Key Takeaways
- • Dakota's guide compares the private credit performance databases most commonly used by institutional investors in 2026, grouping them by how each sources its numbers: pooled and anonymized fund-reported cohorts, loan indices derived from BDC SEC filings, or individual named-fund records.
- • Joe, Powered by Dakota, is positioned as the only option in the guide that reports Net IRR, TVPI, DPI, and RVPI for more than 18,000 named funds and vehicles, per the article.
- • Cliffwater's Direct Lending Index (CDLI) is built from SEC filings of eligible business development companies, covering roughly 21,000 directly originated U.S. middle-market loans, and measures direct lending as an asset class rather than individual funds.
- • Preqin, PitchBook, Burgiss, Cambridge Associates and KBRA DLD are each assessed for a distinct use case, from cohort benchmarking to deal-level covenant analysis.
Summary
A Dakota.Learn publication titled "Best Private Credit Fund Performance Databases: 2026 Guide" examines how private credit performance data is constructed and which databases are most used to evaluate it. The guide notes that many widely cited private credit figures come from BDC-derived loan indices, which serve as a proxy for direct lending performance but do not answer the question of how a specific credit fund performed.
The article classifies data sources into three approaches: pooled and anonymized fund-reported cohorts that publish medians or quartiles by strategy and vintage; loan indices built from underlying holdings in BDC SEC filings; and individual, named-fund records that show exactly which fund produced a given Net IRR, TVPI, DPI, or RVPI.
Joe, Powered by Dakota, is presented as a private markets intelligence platform for institutional investors, covering named-fund performance, custom peer benchmarking, manager track records, funds in market and transaction data. According to the article, every record is reviewed by Joe's research team and updated as new information becomes available. The guide also cites Preqin's cohort-based credit benchmarking, which Preqin's acquisition by BlackRock in 2024 for $3.2 billion (as stated in the article) brought alongside Aladdin and eFront. PitchBook's value is described as pairing fund data with its LCD leveraged loan franchise. Cliffwater's CDLI is described as the most-cited private credit benchmark, while Burgiss offers cash-flow-verified pooled benchmarks, Cambridge Associates provides a widely referenced index, and KBRA DLD focuses on deal-level covenant and structure data.
The guide closes with a decision table mapping common diligence questions to the appropriate source, and argues that private credit diligence often requires both an asset-class index and named-fund data.
The article classifies data sources into three approaches: pooled and anonymized fund-reported cohorts that publish medians or quartiles by strategy and vintage; loan indices built from underlying holdings in BDC SEC filings; and individual, named-fund records that show exactly which fund produced a given Net IRR, TVPI, DPI, or RVPI.
Joe, Powered by Dakota, is presented as a private markets intelligence platform for institutional investors, covering named-fund performance, custom peer benchmarking, manager track records, funds in market and transaction data. According to the article, every record is reviewed by Joe's research team and updated as new information becomes available. The guide also cites Preqin's cohort-based credit benchmarking, which Preqin's acquisition by BlackRock in 2024 for $3.2 billion (as stated in the article) brought alongside Aladdin and eFront. PitchBook's value is described as pairing fund data with its LCD leveraged loan franchise. Cliffwater's CDLI is described as the most-cited private credit benchmark, while Burgiss offers cash-flow-verified pooled benchmarks, Cambridge Associates provides a widely referenced index, and KBRA DLD focuses on deal-level covenant and structure data.
The guide closes with a decision table mapping common diligence questions to the appropriate source, and argues that private credit diligence often requires both an asset-class index and named-fund data.
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