Data fragmentation continues to be a significant challenge faced by the wealth management sector. While many are looking to improve their infrastructure and move towards a unified data layer, what exactly does that look like?
Advisors across the industry are still stuck with time-consuming admin work that limits their capacity to handle high-value tasks, such as fostering relationships. A recent report from Fidelity International found that on average advisors spend just a third of their time with clients. The majority of their time is spent on preparing reports, compliance tasks and admin. Over 70% of the respondents noted a reduction in administrative burden would help enhance their working day.
WealthTech company Prometeia sees data fragmentation as the most persistent structural issue the wealth management sector faces and has been for a long time. However, due to industry demands for more personalised, data-driven and digitally enabled advisory services, firms are starting to notice.
It is common for firms to have client, portfolio, product and market suitability data in isolated systems, and the same is for risk analytics and reporting outputs. Not only are they disjointed, they are cared for by independent teams and data models. “A relationship manager may see one version of the client in the CRM, another in the portfolio management system, another in the advisory platform and yet another in reporting or risk tools,” Prometeia said.
While fragmented data might not seem like the obvious cause of administrative burden, it can cause a lot more friction than expected. Fredrik Davéus, CEO and co-founder of Kidbrooke, explained that data fragmentation is “the problem under most other problems.”
When speaking with CTOs and the heads of digital at wealth management firms, most of them will point to time-to-market, client engagement and regulatory compliance as their biggest challenges, Davéus explained. However, the root cause of the issues tends to be data fragmentation.
He said, “A firm can’t deliver a consistent client experience across channels if the data feeding those channels is inconsistent. It can’t run reliable suitability assessments if the market data in its planning engine differs from the data in its reporting system. And it can’t move quickly when it wants to launch a new product or journey if every new initiative requires a fresh data integration project.”
There is significant damage that can be caused by fragmented data. For instance, forcing teams to hunt for data across systems wastes time in their day and can even lead to data being missed. This can impact the quality of their offerings to clients or even lead to compliance mistakes. Siloed data can also cause a burden to the client, forcing them to provide the same information multiple times. Ultimately, data fragmentation can impact the growth and efficiency of a firm.
Prometeia emphasised that fragmentation’s impact is not exclusively technological but also affects the operating model of the entire firm. Slower advisory processes, more reconciliation work, greater operational risk and lack of consistency across client experiences. These all add up and cause challenges for the firm.
“For wealth managers, the real challenge is that modern advice depends on connecting many different data domains in real time or near-real time: client needs, portfolio composition, product characteristics, market conditions, risk metrics, regulatory constraints and investment recommendations,” Prometeia said. “If these elements are not connected through a coherent data architecture, the advisory process becomes inefficient and the quality of insight suffers.”
This comes against a backdrop where clients expect advice that reflects their specific, overall financial situation, goals, risk profile, preferences and portfolio composition. Relationship managers need to spend more time with their clients and less time searching for data, Prometeia explained. Poor data connectivity also results in poor consistency. If portfolio analysis, suitability checks, proposal generation and reporting all have different datasets, they will produce different answers to the same client question.
Friedhelm Schmitt, Co-Founder & CEO fincite, framed data fragmentation as a strategic liability.’ He said, “When a client’s assets sit across five custodians and three systems that don’t talk to each other, the advisor’s most important tool, holistic context, simply doesn’t exist. What suffers is advice quality and thus the overall client experience.”
Modern client demands are only making the problem worse. Real-time portfolio views, personalised reporting and advice that reflects the entire financial life are all dependent on strong data structures. It cannot be delivered through siloed data. An even more pressing reason to ensure unified data is to take advantage of AI. Firms are racing to implement the latest AI solutions, but without clean data layers, it will not meet its full potential. Schmitt added, “You can’t build intelligent, compliant advice on a fragmented foundation.”
In the same vein, Davéus highlighted that data fragmentation can destroy the customer trust by a lack of uniformity across divisions. He said, “If your planning engine is drawing on one data source, your adviser tool on another, and your client portal on a third, you will eventually show a client three different numbers for the same thing. That destroys trust instantly, and it’s very hard to recover from.”
The previously mentioned Fidelity survey highlighted compliance as a significant time sink for advisors, and data fragmentation can only cause them more trouble with it. Davéus pointed to the UK’s Consumer Duty, which requires firms to demonstrate outcomes the offer clients are consistent and fair. This becomes impossible to evidence if the data underpinning the outcomes is inconsistent.
The fragmented data situation in the wealth management sector has formed over decades. Legacy systems were never designed to communicate and as new products are developed, they rarely connect with one another. Similarly, acquisitions add to the problem, with the data infrastructure often left in isolation, rather than being connected. Schmitt said, “The result is not one wealth management system. It is five or more systems held together by spreadsheets, good intentions and a lot of prayers.”
Prometeia echoed similar causes for the data fragmentation issues, pointing to five core reasons. The first is legacy architecture, which has been built over decades, with new systems bolted on over the years. Each of those were designed for specific workflows and not to work in unison with other parts of the infrastructure. Cause two is organisational fragmentation, with data ownership having been split across business units without a common governance framework. “Each function may optimise its own data processes, but the end-to-end advisory chain remains fragmented.”
The third cause is product and instrument complexity. Prometeia noted that wealth managers deal with a variety of instruments, whether it is funds, bonds, structured products, discretionary mandates, private markets, pension products or cash positions. These all come with their own identifiers, data vendors, classifications, valuation rules and risk analytics. Cause four is regulatory evolution. It said, “Requirements such as suitability, product governance, cost transparency and sustainability preferences have added new layers of data. These requirements are necessary, but they often expose weaknesses in existing architectures because the required data is not always available, standardised or connected to the advisory workflow.”
Its final cause is channel proliferation. With the various new ways for clients to engage with their provider, it has created greater customer experience, but more datasets to be collected. Whether it is interacting through branches, relationship managers, mobile apps, web portals, call centres, or somewhere else, if these are not supported by the same data layer, the client experience will become inconsistent.
“In practice, fragmentation usually comes from the accumulation of many rational decisions made over time. The problem emerges when those decisions are not tied together by a common data strategy.”
Unified data layers
To help wealth management firms take better control of their data, firms are looking to unified data layers. These are a single, holistic data layer where all downstream systems access market data, product data and customer data, regardless of where that data sits. Ultimately, this ensures consistency across the business. For instance, if the planning engine, advisor tool and client portal ask for a price or characteristic of a fund, they will receive the same answer all from the same normalised source.
Prometeia emphasised there is a distinction between a central database and a unified data layer. “It is the architectural and functional layer that connects, standardises and governs the data needed to support the wealth management business.” This means creating a trusted foundation where all data can be accessed consistently.
A good unified data layer, according to Prometeia, should create a single logical view of client and portfolio regardless of where the data sits. It should also standardise product and instrument information. “For example, the same fund, bond or insurance product should be recognised consistently across portfolio analysis, suitability checks, proposal generation and reporting.”
Other telltale signs of an effective unified data layer is one that integrates analytics and not just the raw data. Data is only useful when enriched with risk measures, performance indicators, liquidity metrics, sustainability data, target market information, cost data and other decision-support metrics, it said. Some other hallmarks are traceability to understand where data came from, its updates and how it was used, as well as flexibility to be tailored to a client’s specific infrastructure.
“From Prometeia’s perspective, the most effective approach is to combine a robust data foundation with business applications that use that data directly in the advisory and investment process. The value is not only in consolidating information, but in making it actionable: portfolio diagnosis, investment proposal generation, suitability controls, scenario analysis, monitoring and client reporting should all rely on the same governed data and analytics layer. That is what makes the difference between a data repository and a true wealth management operating layer.”
For Davéus, an effective unified data layer does three things. First, it aggregates data from various providers, normalises it, resolves conflicts, fills gaps and applies consistent transformations. Secondly, it enriches data by adding analytics, risk metrics and portfolio-level statistics. Finally, it provides this via a single, clean API that all downstream systems can utilise without complexity. He added, “The result is that when a data provider changes, or a new one is added, the change happens once, in the data layer, and every system that depends on it benefits automatically.”
While it allows firms to access all their relevant data, it is not just a large database. Schmitt explained, “A unified data layer does not mean one monolithic database. In practice, it means an API-first architecture that aggregates internal and external portfolios into a single client view. It means data lakes and data ponds that can easily adapt to the demands from customers and to possibilities from technology. At fincite, we call this the shift from AuV to AuM: from assets you can see to assets you can actually manage.”
For firms looking to implement a unified data layer, Prometeia, Schmitt and Davéus offered some advice.
Davéus urged that firms treat implementation as a series of deliberate, modular decisions. They should start with the area that data fragmentation causes the most immediate stress, which is typically, market data or product data. From there, solve the issue with a clean, API-based layer and prove its value. From here, they can gradually extend it to solve other pain points.
Firms will likely come across a number of challenges during their implementation. For instance, legacy contracts with incumbent data providers that cause switching friction, internal politics around which team owns the canonical data source, and an underestimation of the effort needed for data quality remediation.
He added, “The firms that navigate this well are those that treat data infrastructure as a strategic investment because in wealth management today, the quality of your data layer is directly correlated with the quality of the client experience you’re able to deliver.”
Prometeia explained that success comes from a long-term vision. “Implementing a unified data layer is not only a technology exercise; it requires a clear vision of the wealth management model the firm wants to build. The starting point should be the business ambition: more personalised advice, stronger investment governance, scalable advisory processes, consistent reporting and a better digital client experience.” The firms that are successful are those that see the unified data layer as a strategic model and not as middleware, Prometeia continued.
On a final note, Schmitt stated, “The implementation challenge is integration governance: defining the data contracts, mapping legacy fields, and maintaining quality over time. But the firms that invest in this foundation now are the same ones whose AI tools will actually work. And whose advisors will spend their time with clients, not chasing data.”
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