The phrase “data-driven” has become so overused in private markets that it’s nearly lost meaning. Every firm claims it. Pitch decks reference proprietary data, systematic sourcing, rigorous analytics. And yet in the same industry, associates still spend hours pulling from pitch books and digging through recent news articles just to prepare for IC. Partners still walk into LP meetings uncertain whether their benchmarks will hold up under scrutiny. ICs still end up making conviction calls on what someone remembers from the last conference.
There’s a gap between what firms say about their data and how data actually functions in their decisions. Closing that gap is what separates firms that are genuinely data-driven from firms that are merely data-aware.
The distinction matters more now than it did five years ago — and the reason is structural.
The information asymmetry problem in private markets
Private markets have always operated with asymmetric information. That’s partly where alpha comes from. The GP who knows a sector more deeply, has a relationship the next buyer doesn’t, or sees around a corner that the market hasn’t priced yet — that’s the edge.
But there’s a second kind of information asymmetry that works against GPs, not for them: the gap between what a firm knows about its own performance and what the market looks like.
Intapp serves more than 1,700 private capital firms globally — including more than half of the PEI 300 — and the pattern is the same across all of them. Every firm has proprietary data accumulated over years of deals, relationships, and portfolio monitoring. That data is real and valuable, but it reflects one point of view: its own. It can tell you what you’ve done, but not how you compare to what everyone else has done. And as LP sophistication increases, the ability to answer the second question has become as important as the first.
LPs are no longer walking into diligence meetings as passive recipients of your narrative. They’re pulling independent benchmarks beforehand. They know your vintage, your strategy, and roughly where you should land before you say a word. The question they’re really asking when they sit across from you isn’t “How have you performed?” — it’s “Can you contextualize your performance against a picture I already have?” GPs who can’t answer that question on the same evidentiary basis the LP is working from don’t lose the argument. They simply operate with a disadvantage that increases with every conversation.
Three forms of capital – and why the third one is underbuilt
Private capital has always treated financial capital as the primary asset: the money you raise, the money you deploy, the returns you generate. But there are two other forms of capital that determine which firms outperform over the long run.
The first is institutional capital: the encoded knowledge, process discipline, and decision-making frameworks that lets a firm do the same things consistently and at scale — how deals get sourced and screened, how IC prep works, and how the LP relationships are managed from first conversation through close. This is what separates a firm that performs well because of who’s in the room from a firm that performs well because of how the room is structured. The former is fragile. The latter compounds.
The second is data capital. This is where most firms are underbuilt — not because they lack data, but because the data they have doesn’t flow to the decisions that need it.
Data capital isn’t the size of your data warehouse. It’s about whether market intelligence reaches a deal team before IC on Friday, whether a fundraising team can contextualize fund performance against a credible peer set before first close, whether the value creation story you tell your LPs at the annual general meeting is grounded in something more defensible than your own assumptions. Data capital, in other words, is measured not by what you have but by where it shows up.
The conviction gap
There’s a specific moment in the life of any investment decision where the absence of embedded benchmark data becomes visible. Linda Yu, VP and product specialist for GP solutions at MSCI, described it recently in a webinar.
An associate is preparing for Friday’s screening committee. A new opportunity has come across the desk. The questions the partners will ask are predictable: How does this target compare to peers, not directionally, but actually? What are deals like this trading at? Not two years ago when the last comparable closed, but now? Can we validate our return assumptions with real market data, or are we defending our assumptions with more of our own assumptions?
Without benchmarks embedded in the workflow, one of two things happens. Either the associate spends significant time assembling data from secondary sources — pitch books, news, conference recollections — and produces analysis that’s directionally right but not defensible. Or the team goes on instinct because there isn’t time to do it properly.
The cost of that second outcome isn’t primarily the hours. It’s the moment in the IC discussion when someone says, “I think that’s fair value” — and no one in the room has the data to confirm or push back. That’s what Yu calls “the conviction gap,” and it’s quiet, which makes it hard to measure and easy to normalize.
The difference between walking into IC saying, “I think that’s fair value,” and walking in saying, “We are at the Xth percentile of comparable transactions, and here is the distribution,” isn’t a matter of confidence. It’s a matter of whether the benchmark exists inside the workflow at the moment the decision is being made.
The fundraising parallel
The conviction gap is just as present on the fundraising side and arguably more consequential in the current environment.
The fundraising market has tightened. GPs who would have differentiated on track record and headline IRR alone are finding that narrative insufficient. LPs want to understand the “how” underneath the performance — what returns were generated, which value creation drivers produced them, and how that compares to what similar funds achieved in similar conditions.
The question “Are we top quartile?” has gotten harder to answer credibly. Whether the performance is there is not the issue. The issue is whether “top quartile” means anything when the peer set is defined by an authority the LP trusts. A GP asserting top-quartile based on a self-constructed peer group is a different conversation from a GP whose positioning is grounded in MSCI data — the same data the LP is likely using to evaluate them.
When both parties in a fundraising conversation are working from the same evidentiary foundation, something fundamental changes. The conversation stops being adversarial and becomes analytical. It shifts from “convince me your numbers are good” to “let’s talk about what these numbers mean.” That posture — GP and LP as co-analysts of the same dataset — is a meaningfully different place to be. It builds trust faster, handles objections before they’re raised, and produces a first close dynamic that reflects genuine shared understanding rather than negotiation.
The data-workflow integration problem
Here’s where the data-driven aspiration most commonly breaks down: Data and workflow exist in separate places.
A firm might have access to excellent benchmark data, but if accessing it requires leaving the deal management system, running a manual export, and reassembling the output in a format the deal team can use, the benchmark is effectively not embedded in the decision. It’s available in theory. In practice, the associate preparing for IC on Thursday night is not going to add three steps to an already compressed workflow. The benchmark gets skipped or is approximated from memory.
This is not a discipline problem, it’s a design problem. Data that isn’t embedded in the workflow isn’t really data capital, it’s data inventory, and data inventory doesn’t close conviction gaps.
The principle that follows is simple. For benchmarks to change decisions, they have to exist inside the system where decisions happen, at the moment those decisions happen — not in a parallel tool that can be consulted, not in a quarterly report that gets read after the fact, but inside the deal record, on the fund overview, one click from where the team already is.
That’s what changes when MSCI’s institutional-grade private capital benchmarks live natively inside Intapp DealCloud. The deal team doesn’t need to go find the data — the data is on the record. The peer set is defined once and loads automatically. The value bridge, the multiples distribution, the fund-level IRR and TVPI in context — all of it surfaces where the work is already being done.
What institutional-grade actually means
Not all private markets data is built the same way, and the distinction matters for whether benchmarks hold up in an LP meeting.
The most important design choice is sourcing. MSCI’s private capital database is 100% LP sourced, drawn from the cash flows and commitments of the LPs who actually invested the capital. It doesn’t come from GP self-reporting, press releases, or scraped public filings. The practical effect is that survivorship bias is eliminated. Funds that didn’t work out are in the dataset. What you’re benchmarking against reflects the true investment experience, including the outcomes that tend to disappear from curated datasets.
Their sourcing model is also what makes the benchmark credible in the room. When a fundraising team sits across from a pension fund or endowment, the peer set is built from data those institutions and their peers contributed directly. The LP isn’t evaluating the GP against a benchmark the GP chose; they’re looking at the same data they use to evaluate the GP — and that shared foundation is what enables a genuine analytical conversation rather than a sales one.
The depth of the dataset — 50-plus years of private capital history, 28,000-plus funds, and nearly 600,000 underlying investments — also matters in a way that isn’t obvious at first. It’s what enables granularity below the fund level, down to the deal. And that’s the layer where most investment decisions actually get made — and where most other data providers stop.
The firms that will define the next decade
The private capital firms that compound all three forms of capital — financial, institutional, and data — aren’t just better equipped for the decisions they’re making today. They’re building a structural advantage that compounds over time.
Institutional capital scales because best practices get encoded in workflows, not held in the heads of senior partners. Data capital scales because every benchmark used in an IC discussion, every peer set constructed for a fundraising meeting, makes the next analysis faster and more defensible. Financial capital follows. It doesn’t do so because the others cause returns directly, but because they improve the quality and consistency of the decisions that generate them.
The firms still running these processes on instinct, manual exports, and conference-room memory aren’t necessarily making bad decisions. They’re just making decisions at a lower resolution than the market increasingly demands. LP sophistication is raising that standard faster than most firms are raising their data infrastructure to meet it.
Being data-driven isn’t a philosophy, it’s an operational condition. The data has to be there, in the right place, at the right moment, grounded in a source the room trusts.
To see how MSCI’s institutional-grade benchmarks work natively inside Intapp DealCloud — across deal screening, IC preparation, portfolio monitoring, and fundraising — Schedule a demo with the Intapp team.