Allocators increasingly screen and shortlist managers through the databases and platforms they already use, often well before a first meeting gets scheduled. A track record that lives only in a pitch deck or a one-off PDF doesn't show up in that process, no matter how strong the underlying returns are. Getting performance data into the platforms allocators actually use, and getting it in correctly, has become part of fundraising itself, not a separate administrative task sitting behind it.
Why It Matters
The industry has spent the last several years converging on what credible performance disclosure looks like: net-of-fee returns as the primary figure, clear disclosure of the fee assumptions behind them, and enough underlying detail for an allocator to verify a number rather than take it on faith. Frameworks like GIPS and the ILPA reporting templates exist because the industry needed common ground on this, not because any single allocator invented the requirement.
None of this means a manager with weak data gets rejected outright. More often, they get quietly deprioritized in the data-driven part of an allocator's process and never learn why. A profile with inconsistent numbers, an undisclosed fee basis, or stale data reads as a reason to move on to the next name, even when the underlying fund is performing well.
Your entry is doing the screening whether you maintain it or not. Allocators filter by asset class, vintage year, and net IRR percentile, and a fund only appears in those results if its record is complete and current. Dakota Private Markets holds performance for 18,000+ funds. Request Access →
Three Practices That Keep Performance Data Credible
1. Standardize methodology everywhere your numbers appear
The same fund's performance metrics shouldn't read differently across a PPM, a database profile, a due diligence questionnaire, and a quarterly LP letter. Inconsistency between venues reads as sloppiness at best and manipulation at worst.
2. Lead with net returns
A gross-only figure with no indication of the fee basis behind it undercuts a track record before an allocator gets past the summary. State plainly whether a net figure reflects actual fees or a model fee schedule, and note the fee terms it assumes.
3. Treat the entry as a living document, not a one-time submission
Performance data submitted once at fundraise launch and never revisited becomes the detail that quietly excludes a manager from a database-driven search months later, once the numbers no longer match where the fund actually stands. Refreshing the entry should be part of the regular reporting cycle, not a fundraising-only task.
Where the Entry Actually Gets Used
Dakota Private Markets' performance data tools track investment strategy performance with competitor benchmarking and fund investor portfolio analysis, so an accurate, current entry determines whether a manager shows up in the comparisons allocators are actually running.
Dakota has performance for 18,000+ funds and 159,000+ performance records across seven asset classes, and allocators rely on this data to evaluate managers by asset class, vintage year, and net IRR percentile. Every record is reviewed by Dakota's research team before publication rather than auto-populated from filings, which is the standard your own entry is being read against.
See How Your Track Record Reads
A strong fund with a thin data entry loses to an average fund with a complete one, every time an allocator runs a screen. Dakota Private Markets shows you the peer set your record sits inside: Net IRR, TVPI, DPI, and RVPI across 18,000+ funds, filterable by vintage year, asset class, sub-strategy, geography, and fund size.
Request access to Dakota Private Markets →
.jpg)
