The challenge: unifying wealth platforms without increasing risk
The organization’s data environment reflected the complexity of modern wealth management. It spanned WRAP platforms, Master Trust offerings, and four acquired platforms that needed to operate as one. Each platform served different customer profiles, fee structures, advisor relationships, and compliance requirements.
Data flowed from dozens of evolving source systems, including multiple Oracle databases. Legacy data was inconsistent and difficult to harmonize, and core business concepts such as fees, compliance, and advisor performance were defined differently across platforms. Products and services had to be treated as distinct but interconnected entities to accurately reflect how wealth products are delivered and managed.
As acquisitions continued and regulatory expectations increased, this complexity became a growing constraint. Without a unified and automated approach, every new source, schema change, or platform integration risked slowing delivery, increasing operational risk, and fragmenting customer insight even further. Manual data movement and custom pipelines could no longer keep pace with the scale and change the business required.
As the team described it,
“We move data from Point A to Point B. VaultSpeed makes that seamless.”
The approach: model-driven automation built for wealth management scale
To address this challenge, the organization adopted a model-driven approach to data delivery, with VaultSpeed at the core of its Data Vault architecture. Instead of relying on hand-coded pipelines and brittle integrations, business concepts were defined once and generated consistently across the platform.
VaultSpeed manages Data Vault modeling at scale, cleans and aggregates data into business consumption layers, and establishes governed relationships across entities and systems. Master data is created and linked back into the warehouse with managed keys, ensuring consistency across downstream analytics, regulatory reporting, and marketing use cases. Metadata is harvested securely from multiple Oracle sources using a local agent, enabling automated detection of schema drift and generation of delta deployment bundles.
Without metadata-driven automation, managing schema changes, relationships, and keys across this many platforms would have required constant manual intervention and ongoing rework. VaultSpeed removed that bottleneck by making change predictable and repeatable.
We knew if we could get the hard part right first, everything else would fall into place. VaultSpeed made that possible.
Head of Data Engineering, Australian wealth managerA proof point at scale: tackling the hardest system first
Rather than starting with a simple source, the team deliberately began with one of the most complex and high-volume systems in the organization: an Oracle CRM platform containing more than 900,000 customer records.
Successfully delivering this system proved that the platform could handle enterprise-scale data volumes, complex relationships, and continuous schema evolution without destabilizing downstream use cases. It validated the entire approach and established a foundation that could support additional platforms with far less effort.
VaultSpeed was introduced by the organization’s technical lead, whose team of 5 engineers now drives core data modeling and vault development. From the outset, the platform integrated cleanly into agile sprint cycles and quickly became a trusted part of day-to-day delivery.
Delivering wealth management use cases at scale
VaultSpeed is now embedded in production and supports critical wealth management use cases across the business. The platform underpins financial crime detection, net funds flow reporting, and event-driven customer marketing, where actions such as new subscriptions trigger downstream API events and automated communications.
It also powers Looker dashboards used for real-time operational and strategic decision-making. The current data model spans complex domains such as fees, compliance, and advisor relationships, providing a consistent foundation for both regulatory and commercial use cases.
The platform continues to expand to support additional systems and capabilities, including a modern CRM stack built on Java, React, MongoDB, and Elastic. Integrations with Collibra for lineage, Informatica for supporting transformations, GitLab for CI/CD, and VaultSpeed’s FMC module for pipeline orchestration are extending governance and automation across environments.
As one team member summarized, they are no longer just moving data. They are creating relationships that power the wealth management business.
The impact: scale and speed without compromising trust
The result is not just a modern data platform, but a fundamentally different delivery model. Regulatory delivery is faster and more predictable, integration work no longer requires rebuilding logic from scratch, and new initiatives can be delivered without reintroducing compliance risk.
Data engineers spend less time resolving breakages and more time delivering new use cases. Model changes no longer ripple unpredictably across downstream systems. Advisor, compliance, and marketing teams benefit from more consistent, timely, and trustworthy data that supports daily decision-making.
VaultSpeed has become the backbone of data delivery, enabling the organization to scale wealth management use cases without scaling operational effort.
The takeaway: a blueprint for data-driven wealth management
This organization is building more than a data platform. It is building a foundation for the future of wealth management.
With VaultSpeed at the core of its data strategy, the company has moved beyond fragmented data movement to a model-driven approach that supports scale, compliance, and AI-ready innovation. The result is a data foundation that can evolve with the business while maintaining the trust that financial services demand.
For wealth management firms facing growth through acquisition, rising regulatory expectations, and pressure to deliver more personalized experiences, this story shows that model-driven automation is no longer optional. It is what allows scale, compliance, and speed to coexist.