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.

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

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 long-term gain.” – Ex-COO, large wealth manager

With such a vast amount of data handled day-to-day by wealth businesses, AI presents a huge opportunity to transform how data and its outputs are managed, analysed, and used. 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

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 data-driven insight, firms can better align their services with what their clients value and differentiate themselves from increasingly tight competition.

Another impact on the client experience is through saving clients’ time and effort. One COO wants to see a joined-up 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.

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 decision-making, 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.

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

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 in-house 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.

• “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 free-form 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, mid-tier 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. 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

Implementing a data strategy

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.

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

Establishing new work processes

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.

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.

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 both 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 needs to 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.

Interested in reading the full report? You can find it online here.

About the WealthTech Insight Series (WTIS)
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.

Discover our research collection!

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• The European wealth playbook for growth – read here
• The role of technology for recruitment and retention within wealth management – read here
• The quest to become the best – becoming the trusted wealth coach and adviser – read here
• Productivity and growth in wealth management – read here
• Managing model portfolios on multiple platforms – read here

About Raw Knowledge
Raw Knowledge is a specialist data provider and data management firm under the Industrial Thought group of companies. 

Raw Knowledge began life as part of sister company Financial Software (FSL). Its team helped identify the need to improve industry access to quality excess reportable income (ERI) data and provided this to both its own and FSL clients. As its team, customer base and capabilities grew, Raw Knowledge separated from FSL in 2024 to begin offering its own solutions and services.  

Today, Raw Knowledge provides the most comprehensive ERI data in the market and helps businesses make better, data-driven decisions with its Managed Smart Data Platform. This pioneering data management platform creates a traceable and harmonised view of businesses’ disparate data sources to help them streamline and scale their operations, act faster on insights with fewer errors and maintain a clear audit trail.

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.

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