Due Diligence

How to Test Due Diligence Platform Scalability: 5 Questions to Ask

Five questions to ask before selecting a due diligence platform, or to pressure-test the one you're already using.

Dasseti
Jun 2, 2026
Due Diligence

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Most due diligence platform evaluations test workflow and features. Few of them test the data layer underneath. That's a problem, because the data layer determines whether reporting, analytics, and integrations hold up as the program evolves, which we've written about separately as The Year 2 Problem.

These five questions help surface what's in the data layer. They're useful for teams evaluating a new platform, and for teams who want to pressure-test their current one.

1. Can I see everything I have done with a manager or fund in one place?

Specifically: is it a structured profile with normalized data, or just a list of questionnaires?

A profile-centric platform shows a single, consolidated view of every data point you have ever collected on a manager, across every questionnaire and every cycle. A questionnaire-centric platform shows a list of completed forms. The difference reveals whether the platform stores data in a way that can be queried at the manager level, or only at the questionnaire level.

2. Can I see everything I have done around a specific strategy or asset class?

The same architectural question, applied at the portfolio level. A profile-centric platform can roll data up across managers because the data lives in a stable schema. A questionnaire-centric platform can't, because the data lives in the forms.

This is the question that matters most for teams running strategy-level analysis, ESG reporting across the portfolio, or any cross-manager comparison.

3. What happens to last year's data if I change a question on my DDQ?

This is the most direct test of whether the platform has The Year 2 Problem. A platform with a stable data layer keeps the data intact and the analytics keep referencing the same field. A platform with a questionnaire-centric architecture will flag that historical reports may need updating, or ask for development time to remap.

The answer also tells you how the vendor will handle template evolution over time. If routine front-end changes require back-end work, that pattern will repeat every time the team's needs evolve.

4. Does the API reference questionnaire versions or a normalized profile?

The answer tells you how reliably the platform will connect to the rest of your stack. If the API pulls data using questionnaire and question IDs, every time your team updates a DDQ, those references change. The connections to your CRM, BI tool, or risk system have to be remapped each time. What was sold as straight-through processing turns into a recurring IT project.

If the API pulls from a normalized profile, the field references stay the same regardless of what changes inside a questionnaire. Connections built once keep working.

5. Can I run multi-year analytics without knowing which questionnaire version each data point came from?

The answer tells you whether the platform is built around questionnaires, or around a data layer that outlives them. Multi-year trend analysis is one of the most valuable outputs of a long-running due diligence program, and it should be straightforward to produce.

If the answer involves reconciling questionnaire versions, mapping historical IDs, or running anything in spreadsheets, the platform's data model is constraining one of the program's most strategic uses.

How the vendor answers matters as much as what they answer

A profile-centric vendor will answer these questions clearly and quickly, because the architecture makes the answers simple. If the vendor needs to hedge, qualify, or get back to you, that's also useful information. The architecture either supports these use cases out of the box or it doesn't, and how confidently the vendor can speak to them is a reasonable proxy for which side of that line they sit on.

Want to see how Dasseti COLLECT addresses these questions? Book a demo.

 

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