Due Diligence
The Allocator’s Edge: Scaling ODD with Data and Digital Insight
How Operational Due Diligence Is Being Transformed by Data and Digital Tools: Insights from Jason Windt of M&G Investments.
Enhancing Manager Oversight: Allocator Tools for Smarter DDQ Review
Learn how allocators can streamline DDQ response management, reduce admin, and focus reviews using automation and structured workflows.
Dasseti
Jul 9, 2025
For allocators, the due diligence process doesn’t end when the DDQ is sent – it often intensifies. Chasing late responses, triaging hundreds of returned documents, and figuring out where to focus your time can be more taxing than writing the questionnaire itself.
In this post, we look at how allocator tools – especially those designed for real workflows, not just digital form-filling – can help you shift from admin-heavy processes to data-led decision-making.
Why Managing DDQ Deadlines Still Eats Time
Even the most thoughtfully designed DDQs can stall without clear timelines and structured workflows. Managers are often juggling multiple investor requests, each with its own deadline and internal review cycle.
As a result, allocators find themselves manually tracking responses, sending individual reminders, extending deadlines on the fly, and reviewing information out of sync with internal timelines - all of which leads to unnecessary follow-up and delayed decision-making.
Automating Oversight with Dasseti COLLECT
Dasseti COLLECT gives allocators the tools to manage deadlines and reminders at scale, without needing to chase every step.
- Set it once: Allocators can define response deadlines at the questionnaire level or per recipient
- Reminders on autopilot: Automated reminders can be scheduled before or after the deadline – no manual chasing required
- Grace periods or hard stops: Choose whether to allow late responses (useful for monitoring), or cut off on time (essential for RFPs)
AI-enhanced workflows also power progress tracking and risk alerts – helping you spot unusual responses automatically and prioritize where to focus.
These features don’t just improve workflows – they send a clear signal to managers about your expectations, without requiring constant back-and-forth.
Flagging, Scoring, and Focused Review
Once responses start coming in, the challenge shifts to review. Not every answer requires the same level of attention – and yet many allocators still review every DDQ linearly, from start to finish.
Dasseti COLLECT helps you prioritise with:
- Automated flagging: Highlight outliers or missing answers based on logic rules you define
- Custom scoring tools: Assign internal scores to key questions to guide investment committee discussions
- At-a-glance dashboards: See what’s complete, overdue, or requires escalation across your entire pipeline
The result is less time reviewing what you already know – and more time focusing on what actually changed.
Structured Workflows Make Data Review Smarter
Platforms that simply digitise the questionnaire don’t solve the review problem. If data is still locked in static documents, it can’t be sorted, benchmarked, or searched in any meaningful way.
Because Dasseti is data-first, every response is structured, searchable, and AI-enabled. You can:
- Search across questionnaires for a specific risk or KPI
- Benchmark responses over time or across peer groups
- Create exception views to triage your pipeline
It’s a more strategic way to manage oversight – especially when juggling multiple managers, strategies, and timelines.
Workflows That Scale Without Extra Admin
The goal of allocator tech isn’t to replace judgement, it’s to amplify it. By reducing admin burden, platforms like Dasseti COLLECT free up time for teams to focus on the high-value parts of due diligence: assessing risk, identifying gaps, and supporting investment decision-making.
With structured, automated workflows, your DDQ program doesn’t just run smoother – it scales.
Want more tips?
Download our free guide 'Designing Smarter DDQs', put together specifically for allocators looking to reduce admin, improve response quality, and make better use of data and AI.