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What LPs actually want
from their GPs' data rooms.

After 200+ LP interviews, we've learned that the data room problem isn't about data—it's about trust. What institutional investors are really asking for when they ask for more transparency.

Last year we interviewed 217 limited partners—endowments, pensions, family offices, funds of funds—about what they wanted from their GP relationships. The conversation always turned to data rooms. And the frustration was universal.

The complaints were specific. PDFs instead of spreadsheets. Definitions that changed between quarters. Metrics that stopped being reported when they turned negative. Data room access that expired before the LP's investment committee could review the materials.

But when we asked what the LP actually wanted, the answer was rarely about file formats or data granularity. It was about trust. Could they trust that the GP was telling them the whole story? Could they trust that today's strong performance was not hiding tomorrow's problems? Could they trust that they were seeing the same data as every other LP in the fund?

The data room is not a compliance obligation. It is a trust-building mechanism. Most GPs have this backwards.

What transparency actually means

The institutional LPs we spoke with—the ones allocating $500 million or more annually to private markets—did not want more data. They wanted interpretable data. They wanted to be able to ask questions like "how did Fund III companies that were underwater at year two perform relative to the ones that were ahead of plan" and get an answer in minutes, not weeks.

This requires three things that most GP data rooms do not provide:

Longitudinal consistency. The same metrics, defined the same way, reported at the same frequency, for the entire life of the fund. If you reported "customer acquisition cost" in Q1 2022, you report it in Q4 2025, even if it got worse.

Granular company-level data. LPs understand that portfolio-level aggregates obscure as much as they reveal. They want to see the performance distribution: which companies are driving returns, which ones are dragging, and whether the GP's value-creation thesis is working.

Contextualized anomalies. When a metric changes dramatically, the best GPs proactively explain why. Revenue spiked 80% quarter-over-quarter? Great—was it organic growth, an acquisition, or a one-time contract? LPs can handle bad news. What they cannot handle is discovering it three quarters late.

The trust tax

Several LPs described a phenomenon they called the "trust tax." When they trust a GP, they approve re-up decisions quickly, they respond to co-investment opportunities within 48 hours, and they refer other institutional LPs. When they do not trust a GP, every decision requires committee escalation, every data point gets audited, and they stop taking calls.

The LPs who trusted their GPs did not necessarily have better returns. But they had predictable returns. They knew what to expect, they knew how the GP would behave in different scenarios, and they knew they would hear bad news directly from the GP before they heard it from the market.

The data room is where this trust is built or destroyed. A GP who reports the same metrics, in the same format, every quarter for ten years—even when some of those quarters are ugly—earns credibility that no marketing deck can replicate.

What the best GPs are doing

The GPs with the strongest LP relationships have made their data rooms a strategic asset, not a compliance burden. They give LPs real-time access to portfolio data, not quarterly PDFs. They benchmark their companies against industry peers and share the comps. They let LPs download the underlying data and run their own analysis.

Most importantly, they treat LP questions as feature requests. When an LP asks for a metric that is not in the data room, the GP adds it—for all LPs, not just the one who asked.

— Anya

Trace Limited Partners Data Rooms
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Anya Reyes
Head of Trace · New York

Anya leads Trace, Semperr's portfolio intelligence platform for private equity firms. Before Semperr she was a data architect at a large endowment's investment office.