Wall Street is spending billions on private markets data companies:

Between them, they cover fund returns, LP portfolio analytics, deal data, allocator intelligence and the fund documents behind it all.

Private markets hold more than $25 trillion. Most fund-level performance data stays closely held, and no definitive list of individual fund returns exists. For most LPs, meaningful performance attribution remains out of reach.

Decades after public markets settled on their data standards, private markets still have no equivalent of the S&P 500 or the Bloomberg Terminal.

The incumbents are stretching their public market franchises to fill that gap. Moody’s has taken its ratings and risk data into private credit, and Bloomberg keeps widening its alternatives coverage.

Newer specialists are filling in where the data is thinnest, across valuations, credit documents, portfolio company data, secondaries pricing and more. Among them are 9fin, Allvue Systems, 73 Strings, Chronograph and SecondaryLink, just to name a few.

The race has a clear end goal: the trillions in individual investor capital waiting to enter private markets once that data infrastructure catches up.

Private markets data is one of the biggest stories we’re tracking, so we’re running a summit on it: The 2027 Private Markets Data Summit, next June in New York. More soon, and you can apply early now.

S&P Global has put With Intelligence’s private markets data inside S&P Capital IQ Pro, its flagship research platform.

Users can screen funds by strategy, size, terms and performance, see manager track records and pull investor allocations that allocators report directly.

Private markets data from a specialist tool now sits next to the public company data analysts already use.

S&P calls this an initial launch. Next on its list: investor allocation strategies, live mandates, professional contact data and wealth segment coverage.

Semi-liquid funds are pushing private credit NAVs toward monthly and daily, and regulators want marks that hold up to scrutiny.

Large data providers are moving into territory that independent valuation firms have long held. Keeping up requires a valuation stack that private credit is now building.

We’re convening Continuous, Not Quarterly: Building Private Credit’s Valuation Stack, a one-hour online session with Allvue to map it from the ground up.

We'll run through the layers: the transaction data and comps that feed a mark, the methods that shape it and the IPV process that validates it.

November 10, 12:30 PM ET. Online.

A single fund moves through separate systems, and teams reconcile its records by hand. iAltA says the cost is stale data, manual work and capital that moves late or sits idle.

Its answer is one shared data layer that every step works from. Between May and August, it launched four companies on that layer:

That shared layer is also the groundwork for AI, which can only work with the data it can access.

iAltA’s founding partners include Scott Ganeles, Bill Sherman and Laurence Tosi, part of the team behind Ipreo, Envestnet and iLevel.

Scott says modern technology and AI make this the moment to build that kind of infrastructure for private markets.

iAltA’s fourth founder Bill Crager, who co-founded Envestnet and now leads Field, takes the stage at The 2026 Private Markets AI Summit on November 4, and iAltA leads a breakout on private credit data.

LPs are concentrating capital with fewer managers, so fundraising teams need a current view of investors and their consultants.

Juniper Square and Nasdaq eVestment launched Capital Intelligence for that: data on 30,000+ institutional firms inside Juniper Square’s CRM, with AI matching it to a GP’s contacts.

It’s the latest of four AI additions to Juniper Square’s platform since April:

All four follow the same pattern: the AI comes to the system where a GP’s records already live, and the GP’s team stays in charge of what it does there. Drafts and flags go to a person, and outside agents get the same permissions as the team.

Brandon Rembe, Juniper Square’s Chief Solutions Officer, chairs a panel at The 2026 Private Markets AI Summit on what the firms making real AI progress have in common.

Private markets firms manage more potential investments, a wider range of fund structures and more information every year.

That pressure is landing on the operating model, and SS&C Intralinks frames the challenge as scaling up without losing speed, control or visibility.

SS&C Intralinks’ Olivier Mangez, who heads alternative investments in EMEA, says the strain shows up when deals pick up or a fundraise begins.

Both trace back to information: too much of it, and too few people who hold it.

Olivier’s answer is to let AI handle the volume. It reads and summarizes, so people can take it in and catch what doesn’t add up or what’s missing in a deal or fundraise. The call stays with them.

Sovereign Capital Partners is one GP that grew past the first sign. It started 25 years ago as a founder-run firm that relied on what its deal originators knew.

Today it tracks 16,000 companies, more than any originator could keep in their head. Managing Partner Dominic Dalli calls technology “a real enabler.”

SS&C Intralinks sees AI inside the operating model as how GPs keep pace as volumes grow. Its DealCentre AI covers the deal side and FundCentre AI the fundraise, making information easier to access and helping firms meet LPs’ transparency and diligence demands.

People call private credit opaque almost every day.

Inside the funds, the data piles up: every borrower reports, usually monthly, and the loan agreement makes none of it optional.

Andrew Riess, co-founder of Lumonic (now part of PitchBook), says his direct lending customers collect data “at a level that would shock you.”

The hard part is reading it. By Kevin Hsu’s count, 60% of the private credit funds Lumonic talks to still do that in spreadsheets, borrower by borrower.

Huge volumes of data with a right answer might make monitoring one of the best fits for AI in finance.

On the latest Modern Capital Podcast episode, Kevin and Andrew tell Marc why spreadsheets work until a fund reaches 20 to 40 borrowers, what separates the private markets CTO from IT and more.

Their advice to a fund starting today: get the data legible before reaching for AI.

Listen to the full episode on Spotify and Apple Podcasts.

A GP’s knowledge of its LPs has mostly lived in people: what an investor asked three years ago, what was promised, why they sat out the last fund.

A CRM holds whatever someone remembered to type. The rest leaves with the person who knew it.

Capital goes first to the managers an LP already knows. Those relationships run a decade or more, and the wealth channel is multiplying how many of them a firm has to hold.

Most sit with LPs in funds that already closed, between raises, when nobody is looking.

Altvia’s new Investor Engagement Manager is one attempt to give that memory its own infrastructure.

The firm says the AI system reads the GP data on its platform, flags what’s changing in a relationship and drafts the next step with its sources attached. A person approves it before anything happens.

How much of that memory firms will trust to a system is the open question. Showing the sources and leaving the decision with a person is a sensible place to start.