Few wealth managers could currently claim their data supports decisive, auditable action. Fewer still would pretend otherwise.
FE fundinfo’s 2026 Asset Managers Report found that 65% of asset and wealth managers say fragmented data is preventing them from improving operational efficiency. At the same time, an even larger 69% see the speed and accuracy of their data becoming an increasingly important differentiator when it comes to winning and retaining clients.
Firms have identified the problem, as well as its consequences for business, but not yet figured out a successful solution.
Data used to be considered a deep back-office function sitting under an IT umbrella, but, in recent years, it has re-emerged under a bright corporate spotlight. Leadership teams in organisations can now be seen pushing data improvement initiatives from the top down.
This shift comes as no surprise in an age of AI, where data is being reinterpreted and recast as a vital business asset that drives efficiencies, unlocks competitive advantage and underpins growth. Today’s tighter margins and the demand for efficiency are quickly propelling data management up the agenda.
But building and maintaining what is often referred to as ‘a single source of truth’ is not the easiest task for firms when data is still bound in scattered silos. When colleagues in different roles are working within fragmented, legacy systems, the task of consolidating and standardising data feels like a herculean feat. Hard though it may be, completing this task is imperative for wealth firms of all sizes and complexions that want to grow and prosper in an increasingly competitive market.
Making data useful, not just pretty
In our endeavour to determine what good data quality looks like, we commissioned research to examine what a range of wealth managers are doing in this very area.
The findings, detailed in the resulting report ‘Order from disorder: Moving from fragmented data to competitive advantage’, confirm that while poor data quality is slowing AI adoption across wealth management, firms are still pushing ahead to chase promised efficiency gains.
While some are starting with low-risk applications such as meeting transcription, with plans to build on this when data quality improves, others have set out ambitious AI programmes – with one firm already deploying an AI operating system to support task automation.
Firms reported making progress towards establishing a unified, accessible data architecture, but this progress is by no means instant. Respondents anticipated it would take at least another 18-24 months for them to achieve their data goals.
Where firms are investing in robust data, tangible advantages were cited. For example, allowing practitioners to personalise client reporting and create actionable market intelligence for the oversight committee.
At the sharp end, good data quality equips client-facing employees with the ability to provide better service via real-time access to a client’s information. All the while, analytics will help to generate deeper insights into a client’s behaviour that can be used to support their growth strategy.
The regulator is reinforcing the same message. In its July 2026 review of Consumer Duty outcomes monitoring, the FCA found that firms relying on incomplete or repackaged data couldn’t demonstrate that they were delivering good client outcomes. The regulator expects firms to hold management information that is granular, ongoing and used to inform real decisions – not simply collected.
Knowing where to start
In an ideal world, every employee within a firm should feel empowered to engage with data management technology and have no hesitation or apprehension around it. But it can, at first glance, appear extremely difficult to consolidate and standardise data, particularly for small and mid-size firms. The DIY route to a data management solution is no simple matter. For firms wondering where to start, our report identified five common elements for achieving high-quality data:
- A clear data strategy: establishing a single, accessible repository for the firm’s data.
- Leveraging technology: eliminating siloed data through a single output multi-use model.
- Strong data governance: consistent standards for data capture and use.
- Buy-in from across the business: embedding a data-first culture across teams.
- Evidence to measure data value: demonstrate tangible outcomes from sustained data investment.
Better data pays off in two directions. Outwardly, it sharpens client relationships and the new business that follows from them. Inwardly, it takes cost out of the operation and makes scale realistic rather than aspirational. It then determines who gets to benefit from AI at all, because the productivity gains everyone is chasing will go to the firms that put their data in order first.
Written by Preya Patel, Managing Director of Raw Knowledge Ltd.
