How a Large Financial Services Organization is Scaling Trusted Data Products with OvalEdge

The client is a large U.S.-based financial services organization operating in a complex, data-intensive environment. Its business relies on information spanning claims, policy administration, customer management, and financial operations.

Like many established financial services organizations, the organization manages data across legacy applications, modern cloud platforms, and multiple data-processing systems. Making this information accessible, reliable, and reusable across business functions is essential to its broader data strategy.

The company has been investing in enterprise data governance to support greater operational efficiency, stronger data trust, and the development of business-ready data products.

The organization's data ecosystem presented two related challenges: understanding the data distributed across numerous systems and determining which information could be trusted and reused for business purposes.

“Data products” had become an important enterprise priority. The company wanted business teams to discover and consume governed information without repeatedly rebuilding datasets or relying on manual investigations to establish their reliability.

However, creating trusted data products required more than bringing metadata into a catalog. Teams needed visibility into data origins, transformations, and downstream dependencies. Without connected lineage and consistent certification standards, scaling data products across different business functions would require significant coordination and manual effort.

The organization was also pursuing these objectives with a relatively small central governance team. That team had to work closely with business stakeholders, data engineers, and other technical groups to coordinate governance activities across a much larger enterprise.

The challenge was to establish a repeatable operating model that could support broader data discovery and reuse without requiring governance resources to grow at the same pace.

OvalEdge was already part of the organization's data-management environment. As the company's priorities evolved, the engagement expanded toward supporting governed data products, automated lineage, and a more scalable approach to enterprise governance.

A key requirement was the ability to work across heterogeneous technologies rather than forcing all information into one centralized system.

OvalEdge's metadata connectivity, data catalog, lineage capabilities, and data-product marketplace provided a foundation for connecting technical information with the business context needed for discovery and certification.

The implementation was supported by close collaboration between the rganization's governance and engineering teams and OvalEdge's technical specialists. Together, they worked to establish standards and workflows that could be applied repeatedly as additional data products entered the governed environment.

A governed data-product marketplace

The organization adopted OvalEdge's data-product marketplace to organize data products and associate them with the underlying data assets and metadata.

The marketplace provides a structured environment for making data products discoverable and supporting their reuse across business functions. It is intended to help business users identify relevant information and understand the context behind the products they consume.

Consistent certification workflows

The organization implemented a tiered data-product certification framework with active workflows.

This approach establishes a consistent process for evaluating and managing data products rather than treating each new product as an independent governance exercise.

Certification brings together governance requirements, supporting metadata, and lineage information to strengthen confidence in the products being made available to business teams.

Automated metadata ingestion and data lineage

OvalEdge connects with the organization's underlying data environment to ingest metadata and support lineage across data platforms and transformation processes.

The teams have combined automated lineage capabilities with implementation templates and technical validation to establish traceability for selected data products. They have also developed common lineage standards that can be applied as additional products are introduced.

This work is particularly important in financial services environments where critical information may pass through several systems before becoming available for reporting, analytics, or operational use.

A scalable governance operating model

The organization manages its governance program through a lean central team working closely with business stakeholders and data engineering teams.

OvalEdge supports this operating model through automated metadata ingestion, lineage capabilities, and reusable governance workflows. These capabilities help reduce dependence on manual activities while enabling the central team to focus on governance priorities, standards, and coordination.

The approach combines platform automation with the business and technical expertise needed to maintain trusted data products across a complex enterprise.

A repeatable approach to trusted data products

The organization has moved beyond cataloging data assets to implementing a production data-product certification framework. With active certification workflows, completed lineage implementations for selected data products, and standardized approaches to tracing data across systems, the organization has established a repeatable foundation for extending governed data products to additional business domains.

Better visibility into distributed data

By connecting metadata, business context, and lineage, OvalEdge gives teams a more consistent way to investigate where information originates, how it is transformed, and how it relates to governed data products.

This visibility provides an important basis for assessing data products before they are made available for broader business use, particularly when information comes from multiple systems and transformation processes.

Governance that can scale with a lean team

Automated metadata ingestion, lineage capabilities, and reusable implementation standards allow the organization’s central governance team to coordinate its program without taking responsibility for every technical activity.

Business teams contribute requirements and definitions, while data engineering teams provide technical expertise. OvalEdge brings these activities together in a shared governance environment, creating an operating model that can support further expansion without relying entirely on additional central governance resources.

A stronger foundation for enterprise reuse

With a governed marketplace and a consistent certification process in place, the organization has established a common approach for organizing and preparing data products for business consumption.

As adoption expands, this foundation is intended to help teams reuse existing data products instead of independently rebuilding datasets and pipelines, supporting the company's broader operational efficiency objectives.

The organization sees its governance foundation as an important prerequisite for broader AI adoption.

Its roadmap begins with metadata ingestion and lineage, followed by consistent business definitions, governance controls, and certification. The next stage involves AI-assisted capabilities for activities such as data discovery, metadata enrichment, and classification.

The organization is also exploring how governed metadata and data products could provide business context to enterprise AI tools through emerging integration approaches, including Model Context Protocol (MCP).

These AI capabilities represent the next phase of the program rather than outcomes already realized.

By building a trusted metadata and governance layer first, the organization is positioning itself to expand AI-enabled data access while maintaining greater visibility into the information those systems rely on.