Order from disorder: Moving from fragmented data to competitive advantage

RESEARCH PAPER

Order from disorder:

Moving from fragmented data to competitive advantage This paper explores data management and its newfound position at the top of the strategic agenda for wealth management firms

Contents

2

Executive summary

4

Introduction

6

How good data can transform wealth management operations

10

Reaching the single source of truth

14

Taking people on the data journey

18

Five essentials to achieve good data

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Conclusion

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About the research

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Order from disorder | Research paper

Executive summary This report explores data management and its newfound position at the top of the strategic agenda for wealth management firms, driven by the dual imperatives of enhancing client outcomes and meeting increasing regulatory and operational demands. Across the industry, firms are recognising that data is no longer just a back-office concern but a core business asset – one that underpins growth, efficiency, and competitive differentiation.

This report argues that data is not merely a technical function or a concern purely of operations departments.

Everybody in a wealth business interacts with data, and that makes data everybody’s business. Although senior leaders such as chief operating officers (COOs) or chief data officers (CDOs) typically retain overall accountability, there is growing consensus that data ownership must be embedded across an organisation.

Firms have high expectations for how improved data capabilities can transform both the client experience and internal operations. Good data enables more personalised, timely, and proactive client engagement.

It allows workflows to be streamlined and reduces reliance on the kinds of manual processes which inevitably introduce errors.

At the same time, advanced analytics are providing deeper insights into client behaviour, sales effectiveness, and business performance – supporting more informed decision-making and targeted growth strategies.

The precise contents of a data strategy will vary between firms according to their specific needs, implementation remains the most challenging aspect.

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The benefits of improved data also extend beyond the front office. Compliance and regulatory functions especially stand to gain from more accessible, consistent, and reliable data. Faster reporting, improved accuracy, and greater transparency are becoming increasingly important as regulatory scrutiny intensifies. This makes good data not only a driver of growth but also a critical enabler of risk management and regulatory compliance.

CLIENT EXPERIENCE TOOLKIT

Although many firms share the ambition of creating a single source of truth, fully realising it is still a challenge.

But the journey toward effective data management is not without challenges. Many firms are still operating across fragmented legacy systems, making it difficult to consolidate, standardise, and ultimately get true value from their data. The process of identifying, cleaning, and integrating data – particularly unstructured client information – remains complex and resource-intensive.

And although many firms share the ambition of creating a single source of truth, fully realising it is still a challenge.

Although the precise contents of a data strategy will vary between firms according to their specific needs, implementation remains the most challenging aspect. This is why people and organisational culture are critical to success. What might at first glance appear to be a primarily technical challenge becomes just as much a human-centred challenge.

Securing organisation-wide buy-in, investing in training, and fostering a data-first mindset are all essential to embed the new ways of working that allow good data to flourish.

www.thewealthmosaic.com

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This report argues that establishing a robust data strategy – supported by appropriate governance, technology investment, and cultural alignment – is essential for all wealth firms, regardless of size.

As the industry continues to evolve, the ability to harness high-quality, well-structured data will be a defining factor in determining which firms can successfully leverage emerging technologies, including artificial intelligence (AI), and deliver sustained value to clients.

The transition from fragmented manual processes to integrated and data-driven operations represents a significant opportunity. Firms that can effectively capture, manage, and utilise their data will not only improve efficiency and productivity but also strengthen client trust and unlock new avenues for growth.

Order from disorder | Research paper

Introduction Data is finally having its day. Across the wealth industry, the challenge of building a ‘single source of truth’ for data is rising urgently to the top of firms’ agendas.

For many years, the data problem within wealth management has been lurking in the background. It was a growing issue that everyone knew existed, but which firms regarded as secondary, painful to deal with, and a headache to resolve. They recognised that their data environments were fragmented, inconsistent, and poorly governed. But addressing the problem was complex, operationally difficult, and resource-intensive – and there were always more pressing items in the intray, more business items demanding attention.

The resistance went well beyond the data clean-up itself. It reflected a broader spectrum of underlying issues within many wealth firms: legacy technology stacks, limited appetite for operational change, and the growing scale and complexity of the data used across wealth management businesses.

However, there is now an accelerating cultural acceptance that good data – by which we mean data that is accurate, accessible, consistent, timely, and wellgoverned – is a business-wide issue, not just the remit of the IT or operations departments.

Today, good data is critical for business growth.

Good data underpins the modern wealth management business. It is essential for: • • • • • • •

Client reporting Regulatory responses Portfolio performance Executive management information Risk management AI and analytics Client experience and service

Amid the industry’s drive for growth and scale, increased regulatory obligations, and an unrelenting M&A environment, the need for high-quality data is stronger than ever.

The rise and adoption of artificial intelligence across the wealth industry is also a high-profile driver of the need for better data. AI’s effectiveness depends entirely on the quality of the data beneath it. Its prominence and sheer potential are now serving to focus minds on the need for accurate, timely, and trustworthy data – without which AI is a non-starter, let alone capable of serving broader business needs.

Collected, managed, and used well, good data can yield significant results for wealth firms in driving improved efficiency, productivity, business management, regulatory compliance, and – most importantly – client service. However, it is complex, multi-faceted, and heavily reliant on the buy-in of the whole business for successful outcomes. It is an ongoing part of day-today business – or should be.

Good data – by which we mean data that is accurate, accessible, consistent, timely, and well-governed – is a business-wide issue, not just the remit of the IT or operations departments.

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Why data management is everyone’s responsibility Although there have long been individuals pushing for greater focus on the data problem, today there is a much broader and stronger recognition across the wealth management industry of the importance and commercial necessity of good data management.

That includes recognising that persistent challenges within the industry – such as poor productivity and slow response times to reporting – can trace their roots back to poor-quality or poorly organised data, sitting in silos across the business.

Good data is also at the foundation of how firms respond to many of the other challenges facing modern wealth management: • • • •

Clients who demand a more personalised and tailored approach to their relationships The budgeting and performance choices facing all firms The ramp-up in M&A activity that requires accurate representation of a business’ state of health Ever-increasing regulatory requirements that demand evidence for compliance

Wealth businesses are now crafting data strategies designed to respond to these challenges. These are not just the remit of the operations department, but go company-wide, as data moves to the forefront of business strategy. These data strategies often go handin-hand with technology implementation or upgrades.

Executive attention is also turning to data quality as executives realise its importance to the AI projects in which they have invested high hopes and, often, extensive resources. Operations teams are implementing data governance regimes to ensure existing data is cleaned, and new data is being captured in a more timely, disciplined and accurate manner.

Executives are implementing new processes and workflows, alongside training and education, to take everyone in the business on the journey towards good data. Attention is being given to the outputs required from the data, and the ease of access to support reporting across all departments. Good data, and its effective management, supported by buy-in across the business, have the potential to be transformative for firms and clients alike.

Everyone within a wealth business interacts with or uses data in some form or another. That makes it everyone’s responsibility to ensure that a data strategy succeeds.

Order from disorder | Research paper

Our research For this paper, we conducted a series of qualitative interviews over the course of Q1 2026 with executives at largely mid-tier firms in the UK wealth community.

All the firms we spoke with are actively working to raise the standards of their data and are implementing technology to achieve this.

The firms we spoke with are at different stages of that journey. Some already have a data strategy in place. Others have major data projects in play. All are moving away from siloed data and are working towards an integrated single source of truth, typically through a data lake or warehouse. The objective at front of mind for these firms is achieving not just good, but excellent data.

The challenges that firms face in implementing a successful data strategy are broadly recognised across the industry: • • • • •

Legacy systems, holding scattered or siloed data across different departments which don’t speak to each other.

Inadequate maturity for data governance.

Lack of business buy-in for data initiatives.

Weak ongoing management, leading to inconsistent standards.

Workflows and processes that require manual interventions to check data and correct errors.

These problems need to be rectified quickly. Clients are increasingly demanding in their desire for personalised advice, timely outputs, and frictionless access to their investments and performance reporting.

However, beyond the high-profile, high-level challenges outlined above, firms are also grappling with more granular issues. These are often similar across firms, and it is these issues that we have surfaced through our interview process with several wealth businesses, including private banks, wealth managers, and advice businesses.

In the pages that follow, we’ll be exploring these challenges, and discovering how firms are resolving them.

Even though wealth leaders take different approaches to transforming their data management depending on their business models, all are aiming for the goal of a single source of truth for their data.

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Firms typically set a timeline of between 18 months and two years to achieve their data goals

Why now

Our research found four key themes that have characterised firms’ data journeys. Firms are:

The drive towards good data and robust data management has accelerated in recent years and, at most firms, is enjoying greater prominence than ever.

Reasons why include:

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Driving business growth and enhanced client relationships through data: high-quality data enables more personalised client engagement, better-informed decision-making, and improved outcomes – positioning firms to drive revenue growth and strengthen competitive differentiation.

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Improving efficiency to reduce cost and enable scale: the ability to reduce cost and enable scale through reduced manual intervention and better processes creates significant opportunities to lower costs and support scalable operations.

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Bridging the gap between data strategy and execution: creating a data strategy is one thing – implementing it to achieve that single ‘source of truth’ is where the really hard work takes place.

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Embedding a data-driven culture across the organisation: data strategies will fail if they don’t bring people with them. People from the board down to individual relationship managers (RMs) need to buy into the data journey, and data champions within organisations need to clearly illustrate their benefits.

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The proliferation of fragmented data across custodians, platforms, legacy systems, and clientprovided sources has made it increasingly difficult to maintain consistency, accuracy, and a single source of truth.

Rising regulatory expectations, and the need for robust governance, auditability, and risk control, mean firms must embed data discipline directly into their workflows rather than treat it as an afterthought.

Growing client demand for transparency, personalisation, and real-time insight requires firms to transform raw data into clear, contextualised information that supports better decisions and stronger adviser-client relationships.

The surge in unstructured data volumes, particularly of client data contained in emails, PDFs, or client correspondence, makes it difficult to capture in historic systems.

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The deployment of AI and analytics tools is exposing data weaknesses in legacy data foundations.

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Specific pressure points that arise in day-to-day business, such as regulatory deadlines and annual reviews, also expose weaknesses.

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Ongoing M&A activity quickly highlights the need for good data to produce an accurate picture of business health.

A 2026 Semarchy global survey found that 51 percent of organisations pursue AI initiatives without Master Data Management (MDM), and 38 percent have no data quality framework, leading to significant project delays.

Executives now understand that advanced analytics are only as good as the data that feeds them.

Even though wealth leaders take different approaches to transforming their data management depending on their business models, all are aiming for the goal of a single source of truth for their data. Some are working in-house to get to their desired state, while others are using a combination of internal and external resourcing.

Firms typically set a timeline of between 18 months and two years to achieve their data goals – recognising that not everything can be done all at once and that complexities are inevitable, not least in getting the supporting technology in place. Depending on the maturity of their data strategy or project progress, the goal of establishing a single source of truth for their data may be some distance away but, crucially, change is under way.

Our research examines this progress – revealing what is forefront of senior stakeholders’ minds right now as they attempt to achieve that single source of truth and how they are overcoming the bumps along the road.

Order from disorder | Research paper

How good data can transform wealth management operations Driving better efficiency through operations and technology Data organisation is a challenge for multiple different industries, not just wealth. According to Atlan’s 2026 Data Consolidation Challenges report, 87 percent of organisations face disconnected data sources that create risks in quality, consistency, and compliance.

Poor data leads to loss of time, increased cost, and lower productivity. Different departments will be affected in different ways across varying business models, but wealth management’s continued reliance on manual processes for client onboarding, payments, and reporting creates space for error. When mistakes occur, firms must spend additional time identifying, correcting, and reprocessing data, compounding operational strain.

“It’s going to be the front office that drives this. Part of the data strategy is to centralise client onboarding so that you have a centralised capture of client information and the customer experience is improved.” – COO, mid-tier bank Fragmented and inconsistent data – from both internal and external sources – intensifies these challenges. Wealth firms typically receive data from multiple external providers, all reporting in their own format with little consistency across structure. Operations teams or systems owners are typically those left with the daunting task of translating and reconciling these feeds – a time-consuming task that is never finished.

“Manual input, and workflows where there are too many human interventions, can lead to inaccuracies in numbers.

Several workflows are not as automated as we would like.”

– COO, mid-tier bank At the same time, internal silos – often created by different teams with different practices, including individual RMs and advisers with their own processes that they defend fiercely – can result in incomplete or inconsistent data capture, or data not being captured at all.

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But firms are starting to see the benefits of establishing good-quality, trustworthy data that can be accessed as required. Firms we spoke to echo a common understanding that centralising key processes, such as client onboarding, can significantly improve both data quality and the client experience.

“AI can really start to support us to speed up the time it takes. Then you’ll have the dilemma: ‘If we’ve got more time, do we sample more clients or do we save more money on headcount?’. It’ll be a nice problem to have when we come to that decision.” – COO, mid-tier wealth manager In particular, as firms seek higher growth rates, they are recognising that without consistency in data practices, they cannot scale their businesses successfully. Where advisers and RMs operate in silos or diverge from standardised processes, data integrity suffers – ultimately limiting a firm’s ability to expand efficiently. Firms that invest in structured processes and good data are seeing tangible benefits.

“The new process has improved the way that we work, it’s added consistency and in turn given us better data so that we’re able to evidence the advice outcomes much more easily.” – COO, mid-tier advice firm Beyond operational efficiency, good data underpins higher-quality management information (MI) and confident strategic decision-making. Many firms still struggle to produce timely, accurate internal reporting, making it difficult to assess performance or build a case for investment in the business. Good data enables firms to understand the key metrics, such as cost-to-serve and revenue by client, that are essential for firms to target the appropriate investment at the right areas and drive differentiation.

“It’s all very well winning a client – but what fees are we getting? It’s not just about the AUM but the revenue we’re earning from it and understanding the resulting cost to service this.” – COO, mid-tier wealth manager

Order from disorder | Research paper

The increasing complexity of data Even as firms seek to rationalise their technology and service-provider ecosystems, operational complexity continues to grow. Each new custodian, platform, or data vendor brings distinct data structures, reporting standards, and integration requirements. This challenge is magnified as firms expand into private markets and other specialised asset classes where data is far less standardised.

In private equity, for example, information is often delivered as PDFs, spreadsheets, or bespoke fund reports rather than through automated feeds, creating a heavy reliance on manual processes. The influx of both structured and unstructured data from these specialist sources increases reconciliation work and the potential for inconsistency.

Without a unified data model, strong governance, and automation to connect these sources, each new provider relationship adds friction, cost, and risk – undermining the scalability firms are seeking.

“New data providers provide more value and insight – once you get over the hump of consuming the data and understanding what it is. It’s the short-term pain for longterm gain.” – Ex-COO, large wealth manager With such a vast amount of data handled by wealth businesses, AI presents a huge opportunity to transform how data and its outputs are managed, analysed, and used day-to-day. But firms are proceeding with caution – many recognising that their current data foundations are not yet robust enough to use AI in the way they would like. There are also real concerns around security and privacy that are paramount for the protection of client data and maintaining the high level of trust implicit within the industry that clients so value.

Many firms we spoke with are starting slow, with low-risk internal applications for simple and contained tasks such as meeting transcription, using tools such as Microsoft Copilot. Over time, as data quality improves, the goal is to be able to use it in a more sophisticated manner.

Some are more ambitious and have more in-depth AI programmes in development within the wealth business itself. Others are developing a programme through the banking side of their business, or at the larger group level, but it is anticipated that these will eventually be utilised by their wealth businesses. One firm interviewed is starting to use an AI operating system to support the automation of tasks such as client fact-finds for onboarding, that will save significant time and effort once completed.

But for all, good data is essential to the success of their endeavours.

“I do think it’s about getting the data in good shape to now support some of these AI tools that we’re starting to bolt on.” – COO, mid-tier advice firm

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Enhancing client service and driving growth Firms we interviewed have great expectations for their data strategies – not just for improving client interactions and outcomes, but also for improving service and overall business behaviours.

At the front line, better data is already beginning to reshape how relationship managers engage with clients.

For instance, the simple task of auto-transcribing meetings means that the RM is more engaged during the actual conversation with clients, rather than trying to take notes.

Good data enables those notes to be turned into actionable insights that can be quickly implemented. Reports can be returned to clients swiftly in response to one-off requests.

This shift is supported by the move toward digitised workflows, replacing manual, document-heavy processes with integrated systems that capture and update client information in real time.

“So instead of having a very manual word document generated two weeks beforehand, then someone sends that out or phones the person up and manually updates the word doc, we have a single digital fact-find that gets filled in and updates directly through to our back office, and that’s then done.” – COO, mid-tier advice firm One firm interviewed for this report is using better data to analyse its pitch wins against client demographics – identifying where it has seen success depending on a client’s age, occupation, or risk profile. Firms are also using their improved data capacity to better track sales pipelines to better understand the journey to becoming a client, and where the pitch falters. This marks a broader shift in mindset – from viewing data as static reporting to using it dynamically to inform decision-making.

Client engagement is also being transformed through improved data capabilities, transforming the role of client-facing portals and apps. Two firms we spoke with are tracking how their clients use portals and apps, analysing the data those client-facing features generate to better understand usage patterns, tailor services, and drive engagement.

Another firm said providing both clients and advisers with the same real-time view of portfolio information on its portal allows immediate, informed conversations and quicker implementation of client requests. Other firms are using the data they collect through their portal and app interactions to develop and launch marketing campaigns more readily.

As data becomes more integrated and accessible, firms are also using it to anticipate client needs and deepen relationships and trust. Some firms are seeking to identify key moments of generational wealth transfer; others are surfacing insight into their clients’ philanthropic interests – an area that is often overlooked, but which a wealth management firm that uses its data well can respond to. By moving beyond assumptions and leveraging datadriven insight, firms can better align their services with what their clients value and differentiate themselves from increasingly tight competition.

At present, working across multiple systems increases the time spent in identifying and reconciling the data that compliance teams need. More unified data environments not only reduce this burden but also support consistency in reporting over time – an attribute that is only going to grow in value as regulators increasingly expect firms to reproduce and validate historical data. It can help firms take a proactive approach to regulation, too – one firm suggested that it could use data to analyse changes in regulations and anticipate future requirements.

Firms are shifting from fragmented, retrospective data usage to integrated, real-time intelligence that underpins both client service and business performance. By unifying data and embedding it across workflows, wealth managers can enhance engagement, sharpen decisionmaking, and improve operational and regulatory outcomes. Those that succeed will not only respond more effectively to client needs but also proactively identify opportunities for growth, differentiation, and long-term value creation.

Another impact on the client experience is through saving clients time and effort. One COO wants to see a joinedup picture of the distribution of products and services so clients can avoid providing the same information each time they want to make use of a different service. By consolidating data into a single hub, the firm can capture data just once.

“We are looking to improved data to enable us to change our behaviours in terms of what the client actually needs, rather than our supposition of what the client wants.” – COO, mid-tier wealth manager In all these examples, if the data is held in one repository in a uniform format, the firm can apply a much greater degree of analytics and give greater focus to the areas needing attention.

The benefits of good data extend well beyond the front office. Firms emphasised that regulatory and compliance teams stand to gain significantly from more efficient and reliable data management. High-quality data enables accurate, timely regulatory reporting and faster responses to supervisory requests.

For instance, for firms under the supervision of the UK’s Financial Conduct Authority (FCA), it is also central to meeting Consumer Duty obligations, as well as other requirements such as Sustainability Disclosure Requirements (SDR) – both of which require firms to evidence good client outcomes and substantiate sustainability claims with clear, verifiable data.

Order from disorder | Research paper

Reaching the single source of truth Across the industry, firms are actively advancing toward unified data architectures. Of those firms we interviewed for this research, all have a firm-wide data strategy or project in place. For those firms tackling integration of acquired businesses, the process is more complex – but also more necessary – as they consolidate disparate data sets from legacy platforms into a cohesive framework.

An additional complexity for several firms which are part of a merger or an acquisition is the consolidation of legacy data from the acquired business onto the buying firm’s platform. Although their own data may be in good order, acquisition activity can often degrade data quality, resulting in significant remediation efforts that impact cost, risk, and value creation.

But the direction of travel is clear: leadership teams are mandating data initiatives from the top down, embedding them across wealth management organisations.

“We’ve had a data strategy in place for the last two years – it is board-mandated and feeds down through the ExCo and into the business.” – COO mid-tier bank

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At the core of these strategies is the ambition to establish a ‘single source of truth’. Typically, firms achieve this through a data lake or warehouse. Firms interviewed for this paper have reported a range of these, from the high-performance data lake Snowflake, through to data platforms built inhouse utilising software such as Microsoft Power BI. All firms we spoke to rated their current quality of data in the ‘average-to-good’ range, but with internal nuances for differing data sets. For instance, they generally rated structured data – such as investment and regulatory data – as more reliable due to standardised formats and clear workflows and processes. However, collecting unstructured or more qualitative data – including that gathered directly from clients via emails or meetings, or through PDF reports – presents a greater challenge, and remains far more inconsistent and difficult to manage.

“What I would refer to as secondary data – where we talk to the client and data goes into the CRM tool – it is much more mixed [in quality].” – CTO, mid-tier advice firm

Correctly capturing data This inconsistency is worse where firms rely on multiple smaller platforms: in these cases, firms often find it harder to obtain the data they require to report in a standardised format. Such data is often manually communicated via email and needs to be recorded manually once received.

Firms increasingly recognise that addressing this challenge requires not only better technology, but also more disciplined processes around standardising data capture and reducing reliance on free-form inputs.

Everyone uses data. That means it is everyone’s responsibility to ensure it is accurate, timely, and used effectively to support the business.

“I think it’s coming up with a set of business processes that allow you to capture all the relevant data points that you need without creating huge amounts of manual work.

Changing processes and workflows to ensure data is captured correctly, using uniform fields rather than freeform text, when dealing with unstructured data, is a must, to ensure consistency.” – COO, mid-tier professional services firm Building a centralised data repository is only part of the solution. Equally important is ensuring that data can be effectively distributed back out across the business.

Chief operating officers (COOs) we interviewed said they wanted greater organisation-wide awareness, not only of what data is being captured but also why it is needed.

Here, the front office plays a critical role as the primary source of client data. A data strategy requires appropriate controls and efficient workflows in the front office, allied with an understanding among RMs and advisers of the centrality of their role.

At present, much of the data captured by the front office tends to be manually recorded and qualitative in nature; client preferences are often not easily automated. The challenge is in collecting client data in an efficient manner that does not create a drain on time from RMs or advisers.

It needs to be correctly labelled within the data warehouse and accessible for use through outputs – for instance reporting back to clients.

“A lot of data is input by RMs – we need them to understand the strategy around storage and data lakes.” – CTO, midtier advice firm Inevitably, firms will need to conduct remedial work on their existing data, which is a key pain-point not only for the front office but for the back office also. A significant degree of business buy-in is essential for success since so much data is captured through RMs or advisers. A data strategy must be endorsed from the top of the business and supported throughout the organisation; if RMs or advisers are expected to take remedial action they, in turn, need hands-on support.

Everyone uses data. That means it is everyone’s responsibility to ensure it is accurate, timely, and used effectively to support the business through its use and outputs. This is of course easier when there are systems in place and efficient workflow processes to follow.

“Many individuals want to use data but there is a lack of awareness of the role everyone plays in making data available and organised.” – COO, mid-tier wealth manager Order from disorder | Research paper

Implementing a data strategy

Establishing new work processes

Devising the data strategy itself can be relatively straightforward, and its precise contents will be defined by an individual firm’s business needs, often translated into a strategy through work with a specialised data management consultant or third-party provider.

In practice, many firms initially focus on organising existing data to be captured and ensuring it is captured consistently and stored appropriately. Although data strategies are typically defined at an enterprise level, early efforts often concentrate on establishing control over structured data – much of which resides within back office functions.

The hard part is the implementation. An effective data strategy will require significant remediation work on legacy data and investment in systems. Firms must prioritise both.

“A challenge in implementing the data strategy is resource allocation and battling for time.” – COO, mid-tier wealth manager Resourcing is a real problem: the smaller the firm, the bigger the issue. Firms that are merging or integrating businesses may find themselves integrating a data strategy alongside their broader transformation programme, which means upgrades or replacement to the underlying technology itself, further stretching resources. The task of ensuring the incoming data from the acquired firm is aligned to the same standards as the acquiring firm is enormous – often requiring significant expenditure on data remediation.

“Resource is always a challenge in terms of just getting people focused on it. It’s not always the sexiest or most interesting of topics for people to engage with.” – COO, mid-tier wealth manager At the same time, firms need to ensure their data improvements work alongside new technology to ensure that the correct workflows and processes are established.

In the short term, this can slow the data element, while the technology is developed and implemented. That often stretches internal capacity, making prioritisation essential.

Not all firms can afford external support in the shape of consultants and technology advisers, and if the day-to-day business of the wealth firm is to remain the priority, finding individuals internally to come off-line to staff these projects is a challenge.

“Given the legacy business, we have a lot of unstructured data that we’ve effectively never captured. It all comes back to consistency and standardisation.” – COO, mid-tier wealth manager

At the same time, there is a more immediate emphasis on front office data, particularly client information held within CRM systems. Much of this is incomplete, and often recorded in an unstructured format, making it difficult to access and analyse. Effective data strategies are focused on gathering client data through better processes and workflows, and ensuring a single point of capture to avoid duplications and inconsistencies. As a result, effective data strategies must bridge front and back office, enabling a unified, end-to-end view of data across the organisation.

“We decided on the data standards we wanted, said that for new clients you have to fit into these standards, and then did backward-looking fixes to existing data – largely done by the guys in the back office so we didn’t bother the investment managers too much.” – Ex-COO, large wealth manager The front office, as both the primary collector of client information and the driver of revenue growth, stands to gain significantly from improved data quality – enabling greater personalisation, faster onboarding, and a more seamless client experience.

However, it is also the area most likely to be disrupted by the remediation required to cleanse and standardise existing data. Onboarding remains the critical focal point, where automation can rapidly improve accuracy and ensure cleaner data flows into downstream systems. By strengthening the quality and timeliness of data captured at this stage, firms are better positioned to understand client needs, respond more effectively, and build deeper, more trusted relationships.

By improving the data quality captured through the front office, firms can better understand their clients, respond to their needs, and track the pipeline for further growth. By reducing the time taken to gather data, and by conducting more timely analysis ahead of making appropriate recommendations, firms can achieve a deeper relationship with their clients and greater levels of loyalty and trust.

Over time, however, the middle and back offices often realise the most sustained benefits, through enhanced reporting, stronger risk management, and improved operational efficiency – all of which are built on these more robust data foundations.

“Being great at data may only be good enough to keep up – we will need to be excellent to grow, and it will be a competitive advantage to excel at data.” – COO, mid-tier bank Technology build or integration goes hand-in-hand with any data strategy. Longer term, the focus for all firms should be the drive towards a single repository for all data, that is easily accessible to all those that need it.

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Governance – the need for data ownership An important component of any data strategy, and one that must be incorporated from the outset, is the governance of data itself. Firms must establish appropriate governance and clear guardrails from the very start to establish standards and maintain quality of data over time.

These must be part of the data strategy and the lines of responsibility for this need to be clear.

Our discussions with wealth firms found that topline responsibility typically sits with the COO or CDO, and that within their reporting lines will be a data team overseeing the technical aspects of the data. Many firms have found it helpful to appoint ‘data champions’ to lead the cause within the business, engaging all teams to drive success.

There is an increasingly strong belief that data quality and management is the responsibility of everyone in the business, not just a chosen few. This means combining central oversight with distributed ownership. Within firms, often an individual will have responsibility for a system and then others will have responsibility for individual data sets and data quality management within that system. Data teams oversee technical architecture, but responsibility for data quality sits across the organisation.

“The board considers that everyone in the business has responsibility for good data. Management team members have responsibility for individual data sets – it is not a tech strategy but a business strategy.” – COO, mid-tier bank

For governance to be effective, firms must also clearly map how data flows through their organisation. Incoming data must be identified in a consistent manner and stored correctly, so that it can be easily accessed and used for timely reporting, both internally and externally.

Different firms are approaching this in different ways. No firm we spoke with is yet at a point where their data lake or warehouse is fully operational, although some are not far off. The key challenge firms face in implementing their strategies is gathering the required data, from across multiple existing systems, and funnelling it into the new structure while also establishing the correct labelling. This tends to be most challenging for client data or unstructured data – and for this data, existing technology is often not utilised to its full extent.

“Just trying to pull together stuff over the split systems is difficult. You therefore need specialist people to pull it all together. Hence the data lake project.” – COO, mid-tier wealth manager The work isn’t done once firms have centralised and remediated their data. It is essential going forward that they conduct ongoing reviews of the data they hold if they are to maintain quality and trust in that data. These reviews typically happen as part of workflow processes, or as part of checks such as anti-money laundering (AML) refreshes and client annual reviews.

Reviews form part of the ongoing governance structure around data. Although some firms may have an annual review in place of the data strategy itself, reporting back to the ExCo on outcomes, data reviews largely take place on an ongoing basis as part of operational and regulatory control. Client data is especially dynamic as circumstances change and must be recorded.

Order from disorder | Research paper

Taking people on the data journey A wealth business relies on its people and its internal culture to ensure the successful implementation of any data strategy. Board approval for the necessary investments and resourcing is important, but so too is firm-wide buyin, engagement, and understanding of the strategy’s objectives and end goals. Although many accept in theory that good data is important, without this understanding, that will not lead to investment or behavioural change.

“People generally recognise that without the right data it’s hard to do a lot of things – but it doesn’t then often translate into actually investing money to sort that problem out.” – COO, mid-tier wealth manager That makes training and culture critical to any data strategy. Firms must establish a ‘data-first’ culture and enact comprehensive training programmes as part of their strategy.

Not everyone in a business is technically efficient, and time can be wasted by individuals trying to track down the right data. This means firms must develop business-wide guidance on who can access data and how, both today and in the future. As data becomes more accessible, easier to use, and most importantly, trusted confidence in the new data structure will grow.

Our conversations with wealth firms revealed three people-related aspects to any data strategy that are essential to get right.

“Getting good people who genuinely understand the data – and don’t just say they do – is the biggest challenge we have.” – CTO, mid-tier advice firm

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First: Talent.

“It’s slightly a holy grail when it comes to technology people – they’re technical experts and may also have that business context – but they need the commercial perspective too.” – COO, mid-tier wealth manager Firms need individuals who understand what good data requires, and importantly how that applies practically to wealth businesses. The talent requirement has a few core capabilities: •

Strong technical and analytical skills to manage, manipulate, and extract value from data

•

Business understanding and wealth management sectoral understanding to devise practical and efficient processes and workflows

•

Data synthesis skills to consolidate fragmented data into usable, structured outputs

All the firms we spoke to have in-house teams, albeit quite small in some instances, which support the implementation of data strategies with the technical and analytical capabilities required. For smaller firms, this resourcing can be challenging. Nonetheless, access to this specialised expertise is essential to ensure the adoption and long-term success of a data strategy.

Second: Buy-in.

Third: Enablement.

“We need to stay true to our strategy. If you don’t get business buy-in, it becomes an IT strategy rather than what the business needs to function.” – COO, mid-tier bank

“We had to pivot away from having people self-serving, which is how we set up the original data architecture.

We effectively created what we call a business intelligence team to support the business.” – COO, mid-tier wealth manager

A data strategy cannot succeed without organisationwide engagement. All employees across the business must understand and commit to the data strategy and the inevitable changes it will impose upon processes and workflows. Data must become everyone’s responsibility – not simply a concern of technical teams. Key aspects of this buy-in include: •

Consistent adoption of new workflows and weaning the business away from manual processes

•

Aligning the strategy with business needs, ensuring the strategy is seen as enabling outcomes – not just as technology change for the sake of it

•

Cultural change that addresses resistance to new technology or new processes, and builds confidence in data-driven approaches.

Resistance to change can undermine progress, making it essential to align the strategy with business needs everyone accepts, rather than positioning it as a purely technical initiative. Encouraging those less enthusiastic about data to become data-proficient does not happen overnight. However, with strong use-cases to illustrate the benefits – and importantly, ongoing training and support while new systems are being implemented to house a data lake or warehouse – the chances of a more data-enthused workforce are higher.

“I hired a head of data governance who was very good and very experienced. We did some roadshows for the business to help them understand the need for good data governance and the benefits of it.” – Ex-COO, large wealth manager

Even with the right talent and strong buy-in, a data strategy will falter if employees are not practically enabled to use data in their day-to-day roles. Firms must improve data accessibility to make it straightforward for employees across the business to find, access, and use data without friction.

Improving the user experience of data is critical to driving sustained engagement. Where its accessibility is improved and employees can see how it improves their productivity, they will adapt their working practices. If data is difficult to locate or requires significant manipulation, employees will revert to manual workarounds, undermining the strategy.

This is especially critical in the front office, where the need to spend time with individual clients, combined with constant time pressures, makes ease of access essential.

Enablement requires: •

Intuitive access to consolidated, reliable data, tailored to different user needs, including those of clients, boards and ExCos, finance teams, and regulators

•

Clear structures and standardised outputs, such as dashboards and reports, that reduce the need to build from scratch

•

Support mechanisms, such as business intelligence teams or data specialists, to help users translate data into usable outputs

•

Empowerment of ‘super users’ who can experiment, refine outputs, and act as internal champions, creating templates that can be used by others or acting as case studies for the good use of data

Although not everyone may be comfortable working with data, its benefits can certainly be shared, and individuals can understand its importance and start to engage with it further.

For many employees, working with data does not come naturally. Providing structured starting points and guided support helps build confidence and capability over time.

“A lot of people just can’t build out data from scratch. They struggle with data and the concept of what’s possible so we found that if you give people something to start with, we can then help them build it out.” – COO, mid-tier wealth manager

In the short term this process may be painful, particularly while data is remediated. But getting this right over the longer term significantly reduces manual intervention and increases productivity through higher-quality, more timely outputs.

Strong data governance as part of the data strategy will ensure standards are maintained as these new reports are trialled.

The vast amounts of data, if well organised, trustworthy and accessible, will increase business opportunities for firms and reduce the costs of manual intervention. People will buy in as they gain confidence in the data source and recognise the benefits it brings.

Order from disorder | Research paper

Five essentials to achieving good data The drive for good data in the wealth industry is clearly underway. The goal is the ‘single source of truth’ for data storage, typically held in a data lake or warehouse, and accessible to all in the business as appropriate. Good data is now increasingly seen as a key differentiator by wealth firms to support growth, achieve scale, and drive ROI – as well as supporting better decision-making, improved client outcomes, and greater operational efficiency and productivity.

To achieve this, wealth managers must:

1. Have a data strategy All wealth firms, regardless of size, should have a data strategy in place. The goal should be a single repository for a firm’s data, accessible to all who need it. Where technology and resourcing constraints limit this end goal, some core governance principles – that prioritise data quality and standardisation – go a long way to improving day-to-day efficiency until the firm can make the appropriate investment.

2. Leverage technology Technology investment should support the goal of a singleinput, multi-use model. The days of data becoming stuck or siloed in different departments should become a thing of the past rather than something that is maintained for the sake of ease or continuity within the business. New processes and workflows should get rid of as much manual intervention as possible to remove the risk of human error.

3. Define data governance principles at the outset Through effective data governance, firms should ensure consistent data capture, storage, and usage. They should implement clear guidance for the input and ongoing management of data. Data journeys should be understood via defined workflows and automated processes. This will ensure the timely and appropriate recording of essential data is achieved. Manual interventions should be reduced as much as possible to reduce human error. The practice of entering data into a variety of different systems must end, if accurate and timely data is to be maintained.

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4. Get early and ongoing buy-in across the business Staff across the business need to buy in to the data strategy and culture. While the top of the business should set the example, it is only by embedding a datafirst culture throughout the business that the strategy will succeed. This means ongoing updates, education, usecases, and data support for everyone interacting with the data so that it becomes habit, rather than a nice-to-have.

People will then gain trust in the data and increased confidence in using it.

5. Aim to evidence and measure data value Sustained investment in data must demonstrate tangible outcomes. Establishing clear KPIs and linking data initiatives to business performance is essential for wealth firms to demonstrate success for their data strategies.

With these five foundations in place, firms will be better positioned to leverage advanced capabilities, such as AI, unlocking further efficiencies and insights. Then, they will truly see how good data can transform their business operations and unlock new opportunities for growth.

Conclusion Data management is becoming strategic for wealth firms, with accuracy, trust and accessibility increasingly critical to compliance, client relationships, and long-term growth.

The wealth industry is by no means the only sector trying to get to grips with its data. It is a challenge across almost all industries and sectors. However, wealth businesses face unique challenges.

Client trust depends on error-free reporting with timely, accurate data. Regulators increasingly demand robust, data-backed evidence of good outcomes.

Data has historically come behind wealth firms’ other transformation priorities, but this is changing. Firms recognise the importance of having reliable, clean data that is easily accessed and can support growth.

A wealth firm’s intensely personal relationship with its clients affords it a degree of knowledge that many other industries would envy. The data clients must share to enable the effective management of their wealth is likely comparable only to the information required by the medical profession. This creates both a responsibility and an opportunity: to use this data effectively to safeguard and grow a client’s wealth, but also to drive growth and enhance trust in wealth businesses.

Good data will support a wealth firm in its day-to-day business. Excellent data will aid in driving growth and allow a business to differentiate through increased opportunities and deeper, more trusted client relationships.

Order from disorder | Research paper

About the research WealthTech Insight Series This research is part of The Wealth Mosaic’s WealthTech Insight Series (WTIS), an ongoing and developing research process, mixing online surveys and interviews, and focused exclusively on technology in the wealth management sector across the world.

Rather than a one-off research process, the WTIS will seek to build an ongoing program of research among wealth managers of different types across the world on a broad range of technology and related topics, building up an aggregated knowledge base of both qualitative views and perspectives as well as quantitative data points.

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We wanted to sponsor this whitepaper with The Wealth Mosaic to better understand how others in our industry were approaching the challenge of establishing a single accurate, accessible and auditable source of truth across their business.

We have our own data management solution – the Managed Smart Data platform – which was originally built in response to the specific challenges we faced in sourcing, processing and delivering highquality Excess Reportable Income data to our clients. This paper, and the data challenges it highlights, validates the ways we’ve expanded the platform’s capabilities and gives us hope that our industry is finally moving in the right direction when it comes to comprehensive, end-toend data management.

Preya Patel, Managing Director, Raw Knowledge

Raw Knowledge originally formed part of sister company Financial Software Ltd (FSL), investment tax specialists and creators of the award-winning capital gains solution CGiX. We provided Excess Reportable Income (ERI) data to FSL clients for over ten years, as well as our own clients via independent contracts.

As our own client base grew, the Raw Knowledge team separated from FSL in 2024 to provide our ERI data as a standalone service and develop new data-focused capabilities.

Today, Raw Knowledge provides the most comprehensive ERI data in the market and helps businesses establish a single, actionable and auditable source of truth with the Managed Smart Data platform, an end-to-end data management solution built specifically for financial institutions.

Find out more at www.rawknowledge.ltd

Created in partnership with Raw Knowledge, this paper is intended to advance and develop the conversation around data in wealth management as it becomes more and more integral to so many firms’ long-term transformation plans.

It adds to our growing, aggregated knowledge base that gathers qualitative perspectives gained through high-level industry research.

It portrays not only the challenges facing wealth firms as they revise their approach to data management, but demonstrates how many leaders are leading the way.

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

Find out more at www.thewealthmosaic.com

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