The financial services industry has spent decades perfecting data infrastructure for public markets. Clean, standardized feeds power everything from trading to risk management systems. So when private markets firms look to modernize their data capabilities, the natural instinct for many firms is to try and replicate what works in public markets. That approach is flawed.
The reason is complicated. The vast majority of private markets data is not structured data in the traditional sense. It's buried inside scanned and digital documents that cover the spectrum of file types we all use everyday.
Take a look into any private equity, real estate or infrastructure deal room or folder structure and you'll understand the problem pretty quickly. Virtual data rooms don't contain well organized rows and columns of financial and operating metrics. Instead, they present as a vast collection of PDF files, Word documents, PowerPoint presentations, Excel files, audio and video files formatted in ways only a human (for now) could creatively design.
In private credit, an agreement might span 300 pages, embedded with financial covenants, rate resets, different credit facilities, different borrowing entities, rating requirements, and capital table positioning details. That is just one of many documents associated with the deal which may also include amendments, fundamental research, historic financials and corporate org charts. Each document contains critical information but it's buried within unstructured text and graphics.
This is the fundamental operating reality of private markets. While public markets operate with standardized documents and well-understood structures, private markets are built on custom solutions. Structured data always augments the diligence process but that data often lives in Excel (and more than likely with plug-ins from market data providers). Private markets analysis and company / asset monitoring eventually results in structured data, but it almost always originates in long form documents.
This makes traditional data architecture sub-optimal. The tools that work well for public markets such as data warehouses, golden source systems, harmonized client and asset views, all assume that you are starting with structured data. Even then there is often an overlay that cleans and analyzes information that already exists in neatly organized databases to improve its quality.
Private markets operate differently. The valuable intelligence exists within documents first (and Excel and CSV files are the closest you get to structured data as inputs). Investment teams and their diligence advisors must comb through all of this unstructured information, extract, organize, and structure it, then run it through their proprietary models. It is a manual process with limited automation that is time-intensive and difficult to scale, especially when you are time constrained (leading to many late nights in the office).
Many firms attempt to solve this by trying to integrate private markets data into public markets frameworks. They focus on a useful but small subset of truly structured data that is available - such as Excel files with cash flows (or copy/paste from a PDF or PPTX) and performance metrics from third-party providers.
While this approach captures some value, it can miss rich sources of investment intelligence: the detailed operational, financial, and strategic information locked within company documents, expert network calls and third party research. That information drives critical investment decisions but often remains hard to access and reuse.
The solution isn't better document management (though it will help on the margins) or more sophisticated Excel processing (but Excel is here to stay!). It requires recognizing that private markets need a fundamentally different data architecture that is designed for document-centric workflows rather than database-centric ones.
This architecture must handle dynamic document structures, extract meaning from complex legal and commercial language, and maintain context across thousands of pages per deal. And, it must work with redacted information, varying document quality, different languages and inconsistent formatting while preserving the nuanced details that drive investment decisions.
A new chapter is unfolding in private markets, and the firms ready to adapt will define it. Too many still treat the challenge as one of scale, assuming that private markets data can be structured, standardized, and processed like public markets data. But that’s the wrong problem to solve.
The real challenge is architectural: building AI native systems that can ingest, interpret, and preserve the meaning of fragmented, unstructured, and proprietary content while enabling access in a way that enhances and doesn’t disrupt the way investment professionals work every day. The deeper challenge is not just about extracting information - it’s about maintaining context and nuance at scale, so the knowledge derived from the documents is value-add to the investment process.
Private Markets investing is complex, long term and requires a lot of conviction. Firms that embrace this complexity and new technology-first approach, rather than avoiding or oversimplifying it, will be the ones to unlock faster and better diligence. Done correctly, it should lead to clearer insights, and better investment outcomes.
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