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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.
Behind that headline number is a deliberately structured dataset, one built so allocators, fundraisers, and research teams can find, compare, and act on fund performance without stitching sources together.
This article opens up what is actually inside: the asset classes covered, the depth within each, the metrics that make the data usable, and the benchmarking it powers.
What’s Inside
1. Coverage Across Seven Asset Classes
Breadth only matters if it comes with depth in each category. A database heavy in private equity but thin everywhere else pushes teams back toward spreadsheets and manager decks the moment they step outside that one asset class. Dakota concentrates on the private fund universe allocators and fund managers work in most, with fund-level 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
That spread means a research team evaluating a private credit manager and a real estate manager in the same afternoon is working from one consistent source for both, not two datasets with different formats and different definitions.
2. Depth Within Each Asset Class
The value of the database shows up below the asset-class level. Every fund is organized by investment style, so comparisons hold up under scrutiny. Private equity breaks down from lower middle market buyout to large-cap, venture from pre-seed through late stage, real estate from core to opportunistic, and private credit from corporate lending to asset-backed. Each record also carries vintage year, geography, strategy, and AUM dimensions.
That structure is what turns raw performance into a true peer group. A 2019-vintage lower middle market buyout fund gets compared to other 2019-vintage lower middle market buyout funds, not averaged against a large-cap vehicle with a completely different return profile. For anyone building diligence notes or an investment committee memo, that distinction is the difference between a defensible comparison and a misleading one.
See what is inside for yourself. Book a demo of Dakota Marketplace to see our private fund and performance data and explore the coverage, depth, and metrics across all 17,100+ funds.
3. The Metrics That Make It Usable
Coverage and structure set the stage. The metrics are what teams actually pull. Dakota's database is built around the return figures investment committees and LPs rely on:
- Net IRR, the primary return metric, net of fees
- TVPI, total value to paid-in, realized plus unrealized value as a multiple of invested capital
- DPI, distributions to paid-in, the cash actually returned to investors
- RVPI, residual value to paid-in, unrealized NAV as a multiple of invested capital
For hedge funds and evergreen vehicles, where a single IRR does not tell the story, Dakota tracks annualized performance on a YTD, 1-year, 3-year, 5-year, 10-year, and since-inception basis. Matching the metric to the structure is part of what keeps the data honest across very different fund types.
4. The Benchmarking It Powers
Depth, structure, and metrics compound when a team narrows the full universe down to a specific question, and benchmarking is where that pays off. Because every record carries strategy, vintage, geography, and portfolio company sector, Dakota Benchmarks builds directly on the database, letting teams construct a true peer group and see quartile-ranked performance within it.
The peer group is built across five filter layers stacked together: asset class, strategy, vintage year, geography, and, uniquely, the sector of the underlying portfolio companies. Dakota is the only benchmarking product that adds portfolio company sector filtering to peer group construction, so a software-focused buyout fund is measured against other software-focused buyout funds rather than an entire broad strategy. That single distinction can move a fund from second quartile in a broad benchmark to first quartile in its real peer group, not because performance changed, but because the comparison finally fits.
Within a custom peer group, Dakota Benchmarks reports the full set of metrics investment committees and LPs use, including IRR, TVPI, DPI, RVPI, and PME, each ranked by quartile. Because the filter criteria are transparent, both sides of the table can work from the same numbers. GPs and IR teams use benchmarks to present performance against a peer group LPs respect, while LP investment teams build their own peer groups to evaluate a manager's claims independently of the benchmark the manager selected.
Put Dakota Marketplace to Work
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 Dakota 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 Dakota’s private fund performance data can ground your next allocation or fundraising conversation in credible, comparable data.
