How LPs Can Structure Look-Through Analysis in Private Markets

If you’ve decided to perform look-through analysis across your private markets portfolio  — whether as a primary or secondary LP  — congratulations, you’re already ahead of the curve. But the next question is critical: What exactly should you track?

Unlike public markets, there’s no universal standard for portfolio-level transparency in private equity, venture, credit, or real assets. Data varies widely by asset class and fund manager.

To build a scalable and useful look-through system, LPs need to define the core data fields they want to extract, normalize, and monitor—starting with the basics, then layering in asset-class-specific detail.

One of the biggest barriers to scalable look-through analysis is that there’s no single standard for how GPs report data on their underlying investments. What LPs receive depends on who the GP is, what asset class they’re managing, and who the LPs in the fund are.

A U.S. buyout fund might report MOIC and IRR using the ILPA template. A European credit manager might send Excel-based TPT files for Solvency II compliance. A venture fund might just provide an investment schedule with minimal commentary. Meanwhile, ESG metrics could show up in a separate GRESB survey—or not at all.

This fragmentation means that LPs can’t rely on a single framework. Instead, they must define their own internal standard and normalize inputs across managers, vintages, and strategies. Here's a snapshot of the most common reporting standards, and what they’re good for:

Given the inconsistent formats and reporting frameworks outlined above, the key to building a reliable look-through system is to start with what’s most common and consistently available: the investment schedule.

Nearly every GP—regardless of asset class, region, or reporting standard—includes some form of investment schedule in their quarterly reports (QRs) or financial statements (F/S). While the level of detail varies, it typically provides a baseline that LPs can work with and normalize across their portfolio. 

At minimum, it includes:

• A list of portfolio companies
• The fund’s cost basis
• Current fair value
• Often, proceeds to date (in QRs; typically absent from F/S)

Beyond these basics, some GPs include additional fields: investment date, sector, geography, or fund-level performance metrics like IRR or MOIC. But even this "simple" schedule isn't truly standardized—every GP has their own reporting standards.

What makes the investment schedule powerful is that it gives LPs a starting point to map exposures across the portfolio. You can begin answering critical questions like:

• Where is our capital deployed, by sector and geography?
• Are GPs executing on their stated strategy?
• Which exposures are driving returns—or underperforming?

But first, you have to normalize the data.

This is where things get tricky. GPs don’t use a common taxonomy for sector or geography classification. One manager might report a company’s sector as "Software," another might say "Enterprise SaaS," and a third might just write "Technology." You’ll need to normalize these into a consistent category—say, “GICS Level 2: Application Software”—to compare exposures across the portfolio.

The same applies to geography. One GP might label a company as "United States," another as "California, USA," and a third as simply "North America." To roll these up meaningfully, LPs need a mapping logic (e.g., to ISO country codes or standardized region groupings like OECD vs. Emerging Markets).

This normalization work may sound tedious, but it’s essential. Without it, exposure analysis and performance attribution break down—especially across multi-GP, multi-strategy portfolios. In later steps, this clean foundation also allows you to link the investment schedule to more detailed company-level financials and narratives (when available).

While the investment schedule gives you breadth, company-level detail gives you depth. Some GPs stop at the schedule—but many, especially in buyout and growth equity, include richer narratives and financial disclosures for each portfolio company. These typically follow the investment schedule in the quarterly report and may take the form of dedicated one- or two-page write-ups per asset.

These deep dives often include:

• A short company description or investment rationale
• Entry and current financial metrics (revenue, EBITDA, net debt)
• Deal highlights and risks
• Valuation methodology (e.g., public comps, DCF, precedent transactions)
• Notes on recent developments—add-ons, write-downs, exits

This section is where look-through analysis becomes more than just exposure mapping. When extracted and structured properly, this data enables:

• Attribution analysis: Was performance driven by top-line growth, margin improvement, or multiple expansion?
• GP benchmarking: Are some managers better at operational value creation vs. financial engineering?
• Valuation validation: How does the reported EV/EBITDA multiple compare to public comps or sector norms?

But, just like the investment schedule, the format and terminology vary widely.

One GP might provide revenue and EBITDA at both entry and current dates; another might only give one metric. Some include performance commentary, others offer just numbers. Financials might reference different time periods (trailing twelve months, calendar year, or fiscal year) or use different GAAP interpretations. And because companies evolve—through M&A, divestitures, or restructurings—the reported figures may be restated quarter to quarter.

This variability makes standardization critical. You may need to tag whether revenue figures are forward-looking or historical, whether EBITDA is adjusted, or whether a valuation is based on a third-party appraisal or internal mark.

Done right, company-level detail transforms look-through from a reporting task into an investment insight engine. It enables LPs not only to monitor what they own, but to understand why those holdings are performing—and how they align with strategic goals.

Once you go beyond the investment schedule, the nature and richness of look-through data starts to diverge significantly depending on asset class. Each strategy emphasizes different metrics, offers different levels of disclosure, and presents its data in its own format.

Here’s a breakdown of what you can typically expect—along with the kind of look-through fields most useful in each case:

Buyout

• Most detailed reporting, often with one line per company and extensive write-ups
• Financial metrics commonly include entry and current revenue, EBITDA, net debt, and EV
  
• Valuation detail often includes multiples and methodology
  
• Watch for restatements due to M&A or portfolio restructuring

Venture Capital

• Lightest reporting—usually just an investment schedule
• Multiple security types per company across rounds (common vs preferred, SAFEs, etc.)
• Qualitative commentary varies, but often sparse
• May include KPIs like ARR, burn rate, or cash runway for later-stage deals

Growth Equity

• Sits between buyout and venture in detail
• Often includes revenue and sometimes EBITDA, but less consistent than buyout
• Some narrative on company traction, expansion, or round valuations

Private Debt

• Reporting driven largely by Solvency II and insurance LP requirements
• Loan-level data: borrower, tranche, maturity, coupon, performance status
• Financial data: split of distributions in interest, principal repayment, PIK
• May include sector, rating, geography, and duration

Real Estate

• Often guided by INREV templates or bespoke LP requests
• Property-level data includes location, type, appraised value, occupancy, and income
  
• May include capex history, ESG characteristics, and tenant breakdowns

Infrastructure

• Mix of financial and ESG data, often reported via GRESB or Invest Europe guidelines
• Sector-specific KPIs such as contract tenor, offtaker credit, PPA structures, or uptime metrics
  
• Revenue models (regulated vs contracted vs merchant) are especially important

Once you've decided to collect look-through data, the next step is defining how to structure it so it can actually support real decisions. Without the right model in place, you risk either collecting too much irrelevant information—or not enough of the data that matters.

Here are three principles that consistently lead to more usable, scalable datasets.

It’s easy to fall into the trap of overcollecting—grabbing every field that appears in a quarterly report, LPAC deck, or financial statement. But more data isn't always better. The more fields you track, the harder it becomes to normalize, validate, and keep consistent across GPs and asset classes.

The solution is to start with your use cases:

• What do you need to monitor? (e.g., sector or geographic exposure)
• What do you need to validate? (e.g., valuation or performance)
• What do you need to report on? (e.g., to ICs, CIOs, or regulators)
• What decisions are you making that require exposure-level inputs?

From there, work backwards. Define the fields that are critical to those workflows—and build your schema around them. If a field doesn’t help you decide, report, or assess something, it’s probably not worth the effort to track.

As your portfolio grows across strategies—buyout, venture, private debt, real estate, infrastructure—the complexity of your schema will increase. Each asset class comes with different reporting conventions, useful KPIs, and levels of granularity.

Rather than force a rigid template across all managers, it’s better to create a modular structure:

• A core set of fields that apply universally (e.g., company name, sector, geography, cost, fair value)
• Strategy-specific extensions (e.g., coupon and maturity for debt; occupancy and rental income for real estate; ARR or burn rate for later-stage venture)

This approach lets you maintain comparability where it matters—without losing the richness of asset-specific reporting where it counts.

Even once you’ve defined the right schema, you’ll still run into the wild west of GP reporting. Sector and geography tags, in particular, come with wildly inconsistent granularity and naming conventions.

For example:

• One GP might report a company as “SaaS,” another as “Technology,” and another as “Enterprise Software.”
• Geographically, you might see “USA,” “North America,” “California,” and “San Francisco” used for different companies—all in the same portfolio.

Without normalization, your exposure analysis quickly becomes meaningless. That’s why it’s critical to standardize tags—whether that means mapping sectors to GICS or NAICS, geographies to ISO country codes, or valuation methods to a fixed taxonomy.

Normalization isn’t just cleanup—it’s what allows you to run rollups, perform attribution, and compare apples to apples across a messy, multi-GP dataset.

Look-through analysis only becomes valuable when it's structured to serve real decisions. That means focusing on what you actually need to monitor, aligning your schema with the strategies you allocate to, and investing in clean normalization across funds.

But even the best-designed schema is only as good as the data that feeds it. For a deeper look at how LPs are extracting and processing data from GP reports, read our blog on Challenges and Solutions to Collecting Look-Through Data.

Download our look-through schema template to see how top LPs structure exposure data across buyout, venture, credit, and real assets.