Modeling Data Around Financial Services is Complex
In regulated financial environments, data models anchor standards, definitions, and controls. They provide the clarity regulators expect and the flexibility organizations need as requirements change.
Large financial services organization generate incredible amounts of interconnected data and have to comply with a myriad of regulations from KYC to GDPR and BCBS 239.
Modeling Your Business & Products
Working with Intangible Product Structures
An insurer or a telecom operator has to deal with intangible concepts. Like terms, renewals, coverage, bundles, etc.
While central to your operations, their meaning is not intuitively understood by all. This “intangibility” often results in:
Misaligned Definitions: Teams define & use terms inconsistently.
Disconnected Views: One unit might see a feature as a product, while another treats it as a configuration.
Complex Relationships: Customers often interact with the same product type across multiple channels, in different ways.
Working with Intangible Product Structures
Financial services operator has to deal with complex business environment from competition, regulations to legacy IT systems,
While data is central to your operations, their meaning is not intuitively understood by all. This “intangibility” often results in:
Misaligned Definitions: Teams define & use terms inconsistently.
Disconnected Views: One unit might see a feature as a product, while another treats it as a configuration.
Complex Relationships: Customers often interact with the same product type across multiple channels, in different ways.
Modeling Your Customer Segments
Complicated Customer Hierarchies
Customer relationships in service industries are intricate, with overlapping transactions.
For example, a single customer might represent a corporate subscription, a personal device plan, and the owner of a family plan all at the same time. Who should own the sales & marketing for this individual?
This makes it difficult to:
- Define a customer in the context of analytics.
- Link products and services to specific customer segments.
- Combine and analyze data across product segments.
Complex Customer Hierarchies
Customer relationships in financial service industries are intricate, with overlapping transactions.
For example, a single customer might have a credit card, savings account, mortgage, HELOC, and even corporate accounts. This combined with legacy systems and it can lead to continued siloed data.
This makes it difficult to:
- Define a customer in the context for KYC and analytics.
- Link products and services to specific customer segments.
- Combine and analyze data across product segments.
- Prepare datasets for AI and predictive modeling purposes
Modeling Based on Industry Standards
Relying on International Standards for Scalability
One solution to these problems is the use of industry frameworks.
For instance TM Forum (telecom) or the International Organization for Standards (e.g. ISO20022 for financial institutions) has published standard business definitions and the connections between these entities to create a business or semantic layer.
However, these often break down when applied to real-world operational data. An enterprise data platform remains as complicated as without a framework if you cannot adapt these standards to your unique needs.
Compliance with Standards and Regulations
In financial services, compliance depends on more than controls and reports. It depends on how data is structured, governed, and understood. A disciplined data model creates a shared language across risk, finance, and technology, aligning business meaning with regulatory expectations. When standards evolve, a strong model ensures your data adapts without ambiguity.
Standards and regulations require data that is accurate, explainable, and auditable. Data modeling translates regulatory requirements into governed structures ensuring lineage, ownership, and meaning are embedded by design. The result is compliance that is proactive, not reactive..
Whether you are building new data products, data vault or data warehouse, having enterprise data models that reflect reality, are easy to understand and scale with business, is critical to successful outcomes.
Why Ellie
01
Modeling Your Enterprise Data Product
Design Business-Driven Enterprise Data Products
The answer to these problems is in designing data products within the larger context of business expertise.
This requires your data team to collaborate with domain experts, bridging the gap between business and IT.
We bridge this gap between business and technical teams by providing a purpose-built platform to model enterprise data products.
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Focus on Business Domains
Define Intangible Concepts, Ensure Data Interoperability
You can:
- Build a common glossary.
- Design modular, reusable data products.
- Iterate on complex data models & data products.
It’s easy for business users to collaborate on Ellie, while technical teams can push these collaborative models into production within the same platform.
03
Frameworks to Reality
Leverage Industry Frameworks, Layer Over Real Data
What you now have is the ability to layer your existing data within a framework.This enables you to build a model that works — the best practices of a framework applied to real-world data.
You can accurately capture the essential structure of data without getting stuck in the details of how the source system organizes it.
Integrations & Open API Access
