middle market and growth equity, investment professionals are the data team. 

Fixed income desks face a parallel version of the same problem. Normalizing pricing data across multiple sources, reconciling credit ratings from Moody’s, S&P, and Fitch against internal risk models, and managing reference data for OTC instruments all require the same labor-intensive stitching process. They all experience the same operational drag.

The highest cost is the opportunity cost of what those hours could have produced: more deals screened, portfolio company conversations, or time on the specific analytical work that actually generates alpha.

Private markets growth is outpacing data infrastructure

Global private markets AUM reached approximately $13.1 trillion in 2023, according to the McKinsey Global Private Markets Review 2024. The capital has scaled. The data infrastructure supporting those assets largely hasn’t.

Public equities benefit from decades of standardization: CUSIP, ISIN, ticker symbols, and exchange-mandated reporting create a baseline of interoperability that asset managers simply don’t have. Private market data is inherently unstructured. GPs use bespoke fund structures. Portfolio company reporting varies by management team, CFO sophistication, and whatever template the operating partner sent three years ago. Valuation methodologies differ across companies in the same portfolio, let alone across funds.

Every new fund, co-investment vehicle, or continuation fund adds another layer of data complexity without adding corresponding infrastructure to manage it. The fragmentation compounds with each vintage.

Third-party data dependency is creating new exposure

Asset management companies increasingly rely on external data providers for market intelligence, ESG scoring, benchmarking, and proprietary deal sourcing signals. Each vendor delivers data in its own schema, with its own entity identifiers, update cadences, and quality levels that are rarely transparent to the buyer.

Without automated validation at the point of ingestion, bad vendor data flows directly into models, valuations, and investor reports. This might show up as an ESG score that was recalculated mid-quarter, a revenue figure that reflects a restatement the provider hasn’t flagged, or a fund identifier that maps to two different entities in different systems. They’re the kind of errors that surface during LP due diligence or after a valuation has been published. The firms that haven’t yet built a validation layer between vendor data ingestion and downstream consumption are operating with an unpriced risk in their data stack.

From data maturity to IRR: connecting the dots

Deal sourcing and screening velocity

Companies with mature data practices can screen more deals faster. The reason for this is structural; when pipeline data is already normalized and enrichable, analysts can query a unified dataset rather than spending two days assembling the spreadsheet that makes the query possible in the first place.

Speed matters disproportionately in competitive auction processes. A company that can complete preliminary due diligence in eight days rather than three weeks has a structural advantage that compounds across a fund’s life. It sees more deals, makes faster passes on the ones that don’t fit, and arrives at IC memos with more time to pressure-test the thesis. It comes down to decision velocity, and it’s worth several turns of the multiple on the deals where being faster actually changes the outcome.

Portfolio monitoring and value creation

Post-acquisition, the ability to aggregate and normalize portfolio company data determines how quickly an asset manager can identify underperformance and intervene. Manual quarterly reporting cycles mean problems surface three to four months after they start.

Automated data pipelines with quality controls surface issues in near-real-time. When a portfolio company’s gross margin compresses in a way that’s inconsistent with prior quarters, a firm with continuous monitoring flags it within days. Here, data maturity translates directly to MOIC: faster identification of value creation levers, earlier intervention on underperforming assets, more accurate exit timing based on actual data rather than lagged reports.

Investor reporting and LP confidence

LPs are asking for more granular, frequent, and standardized reporting. ILPA templates represent the floor, while institutional LPs increasingly want custom analytics layered on top. The ILPA Reporting Templates have expanded significantly, with enhanced fee reporting and diversity metrics reflecting a broader shift toward LP accountability expectations.

Companies that can produce accurate, consistent reports build LP trust in a way that shows up in fundraising conversations. Capital raising from existing LPs on favorable terms is, in itself, an IRR driver. Inconsistent or error-prone reporting, however, erodes the institutional confidence that makes those conversations go smoothly.

What asset managers at the frontier are doing differently

The pattern emerging among companies that have moved beyond manual, fragmented data operations comes down to three operational shifts. None of them require replacing the investment team’s existing tools overnight, but all of them change where the work happens.

1. Automating normalization and stitching at the ingestion layer.

Rather than letting raw vendor data and portfolio company feeds flow into analyst spreadsheets, leading companies intercept data at the point of ingestion, apply automated quality rules, and normalize it against a common data model before it reaches the investment team. This means investment professionals start with clean, consistent data rather than spending time creating it.

Ataccama ONE’s data quality and observability capabilities operate at exactly this layer. The platform monitors data as it flows through pipelines, flags anomalies before they reach downstream models, and routes exceptions to the right owners. When a portfolio company CFO sends a P&L with a different account structure than last quarter, the inconsistency is caught and resolved at ingestion rather than discovered halfway through a board presentation.

2. Unifying entity and reference data across systems.

Asset management companies deal with the same entities represented differently across every system they use. For example, a portfolio company can appear as “Acme Holdings LLC” in the fund admin platform, “Acme Holdings” in the CRM, and “ACME” in the general ledger. A fund’s co-investors appear under four different name formats depending on whether the data came from legal documents, the fund administrator, or a third-party database.

Master data management resolves these into a single, trusted view of each entity. The pattern shows up consistently among businesses that have made this investment. Ataccama ONE MDM capabilities provide the entity resolution layer that makes it possible to answer “what is our total exposure to this portfolio company across all vehicles” without an extensive data reconciliation exercise.

3. Validating vendor data before it enters models.

Instead of discovering data quality issues when a model produces unexpected results, businesses at the frontier apply automated validation rules at the point of vendor data ingestion. This means a data feed from Preqin or MSCI gets checked against expected schemas, value ranges, and internal reference data before it touches a valuation model or investor report.

This validation layer is precisely what distinguishes companies that have absorbed a vendor data quality incident from those that will in the future. According to the EY Global Private Equity Survey 2023, businesses with mature data and technology capabilities achieve more reliable decision-making, efficiency, and insight generation. The validation layer is a major part of how that accuracy gets built.

Measuring the data maturity–IRR connection

Isolating data maturity’s specific contribution to IRR is difficult, but there are leading indicators that asset management firms can track. These indicators correlate strongly with lagging financial performance across fund vintages.

Proxy metrics worth monitoring include:

• Time from deal sourcing to IC memo
• Hours spent on data preparation versus analysis per deal
• Frequency and severity of data-related corrections in LP reports
• Time to produce quarterly LP reports
• Number of manual reconciliation steps in the portfolio monitoring workflow

Companies that improve on these metrics consistently outperform on IRR and MOIC over time. Better data operations free up investment professionals to do work that creates more value.

Ataccama’s data quality and observability capabilities provide the instrumentation to track these metrics automatically rather than through self-reported estimates, which means the measurement baseline itself stops being a manual exercise.

The specialization imperative

The companies that will generate top-quartile returns over the next decade are already treating data infrastructure as a core investment capability. Evidence for this accumulates through every stage of the investment lifecycle, from faster screening to earlier monitoring and cleaner reporting.

As AI-assisted deal screening, portfolio optimization models, and automated due diligence tools become standard in the asset class, the quality of upstream data becomes a constraint. Models trained on unreliable data produce unreliable outputs, and AI agents operating on fragmented, unnormalized data amplify errors.

The firms building the data foundation now are building the infrastructure on which the next generation of investment tools will run. It’s a compounding advantage that every asset manager can benefit from.