By Chris Barisic, CTO, Sycamore Company
In wealth management, data has never been more abundant or misunderstood. Firms talk confidently about being “data-driven,” yet many still struggle to leverage data to answer basic business questions expediently. Executives request reports that should take minutes, only to discover they will take weeks, if they can be produced at all. The gap between what firms believe their data infrastructure can do and what it actually delivers remains one of the industry’s greatest challenges.
The good news is that the conversation around data management has matured. Most firms now recognize that data itself does not have to reside in a single, monolithic location, but data management does require a central point of control. In other words, the future is less about forcing everything into a single warehouse and more about having a unified way to see, govern, and analyze data wherever it resides.
Moving beyond the data warehouse mindset
For years, the dominant model was simple: collect data from every system, centralize it in a warehouse or data lake, normalize it, and then build reports on top of it. Success with that approach was short-lived. As data volumes exploded and sources multiplied, firms found themselves spending enormous time and money simply moving data around. Nightly sync jobs chewed up bandwidth, introduced delays, and created new points of failure.
Today, the industry is moving toward a more loosely coupled data model. Modern platforms allow firms to connect to data where it already lives, link it together through robust connectors, and run analysis without constantly replicating it. Platforms like Salesforce are investing heavily in this vision, enabling organizations to view their information holistically while letting individual systems continue to do what they do best.
This shift matters because it lowers friction. Instead of engineering teams spending months building pipelines and maintaining data lakes, firms can focus on actually using their data to identify trends, improve efficiency, and make better decisions.
The reporting reality check
One of the biggest disconnects I see across wealth management firms is around reporting. Leadership often assumes that if the data exists somewhere in the organization, it must be easy to analyze. In practice, that is rarely the case. Data may live in multiple systems, use different definitions, or lack meaningful relationships between objects. The result is a reporting environment that is slow, brittle, or simply unusable.
This is where architecture matters. You can have the most comprehensive dataset in the world, but if it is poorly structured or inconsistently normalized, you will never get reliable insights from it. In fact, many firms’ reporting problems are not caused by a lack of data, but by the way that data was ingested and modeled in the first place.
Data normalization is foundational work. Bringing data in from multiple sources, reconciling differences, and making it queryable is what turns raw information into something the business can actually use. Increasingly, we are seeing artificial intelligence applied here in a very practical way. The most real, near-term value of AI is not flashy front-end features, but behind-the-scenes data work. AI can take the first pass at mapping and standardizing data models, letting humans then review and refine the output. Done right, this can dramatically reduce time and effort while improving consistency.
Avoiding common modernization mistakes
When firms decide to “modernize” their data infrastructure, the most common mistake is overbuilding. Too often, organizations are sold on a grand vision of an expensive data lake or warehouse without fully understanding whether they need it or how they will use it. Months later, they are left with a costly platform that still cannot answer the questions that matter most.
Another frequent misstep is ignoring APIs, the rules and tools that let different software systems talk to each other in a controlled, predictable way. In 2026, designing a data architecture that is not API-centric is a recipe for rigidity. Data should not only be accessible internally but also exposed securely so that other systems can consume it when needed. An API-first mindset future-proofs your infrastructure and reduces dependence on outdated file-based integrations that still persist across our industry.
Defining best-in-class data management
Looking ahead, best-in-class data management in wealth management will be defined by three things.
First, centralized management, not necessarily centralized storage. Firms need a clear, governed view of all their data, even if that data lives across multiple platforms.
Second, architecture that supports fast, reliable reporting. Data objects must be thoughtfully related, normalized, and designed with real business questions in mind.
Third, security and access control are baked in from day one. Bringing data together increases its value as well as its risk. Role-based access, strong governance, and compliance-aware design are non-negotiable.
The firms that get this right will be the ones that can move quickly, adapt to new technologies, and ultimately deliver better experiences for advisors and clients alike. Data is no longer just an IT concern; it is a strategic asset. Managing it well is not optional. It’s foundational to the future of wealth management.
