Designing Data for Microservices and AI: Why Canonical Models Still Matter
- JM Abrams
- May 28
- 4 min read
By J.M. Abrams, Chief Data Culturist – www.dataculturehivemind.com
Organizations are increasingly data-driven in directing their strategies, especially in healthcare and insurance, two formidable design strategies must work together:
Canonical Data Models (CDMs) to provide shared meaning across systems
Microservices Architecture to enable service autonomy and agility
This post explains why both are essential, how they differ, and how to integrate them in a scalable, AI-ready data ecosystem.
What is a Canonical Data Model?
A Canonical Data Model (CDM) is a standardized and enterprise-wide representation of common business entities that provides a consistent structure and semantics for data exchanged between different systems or domains.
It serves as a single source of truth for data definitions, abstracting away the idiosyncrasies of individual source or destination systems. This model enables interoperability, reusability, and data integration by aligning all systems to a shared vocabulary and structure.
The Starting Point: Canonical Data Models
A Canonical Data Model (CDM) provides a standardized, enterprise-wide approach to representing business entities, such as members, providers, and claims. It abstracts away the differences in how source systems store or name data.
In health insurance, a CDM ensures that every downstream consumer—from actuarial analytics to AI-powered fraud detection—understands a "member" the same way, regardless of whether the data came from a CRM, a claims system, or an eligibility feed.
But CDMs Come at a Cost: Storage Efficiency
CDMs emphasize clarity over optimization. For example, consider a Member who can have multiple phone numbers and addresses (residential, billing, mailing). A CDM might embed these as arrays or define them as linked sub-entities.
This design is not storage-efficient. If two members share a phone number (say, spouses), each would still have a unique record for that number. That’s intentional because semantic ownership outweighs reuse in a CDM.
Couldn’t We Be More Efficient?
Technically, yes. We could normalize by having a shared Phone table and a join table, such as member_phone. But this introduces ambiguity:
Who owns the number?
What if one member marks it “emergency” and the other “mobile”?
What if one member has given consent to use that phone number for health-related communication under HIPAA rules, but the other hasn’t?
In short, over-optimization can lead to semantic confusion, particularly in regulated domains such as health insurance.
Why CDMs De-prioritize Storage Efficiency
While it's tempting to normalize data for maximum reuse and efficiency, CDMs intentionally lean toward clarity, autonomy, and ease of integration.
Here's why:
🗣 Semantic Transparency: CDMs represent real-world business concepts directly. Even if two members share a phone number, each maintains an independent record to preserve identity and context.
🧩 Decoupling from Physical Storage: CDMs are often serialized for communication (e.g., JSON, XML) for transmission across systems. They're about communication, not just storage.
🔄 Data Exchange Priority: CDMs enable interoperability and data sharing, not just transactional operations.
🚫 Avoiding Overgeneralization: Over-normalizing (e.g., shared phones or addresses) introduces ambiguity and governance risks, especially in healthcare or regulated industries.
📜 Governance and Ownership: In health insurance, traceability and clarity of data lineage outweigh byte-level storage savings.
CDMs embrace controlled redundancy because it’s better to be understood by every system and team than to save a few rows of storage at the cost of confusion.
Shifting Gears: Microservices and Autonomy
Modern health insurers are increasingly adopting microservices architecture to enhance agility and scalability. Each microservice owns its bounded context and often its database.
CDM vs Microservices: Design Trade-Offs
Centralized vs Decentralized Data Ownership
CDM: One central source of truth
Microservices: Each service owns and controls its data
Schema Design
CDM: Shared relational schemas
Microservices: No cross-service joins
Optimization Goal
CDM: Optimized for standardization
Microservices: Optimized for autonomy and agility
Semantic Approach
CDM: Semantic consistency across systems
Microservices: Bounded context within each service
Bridging the Gap: Why You Need Both
You may have to choose between clarity and autonomy. But in reality, successful modern architectures use both CDMs and microservices strategically.
CDMs guide how your enterprise defines data entities, such as “member,” “provider,” and “claim,” consistently across departments and systems.
Microservices enable teams to build, scale, and deploy applications independently within their respective bounded contexts.
The glue between them is semantic data contracts — shared agreements that preserve meaning across service boundaries. These contracts ensure that even if each microservice evolves independently, the semantics remain aligned.
Example: Member, Provider, and Medical Claim Services
In a microservices world:
The MemberService owns member demographics.
The ProviderService manages the network and specialties.
The ProcedureCatalogService manages the standardized listing of all medical services and interventions.
The MedicalClaimService stores claim submissions and references member/provider IDs, but does not own those definitions.
How They Communicate: APIs and Semantic Contracts
Services don’t share tables — they talk through APIs or events. This makes semantic data contracts critical.
A semantic contract defines what each field means, the required formats, and valid values. It’s the glue that ensures systems understand each other even as they evolve independently.
Summary: Use Both for a Resilient Architecture
In health insurance, data modeling is no longer just about saving space — it’s about communicating meaning across systems, teams, and technologies.
"A Canonical Data Model prioritizes truth over optimization. It’s about making meaning portable and resilient to change."
Don’t think of CDMs and microservices as competing philosophies.
Think of them as layers:
CDMs define the semantic foundation
Microservices deliver executional agility
Together, they form the architectural spine of a future-proof, AI-ready health insurance platform.
Explore more on data culture and architectural clarity at Data Culture Hive Mind.
Disclaimer: The opinions expressed on this blog are solely those of the author and do not reflect the views, positions, or opinions of my employer.
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