In April 2026, The Wealth Mosaic published a new research paper in our WealthTech Insight Series – Order from disorder: moving from fragmented data to competitive advantage. 

Produced in partnership with specialist financial data provider and complex data management experts Raw Knowledge, the report explores data management and its newfound position at the top of the strategic agenda for wealth management firms. 

The paper found that: 

Data has become a strategic business priority 

Data management has moved from a back office concern to a board-level priority for wealth management firms. Increasing regulatory demands, growing client expectations, ongoing industry consolidation, and the rise of artificial intelligence (AI) are all forcing firms to confront longstanding weaknesses in their data environments. 

Good data is essential for growth, efficiency, and client outcomes 

High-quality data underpins virtually every aspect of a modern wealth management business. Firms are using improved data capabilities to enhance client reporting, strengthen regulatory compliance, improve management information, streamline operations, and support more personalised client engagement. Better data is increasingly viewed not just as an operational necessity, but as a driver of growth and competitive differentiation. 

Fragmented systems remain a major challenge 

Many firms continue to struggle with legacy technology, disconnected data sources, and manual processes. Data is often spread across multiple platforms, departments, custodians, and external providers, making it difficult to create a consistent and trusted view of the business. The challenge is further complicated by growing volumes of unstructured data, including information held in emails, PDFs, meeting notes, and client correspondence. 

The industry's goal is a ‘single source of truth’ 

Firms are actively working towards creating a unified and trusted data environment, typically through data lakes or data warehouses. While most organisations have established data strategies and transformation programmes, achieving a true ‘single source of truth’ remains a complex and resource-intensive undertaking that often takes several years to deliver. 

AI is raising the stakes 

The growing adoption of AI is accelerating investment in data management. Firms recognise that AI applications are only as effective as the quality of the underlying data. As a result, many are focusing first on improving data quality, governance, and accessibility before deploying more advanced AI use cases. 

Success depends as much on people as technology 

Data transformation is fundamentally a cultural challenge. Effective data strategies require organisation-wide buy-in, clear governance, defined ownership, and ongoing training. Data can no longer be viewed as solely the responsibility of technology or operations teams; rather, everyone across the business plays a role in ensuring data is accurate, consistent, and usable. 

Data excellence is becoming a competitive advantage 

Although good data is rapidly becoming a baseline requirement, firms that achieve excellence in data management will be best positioned to improve client outcomes, scale efficiently, respond to regulatory demands, and unlock the full potential of AI. In an increasingly competitive market, data quality is emerging as a key differentiator between firms that merely keep pace and those that gain a lasting advantage. 

Read on as we explore the paper’s findings in more detail 

Now for Mosaic II, in a new interview with Preya Patel, Managing Director at Raw Knowledge, we discuss: 

• Treating data as strategic infrastructure
• Creating trust through data lineage
• Managing growing volumes of unstructured data
• Avoiding common data strategy pitfalls
• Building strong foundations for AI
• Turning data into business value and growth

Read on below...

The report argues that data has moved from a back office issue to a board-level strategic priority. What has changed that is driving that shift?

A few things are coming together. There are increased regulatory expectations, with the Consumer Duty, for instance, and stronger requirements around auditability. Client expectations have increased as well – people want more personalised services, real-time information, greater transparency, and fast, efficient, and proactive service. AI is highlighting weaknesses in firms’ data foundations. If you’re relying on outputs from AI, and the data that's being captured is not clean and accurate, it's ‘garbage in, garbage out’.

At the board level, inefficient data management has now become a major cost. Firms are dealing with much larger and more complex volumes of data – often unstructured data – and it's spread across multiple systems providers. But if data management is seen as a business asset, it can drive growth and create competitive advantage.

Many firms in the report describe their data quality as “average to good”. In your experience, what separates those firms that genuinely have excellent data from those that only believe they do?

The strongest firms are the ones that have the consistent data quality – across not only structured data, but unstructured as well, from back office functions to front office. Ownership is also another big differentiator: in a lot of organisations, the responsibility for data just exists on paper – you've got the processes there, but they're not necessarily put in practice. The firms that put the ownership in practice have better accountability and stronger governance across the business.

Confidence is also a good indicator. Some firms only trust their data once a year, when they're running their reports and responding to audits. But the best firms trust their data every day – they know exactly where it's coming from and how it's managed. Firms with excellent data don't just see the benefits in efficiency or cost reduction. They're also using this data to make better decisions and spot revenue-driven opportunities – and therefore help the business move forward.

The concept of a “single source of truth” appears throughout the paper. Is that realistically achievable, or is it more of a guiding principle than an end state?

That's what so many firms are striving for now, that single source of truth. It’s an achievable goal, but I think it's more of a journey than a final destination. There's a lot involved in that journey, as you’re bringing many parts together – the single source of truth can be difficult when you're dealing with fragmented systems, complex integrations, lots of manual processing, and increasing amounts of structured and unstructured data. The reality is that data is constantly changing, so I don't think there'll ever be a perfect end state. But that definitely doesn't mean firms shouldn't aim for it.

I think the real value comes from continuously improving data, so it remains trusted and consistent across the organisation. With the right technology, a centralised approach, standardised processes, strong governance, clear ownership, all adopted across the business, the single source of truth becomes so much more achievable. Even if perfection may not be possible, firms can still gain significant benefits by working through towards those principles.

Interested in reading more about this topic? Mosaic II is available to read in full here.

In practical terms, how do firms actually solve fragmentation?

The mistake is thinking you must replace all your core systems – that's slow, risky and rarely necessary. The real work is building a proper governed data layer that sits beneath those systems and becomes the single source of truth. Data feeds are standardised, validated, and consolidated into a golden copy that supports reporting, advice, and client tools. That layer is a real investment, but it's the one that actually makes the difference.

What holds it all together is lineage. If every figure can be traced back to its source and the rules applied to it, your data becomes something you can stand behind, rather than just hope is right. My advice is to start where the pain is sharpest – usually pricing or market data – prove the value there, then extend it across the business. The firms that treat this as proper infrastructure, not a quick fix, are the ones that pull ahead. Get that foundation right and everything above it gets easier: your reporting lines up, the client experience stays consistent, and the AI tools everyone's racing to adopt finally have data worth trusting.

Several interviewees highlighted unstructured data as a major challenge. Why has this proved so difficult for wealth firms to solve, and what remedies can they constructively apply?

For many years unstructured data has been challenging. It still continues to be challenging. The problem is that unstructured data doesn't follow a fixed format that systems can easily interpret. Things like PDFs or meeting notes contain the most valuable information, and they're so much harder to search, analyse, and use consistently. A lot of that information is captured manually, which introduces inconsistency and the subjectivity as well.

One of the ways to tackle this is by using structured and standardised methods of capturing information wherever possible. At the same time, AI and large language models are becoming much better at understanding unstructured data and making it more accessible, especially when they're trained with the right business context. The firms that have seen the most success tend to start with specific use cases, prove the value, identify the workflows, and then build from there.

The report suggests that data projects often fail because they become technology initiatives rather than business initiatives. What are the most common mistakes firms make when trying to implement a data strategy, and how can they do things differently?

When they're treated as technology projects, the focus ends up being on implementing systems and tools, instead of delivering meaningful business outcomes. And when that happens, ownership can sit mainly within technology teams rather than being shared across the wider organisation. To be successful, something critical firms need is buy-in from the whole business – so people understand why the strategy matters and how it supports real business goals. People also need access to the data and the skills to use it effectively on a day-to-day basis.

Many firms are investing heavily in AI while simultaneously acknowledging weaknesses in their data foundations. How concerned should the industry be about the gap between AI ambition and data readiness?

It's something the industry should take seriously, because AI is only as good as the data behind it. Data is the foundation for AI: if the underlying data is incomplete, outdated, or inaccurate, AI won't produce those valuable, reliable results that you need. Neither can you trace AI outcomes to how it came to that decision. That can impact client outcomes, regulatory compliance, and business decision-making.

The encouraging part is that most firms are aware of the challenges, which is why many are taking a cautious approach – for instance, just starting with the transcription of notes and focusing on lower-risk internal use cases. But the bigger risk is that some firms see AI as a shortcut to creating value, but they're not sorting their data foundations out first – so they're moving ahead before they've built and understood the importance of the data and the foundations that are needed to support it. 

Where do you see the greatest untapped value still sitting within wealth management firms’ data estates?

This goes back to the unstructured data: there’s a huge amount of valuable information sitting in this manually gathered data – meeting notes, PDFs, emails – that’s often not really made use of. It contains a lot of important information about client goals, preferences, risk appetite, and extremely important events in their life as well. If the firm can capture and use that information effectively, they can provide more personalised services and identify growth opportunities.

Another major opportunity is breaking down the data silos. In many firms, the data sits across systems; you've got teams trying to use this data in-silo which means that insights aren't shared. That can lead to inconsistent reporting, weaker decision making, and an incomplete view of the client. Bringing that data together creates opportunities for better advice, stronger client relationships, and more effective cross-selling as well.

How have you been able to apply good data management in-house at Raw Knowledge?

Our Managed Smart Data Platform was built to solve our own problems before becoming a product. We power the tax and reporting data used by the UK's leading wealth and fund managers, so we face the same challenges our clients do.

Extraction comes first as a distinct step. Much of the data this industry needs sits inside fund manager documentation, each in its own format and layout. UK Excess Reportable Income is our hardest case: the same figure may appear "Excess Reportable Income" in one report and "Deemed Distribution" in another – key dates are buried in sentences rather than held in fields. Pulling those values out reliably, across hundreds of pages and dozens of share classes, is complex. We've gone from processing 50 documents a week to 350, and we're continuously improving. Today, 87 percent run fully automated, end-to-end, at full field-level accuracy.

The platform then validates the data before it enters downstream systems, identifying issues like invalid ISINs missing mandatory fields, outlier values, or inconsistent dates. Critical exceptions are surfaced for review rather than slipping through, with minor exceptions flagged alongside the data. Lineage underpins all of it. Every value traces back to its source, with a full audit trail, applied rules, and approvals. Impact analysis that once took weeks now takes about an hour.

For us, the effect is straightforward: client data turnaround down from seven and a half hours to thirty minutes, costs down by roughly half, and no more arguing over which number is right.

How have you seen it best applied across the wider industry?

Two use-cases come up repeatedly. The first is reference and security master data – identifiers and classifications that don't line up between systems. The platform holds a single golden instrument and counterparty master, which every system draws from so a whole category of reconciliation problems disappears.

The second is pricing, which clients raise most often – when vendors, custodians, and administrators provide conflicting valuations, the platform blends them on an agreed hierarchy, applies fallback logic where a price is missing, and produces a single validated golden price per instrument, ready for NAV.

Looking ahead five years, what do you think will distinguish the wealth firms that have successfully mastered data from those that have not?

The biggest difference will be how firms use their data. The leading firms will use it for more forward-looking activities – identifying opportunities, anticipating client needs – that make the business grow faster. Firms that fall behind will still mainly be using data for reporting compliance and looking backwards at what has already happened.

We'll also see significant differences in operational efficiency – the leaders will have highly automated and streamlined processes, with very little manual intervention allowing them to reduce their costs within the business and scale faster. The rest will remain dependent on manual processes, higher operating costs, and larger teams to support growth.

Interested in reading more about the news, insights, and trends shaping wealth management today? Mosaic II is available to read in full here.

Want to participate in Mosaic III?

Work on Mosaic III: Autumn 2026 edition is already underway. If you would like to feature in the next edition, you can discover the range of contribution options available here.

Or, if you would like to speak to us directly to explore what participation option works for you, email stephen@thewealthmosaic.com.

Discover Mosaic I

If you’ve enjoyed Mosaic II: Summer 2026 edition, don’t miss where the journey began. Mosaic I: Spring 2026 edition explores many of the themes that continue to shape today’s wealth management landscape – including the rise of private markets, the foundations of effective AI adoption, revenue management, client onboarding, and the evolution of digital advice.

Alongside exclusive executive interviews, contributor insights, company profiles, and technology showcases, Mosaic I offers wealth management professionals a curated, global view of the trends reshaping our industry. Read it today here.

Interested in discovering more? Read our reports!

• WealthTech 2026 – read here
• US RIA Toolkit 2026 – read here
• Future View Toolkit 2025 – read here
• UK Toolkit 2025 – read here
• AI Toolkit 2025 – read here
• Client Experience Toolkit 2024 – read here
• US WealthTech Landscape Report 2024 – read here

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About The Wealth Mosaic
The Wealth Mosaic is a UK-headquartered online solution provider directory and knowledge resource, focused specifically on the wealth management industry.

For wealth managers, the buy side of our marketplace, The Wealth Mosaic is designed to enable discovery of key solutions, solution providers and knowledge resources by specific business needs.

For solution providers and vendors, the sell side of our marketplace, The Wealth Mosaic exists to support the positioning, exposure and business development needs of these firms in a more complex and demanding market.