The database referenced in this article, Dakota Marketplace, is the global private markets intelligence platform used by thousands of investment professionals to research LPs, GPs, and private companies. Built by fundraisers for fundraisers, Dakota Marketplace delivers complete, accurate, and daily-updated intelligence across every allocator channel — from family offices and RIAs to sovereign wealth funds and public pensions. Learn More | Book a Demo
A private fund database is a structured record of private investment fund performance and characteristics, built so that allocators, fundraisers, and research teams can compare managers on a like-for-like basis.
Dakota's private fund and performance data tracks more than 17,100 private funds across seven asset classes, with net IRR, TVPI, DPI, and RVPI captured at the fund level. That combination, deep coverage paired with standardized return metrics, is what separates a usable database from a pile of disconnected fund reports.
Most investment teams do not lack access to fund data. They lack access to fund data they can actually compare.
Performance sits in PDFs, pension disclosures, quarterly letters, and voluntary manager submissions, each formatted differently and rarely lining up on vintage, strategy, or geography.
A private fund database solves that problem by pulling fund performance into one place and organizing it around the dimensions investment professionals use to make decisions.
This article walks through what a private fund database contains, the asset classes it covers, how investment teams use it day to day, and how it powers accurate benchmarking inside Dakota Marketplace.
What a Private Fund Database Contains
At its core, a private fund database answers three questions about any given manager: how did the fund perform, how does that performance compare to peers, and is this a manager worth a conversation.
Dakota Marketplace is built around the metrics investment committees and LPs rely on:
- Net IRR, the primary return metric, net of fees
- TVPI, total value to paid-in, measuring realized plus unrealized value as a multiple of invested capital
- DPI, distributions to paid-in, showing cash actually returned to investors
- RVPI, residual value to paid-in, capturing unrealized NAV as a multiple of invested capital
For hedge funds and evergreen vehicles, Dakota also tracks annualized performance on a YTD, 1-year, 3-year, 5-year, 10-year, and since-inception basis. Every record carries vintage year, geography, strategy, and AUM dimensions, so comparisons stay meaningful rather than lumping a lower middle market buyout fund in with a large-cap growth vehicle.
The Asset Classes Covered
Coverage depth is where a private fund database proves its value. A dataset strong in private equity but thin everywhere else forces teams back into stitching sources together. Dakota concentrates on the private fund universe that matters most to allocators and fund managers, with performance records across:
- Private Equity: 4,600+ funds
- Private Real Estate: 4,200+ funds
- Hedge Funds: 2,900+ funds
- Private Credit: 2,100+ funds
- Infrastructure and Real Assets: 1,600+ funds
- Venture Capital: 1,000+ funds
- Evergreen and Interval Funds: 700+ funds
Within each asset class, the database breaks down by investment style, from lower middle market buyout to pre-seed venture, from core real estate to opportunistic, from corporate lending to asset-backed credit. That granularity is what lets an investment team build a true peer group instead of a rough approximation.
How Investment Teams Use Their Private Fund Database
The practical uses fall into a few patterns.
- Manager diligence. Before an allocation conversation, an investment team pulls a manager's track record and sets it against comparable funds of the same vintage and strategy. A top-quartile claim is easy to verify when the peer set is defined consistently.
- Benchmarking. Allocators and fund managers use the database to establish where a fund sits relative to its strategy peers, then carry that into investment committee memos and LP updates.
- Manager sourcing. Research teams filter by strategy, geography, and performance to surface managers worth tracking, rather than relying on inbound decks and word of mouth.
- Fundraising positioning. Fund managers use comparable performance data to understand how their own track record reads to an allocator, and to frame their story against the right peer set.
Dakota Marketplace was built by practitioners who spent decades raising and allocating capital, which is why it is organized around these workflows rather than around raw data exports. Where generic platforms bury performance inside broad datasets, Dakota pairs depth of coverage with the usability that fast-moving research and fundraising work demands.
See the database in action. Book a walkthrough of the Dakota Private Fund Performance Database and see how 17,100+ fund records support your next diligence or fundraising conversation. Book a demo.
From Database to Benchmark
A database is the foundation. What investment teams do with it is where the value compounds, and benchmarking is the clearest example. It also explains why credible private fund benchmarking databases depend on the depth of the fund data underneath them.
Broad quartile rankings compare a fund to every fund in its general strategy, regardless of what those funds actually invest in. A software-focused middle market buyout fund gets averaged in with an industrials-focused one, two very different return profiles, exit cycles, and multiple environments collapsed into a single misleading number.
The problem is not the data. It is the peer group.
Because Dakota Marketplace captures strategy, vintage, geography, and portfolio company sector on every record, our Benchmarks lets teams filter down to a true peer group and see accurate IRR, TVPI, DPI, and quartile rankings within it.
A fund sitting in the second quartile of a broad benchmark may be first quartile once the peer group only includes funds that actually invest in similar businesses. That is not spin. It is the correct comparison, and it only works when the underlying database is deep and structured enough to support it.
A Closer Look Inside Dakota Marketplace
The private fund performance data lives inside Dakota Marketplace, alongside the allocator intelligence and fundraising data that investment teams already use. That means performance records are not a standalone report you export and reconcile separately, they sit in the same workflow where you research managers, track allocators, and prepare for conversations. The features below are what a team actually works with day to day.
Access:
Investment style and strategy dimensions. Filter within each asset class by investment style, from lower middle market buyout to pre-seed venture, so every comparison stays meaningful rather than lumping unlike funds together.
Vintage year and geography filters. Every fund record carries vintage year and geography dimensions across all covered asset classes and strategies, so you can isolate the exact cohort you care about.
Core fund-level metrics. Net IRR, TVPI, DPI, and RVPI are tracked across every fund, the primary return metrics investment committees and LPs rely on.
Hedge fund and evergreen performance. For hedge funds and evergreen vehicles, Dakota tracks annualized performance on a YTD, 1-year, 3-year, 5-year, 10-year, and since-inception basis, reflecting how these structures are actually evaluated.
Layered together, these filters let a team move from the full 17,100-fund universe down to a precise, comparable peer set in a few clicks.
Custom benchmarking on top. Because every record carries strategy, vintage, geography, and portfolio company sector, Dakota Benchmarks builds directly on the database. Teams can filter to a true peer group and see IRR, TVPI, DPI, and quartile rankings within it, using transparent criteria that LPs and investment committees can examine and validate. Dakota is the only benchmarking product that adds portfolio company sector filtering to peer group construction, so a software-focused fund is compared to other software-focused funds, not to the entire broad strategy.
Ground Your Next Conversation in Real Data
Dakota’s private fund and performance data gives allocators, fundraisers, and research teams a single source of truth for evaluating manager track records: 17,100+ private funds, seven asset classes, and the core return metrics investment committees actually use.
Paired with our benchmarks and the rest of Dakota Marketplace, it turns fund performance from something you chase across sources into something you work with in one place.
Book a demo to see how the Dakota Private Fund Performance Database can ground your next allocation or fundraising conversation in credible, comparable data.
