Due Diligence
The Data Gap Holding Back Retail Access to Private Markets
How retail distribution is reshaping private markets data requirements, and why manual due diligence processes can't keep up.
Why Due Diligence Platform Scalability Depends on Data Structure
How due diligence platforms structure collected data has lasting implications for reporting, analytics, and integration as programs evolve.
Dasseti
Apr 21, 2026
The long-term capability of a due diligence platform depends on how it structures data at the point of collection. While workflow and features may shape the first-year experience, architecture determines everything that follows.
For allocator teams still coordinating due diligence across spreadsheets, email, and shared drives, the immediate appeal of a platform is workflow efficiency – centralizing responses, replacing manual tracking, and gaining visibility. But the question most teams don't think to ask is how the platform stores and structures the data it collects, and what that means for reporting, integration, and analytics over time.
Most platforms treat the questionnaire as the organizing structure for the data itself, whether it's a DDQ, RFP, or an ongoing monitoring request. Each response is tied to a specific template, question, and version of that question. In the first year, nothing about this design raises concern. Digitizing questionnaires is fast, dashboards populate cleanly, and the platform delivers a visible improvement over the manual processes it replaced. The problems don't surface until the program evolves, and by that point, the platform decision has already been made.
The limitations of storing due diligence data inside the questionnaire
When data is stored inside the questionnaire, the template becomes the database – and that works while the template stays fixed and the reporting scope stays narrow.
But due diligence programs don't stay fixed for long. Questions get refined, regulatory disclosures change, and ad-hoc questionnaires launch in response to emerging risks. When data is anchored to a specific questionnaire version, each change breaks something downstream – historical comparisons no longer align because the fields have shifted, dashboards need rebuilding to reflect the new structure, and integrations into BI tools or data warehouses require remapping to stay functional. Reports take longer to compile as teams trace numbers back through source data that's harder to follow, and none of these issues resolve themselves – they compound with every cycle, every questionnaire update, and every new reporting requirement, until the platform that was supposed to reduce manual work ends up generating it.
Why due diligence data quality depends on how it's collected
When managers can submit responses in whatever format they choose, analysts spend review cycles reconciling how something was reported before they can evaluate what was reported, and comparisons across managers require cleanup before they can be trusted.
Constraining how questionnaire answers are submitted, through structured fields, defined formats, and validation rules applied at collection, normalizes data from the outset. Analysts get apples-to-apples comparisons across managers and cycles without manual reconciliation, and aggregate analysis holds up at scale.
AI in due diligence is only as good as the data behind it
AI is accelerating across due diligence – with teams increasingly using it for summarization, extraction, prefilling, and anomaly detection – but AI operates on the data it receives. If that data is version-dependent or inconsistently structured, AI outputs inherit the same instability. Summaries, extractions, and analytics all become inconsistent when the data underneath them shifts between template versions and formats.
When the data foundation is solid, AI gives analysts a reliable starting point that human judgment and expertise can build on. When it isn't, AI becomes another layer of output that needs checking rather than a tool that gets sharper with every cycle of structured data behind it.
Why data is becoming an enterprise priority
These challenges are not unique to due diligence. Across asset management, data leaders are re-examining how systems store and structure information – because structure determines what's possible downstream. Pradeep Tekkey, Chief Data Officer at Harbor Capital Advisors, described this shift on The Allocation Agenda podcast: the move from treating data as an operational byproduct to treating it as strategic infrastructure that underpins reporting, integration, and decision-making across the firm.
Due diligence sits at the front end of that data chain. If the architecture is right, the data compounds in value across cycles, teams, and systems. If it isn't, every new requirement adds friction instead.
The distinction between a platform that digitizes a process and one that builds durable infrastructure matters more than most teams realize at the point of selection.