Investment consultants are asking for more data, more often, in more formats. For Asset Managers, the operational burden of responding to these requests has grown significantly over the past decade — and most firms are still handling it the same way they always have: manually.
The scale of the problem
A mid-sized Asset Manager with meaningful consultant coverage might receive dozens of data requests per quarter — each requiring data extraction from a Portfolio Management System, reformatting to match the consultant's template, quality checking, and delivery. Multiply that across a growing consultant universe and the operational cost becomes significant.
The problem is compounded by inconsistency. Different consultants use different templates, different data definitions, and different delivery mechanisms. What one consultant calls 'net return' another calculates differently. What one accepts via email another requires through a data portal. Every variation adds friction.
Why manual processes break down
Manual data handling introduces three categories of risk: accuracy risk (human error in extraction and reformatting), timeliness risk (delays when staff are stretched or absent), and consistency risk (different people producing slightly different outputs for the same request). Any one of these can damage a consultant relationship. All three together make it very difficult to scale.
What a scalable alternative looks like
The firms that handle consultant data requests well have typically invested in three things: a clean, reliable data layer that sits between their PMS and their distribution processes; a set of standardised output templates that can be populated automatically; and a delivery mechanism that removes the manual handoff.
None of this requires replacing existing systems. The most effective approaches we've seen work with whatever PMS a firm already uses, building the automation layer on top rather than underneath. The investment is in the plumbing, not the platform.
The result is a process where a consultant data request triggers an automated extraction, transformation, and delivery — with human review at the end rather than throughout. Staff time shifts from data handling to data quality oversight, which is a much better use of expertise.