As the wealth management sector continues to attract more people and the marketplace becomes crowded with more providers, data analytics could be an important differentiator.
Data is a valuable resource for advisors and the ability to efficiently generate insights is vital. In the digital age firms have never had access to so much data, however, many are still failing to take full advantage of it. Despite data empowering advisors to improve their decision making and creating better portfolios to meet the needs of clients, many firms are limited by legacy systems that lock data in siloed systems.
Modern technology is allowing firms to unlock the full value of their data. A recent PwC report examined the interest of asset and wealth managers in data analytics. It claims 59% are either adopting or considering big data analytics for investment operations.
Fredrik Davéus, CEO and co-founder of Kidbrooke, highlighted the importance of data analytics solutions in the modern market. He said, “Data and analytics have moved from being operational enablers to strategic differentiators in wealth management. With rising expectations for personalised advice, real-time insights, and scalable automation, the capabilities of your analytics platform can define the quality of your offering, and the speed at which you can deliver it. This section outlines what to prioritize when evaluating solutions.”
Modern wealth experiences mean firms need to answer a variety of questions quickly and credibly, he continued. However, this is only possible through robust analytics. “From simulation-based financial planning to real-time goal tracking, data is what powers meaningful engagement.
“Subpar analytics won’t just lead to missed opportunities, they will actively degrade the client experience and slow innovation.”
Hazal Sabah, Senior Product Manager, ByAllAccounts at Morningstar Wealth, also noted the growing importance of modern data analytics. She noted that many firms are plagued with poor data access, whether it is fragmented across systems locked behind vendor walls or duplicated and inconsistent. When firms grow, this data sprawl problem also gets bigger.
As such, firms looking to solve this are encouraged by Sabah to focus on data ownership. This allows firms to control where data moves, how it can be leveraged across tools, scale without restrictions and not needing to wait on vendors.
Another core component of this being ready for the latest advancements in technology. AI is increasingly being used in wealth management to help improve the advisor and client experience, ultimately ending in better outputs from the firm. Unfortunately, siloed data stifles AI.
Sabah added, “The possibilities of AI in WealthTech are massive. But its success fundamentally hinges on the quality and accessibility of the data. Financial data is notoriously “dirty” and unstructured, making the task of training data sets extremely time-consuming. Data scientists report spending up to 80% of their time cleansing the data and just 20% of their time actually doing the useful, actionable work on top of the data.”
What to look for in a data analytics solution
When it comes to identifying the right solution, there is no golden answer. Each firm will have a different set of needs and requirements and so firms will need to assess each solution to find which best meets their needs. However, while there is no single answer to the perfect tool, there are several features and capabilities that a firm should be looking out for.
Firms looking for external partners to enhance the tech infrastructure should examine the breadth of data from data partners. This includes coverage across custodians, held-away accounts and alternative assets. In line with this, also assess the depth of normalised and enriched data, including positions, open and closed tax lot data.
Other key points include the APIs and flexible outputs of CRMs, reporting tools and data lakes, and the reliability of the system.
“Specialized data providers especially play a critical role in delivering well-structured data. They eliminate the bulk of the operational overhead and costs associated with data acquisition and management.”
This is achieved through clean, accurate and enriched data. This ensures AI models provide accurate and reliable outputs. “The old adage “garbage in, garbage out” rings especially true here. To produce reliable outputs, AI models need high-quality data,” she explained.
They also offer access to the right data sources, which is vital given a common challenge in wealth management is the fragmented nature of data. Connections to various available data sources can save a firm a lot of time and internal resources.
For Davéus, there are a few non-negotiable attributes firm should look for when exploring data analytics solutions. One of these is comprehensive functionality. He said, “The platform should cover a broad set of financial planning and investment scenarios, both current and future, to avoid vendor churn.”
Secondly, firms should assess the time-to-market. “Rapid delivery is no longer optional,” he noted, with AI helping to accelerate development cycles and reduce time to launch for new products. Similarly, firms should be aware of the scalability and performance of a solution, ensuring it can cope with real-world load conditions without reducing its performance.
One final important aspect to consider when assessing a solution is the track record of delivery. “A proven ability to deliver on time and on budget is critical. We’ve seen far too many cases where underperforming vendors cause severe go-to-market delays, stalling growth and draining internal resources.”
Red flags of a solution
Looking at the capabilities is not the only important factor when choosing a solution. Firms also need to look for any potential red flags or areas that could cause problems in the long-term.
Davéus highlighted a number of warning signs that should alert firms. The first of these is a narrow scope of analytics. He said, “If a platform only handles a handful of simplified use cases, you’ll quickly outgrow it. Adding more advanced scenarios later often requires costly new integrations or switching providers entirely, which adds tech debt and internal friction.”
The second red flag is an opaque or expensive customisation path. Davéus warned that firms should steer clear of solutions that need extensive vendor-side development for simple adjustments. This becomes an even bigger problem when the changes are handled by an outsourced team that lacks domain knowledge. “This setup often leads to inflated costs, long delays, and poor-quality adaptations that miss the mark with end users,” he said.
Finally, firms should be cautious when there is a lack of domain expertise post-sale. Davéus noted that even technically capable vendors can fall short when their delivery teams lack domain knowledge. “You want a partner who understands your business context deeply, not just a vendor who “ticks the box” on technical features.”
Sabah also highlighted a number of red flags firms should be cautious of. At the top of the list was working with generalist data aggregation partners.
“Working with generalist data aggregation providers can expose gaps in account coverage—particularly for complex account types like trusts, annuities, and managed accounts, including scenarios where delegated access is required.
“Even when accounts are supported, the depth of data points is often too thin to power critical wealth workflows such as performance reporting, portfolio rebalancing, and compliance.”
One final red flag to note is vendor lock-in. Sabah explained that some solutions can make it tough to own and move data or integrate it with existing systems. This creates long-term dependency on the solution, which can impact innovation and increase switching costs when the firm grows and needs other tools.
Sabah noted that firms should look for specialised platforms that are built for wealth management and avoid these pitfalls. For instance, ByAllAccounts Data Network provides clients across wealth management with the precise data required for workflows including performance reporting, portfolio trading and rebalancing, and compliance.
Given the importance of data analytics, FinTech Global recently released a list of 10 innovative solutions that are currently available in the market in 2025.
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