CIM Model

By Cloud Information Model Content Collective · Updated on September 23, 2026

The Cloud Information Model (CIM) is an open, application-agnostic data model intended to give enterprises a shared vocabulary for the entities that show up across CRM, ERP, marketing, service, and analytics systems. Rather than each vendor inventing its own object names and relationships, CIM defines a common set of subject areas, entities, and attributes that any system can map to. The model is stewarded as an open-source project under The Linux Foundation, which hosts a broad portfolio of collaborative data and infrastructure projects.

This article explains how the CIM is structured, how its components relate to one another, how supertypes and subtypes work, and how the subject areas are organized. It also covers the practical decisions an architect faces when adopting CIM — mapping, governance, versioning, and extension — and where the model fits relative to other industry standards.

Key Takeaways

CIM organizes business concepts into Subject Areas, each containing Entity Groups, Entities, and Attributes — a hierarchy that maps cleanly onto schemas, tables, and columns.
Supertypes and subtypes let the model express shared characteristics (a Party that is a person or an organization) while still allowing specialization.
The model is deliberately application-agnostic: it describes business concepts, not any single vendor’s implementation.
CIM is published in multiple formats with example diagrams, so it can be consumed by modeling tools, code generators, and documentation alike.
The number and scope of subject areas grows with the consortium and community contributions, so adoption should account for versioning and change management.
CIM is one option among several; the right choice depends on whether you need a broad cross-domain model or a narrow, deep standard for one industry.

How the CIM Is Structured

The CIM is organized into components so that content can be navigated and consumed more easily. Each level of the hierarchy answers a different question, and understanding that hierarchy is the first step to using the model well.

Subject Area — A major business concept identified by the CIM consortium, such as Party. Each subject area contains one or more entity groups. Think of a subject area as a bounded context: it groups everything the business needs to know about one broad theme.
Entity Group — A logical grouping of related entities within a subject area, such as Account. Entity groups keep large subject areas navigable and give teams a natural unit for assigning ownership.
Entity — A unique object that an organization collects information about, such as an Account Contact. An entity is analogous to a standard database table.
Attribute — A unique characteristic of an entity, such as Account Id or Contact Email. An attribute is analogous to a standard database field within a table.

This four-level hierarchy is intentionally familiar. Data architects who have worked with relational modeling, dimensional modeling, or entity-relationship diagrams will recognize the pattern immediately. The value CIM adds is not a novel modeling technique but a shared, pre-negotiated set of names and relationships that multiple organizations and vendors can agree on.

A useful mental model: a subject area is roughly a schema or a domain; an entity group is roughly a namespace or module; an entity is a table; an attribute is a column. That mapping is approximate — CIM is a conceptual and logical model, not a physical one — but it helps when you translate CIM into a physical implementation.

Supertypes and Subtypes

Beyond the four core components, the CIM design customizes and extends entities into further groupings using supertypes and subtypes. This is where the model gains much of its expressive power.

Related: — The fully pipeline that just keeps running.

Supertype — An entity that is extended by subtype entities, and defines common attributes for similar concepts.
Subtype — An entity that extends another entity, and inherits the attributes from its supertype entity.

The classic example is Party. A party is anyone or anything the business deals with. A person and an organization are both parties, and they share attributes — a name, identifiers, contact points — but each has attributes the other does not. Modeling Party as a supertype with Person and Organization as subtypes avoids duplicating the shared attributes and keeps relationships (for example, “this opportunity belongs to this party”) consistent regardless of which subtype is involved.

Inheritance like this is a well-established concept in data modeling and appears in standards such as the Object Management Group’s UML and in the entity-relationship conventions used across the industry. When you implement CIM physically, you have to decide how to represent inheritance:

Single table — store all subtypes in one table with a discriminator column. Simple to query, but can produce many nullable columns.
Class table inheritance — one table for the supertype and one per subtype, joined on a shared key. Normalized and clean, but requires joins.
Concrete table inheritance — a separate, self-contained table per subtype. Fast for subtype-specific queries, but duplicates shared attributes.

There is no universally correct answer. The right choice depends on query patterns, the number of subtypes, and how often the shared attributes are read together. Document the decision, because it will affect every downstream integration.

Our pick: — that business teams can actually build on.

The CIM Subject Areas

The subject areas represent the major business concepts the consortium has modeled so far. Each is published with its own diagrams and formats, and several carry explicit version markers (for example, v1.0 or v0.1.1), reflecting that some areas are more mature than others.

Setup — Defines who you deal with, for example customer, supplier, and seller. It also covers the software and infrastructure concepts an organization operates: Software Host, Software Tenant, Software User, Software App, Software Test, Software Service, Software Batch Job, and IoT Device.

Data Model — The foundational modeling concepts themselves.

Hire — Activities related to setting up your business, for example internal business unit and worker. Entity groups include Job Application, Employee, Compensation, Training, Location, Work Territory, and Work Report.

Biz Process — Business process and business continuity concepts.

Produce — Handling of material that you will be buying, moving, and selling, for example product and inventory product. Entity groups include Supplier Product, Inventory Received, Inventory Product, Inventory Transfer, Electronic Media, Purchase Order, and Sales Agreement.

Related: — Push-down ELT built for cloud data warehouses.

Market — Activities used to promote your product, for example marketing campaign and web store. Entity groups include Party Resolution, Privacy Consent, Market Audience, Campaign, Promotion, Trade Event, Ad Buy, and Web Site.

Sell — Activities used to sell your product, for example creation of quotes and opportunities. Entity groups include Price Book, Shopping Cart, Quote, Contract, Opportunity, Opportunity Forecast, Sales Order, Loyalty Program, and Competitor.

Service — Activities to provide support for a product sold or serviced, for example a case or a survey. Entity groups include AI Assistant, Asset, Asset Subscription, Web Content, Case, Task, and Event.

If you are shopping: — for hybrid cloud-to-on-prem integration.

Fulfill — Activities you perform to fulfill an order to a customer, for example shipment and return order. Entity groups include Fulfillment Order, Shipment, Return Order, Work Order, Work Resource, and Work Forecast.

Interact — Activities to track engaging with either end users or other systems. Entity groups include Engagement, Conversation, Appointment, Software Event, Data Connector, Data Movement, Loyalty Journey, and Loyalty.

Finance — Activities to trace financial information in the company, for example payment, invoice, and expense report. Entity groups include Budget, Invoice, Payment Method, Payment, Credit Memo, Financial Ledger Account, Forecast, Calendar, and Tax Policy.

Analyze — Activities related to analyzing data, for example analyzing patterns, product use, data movement, data changes, and customer satisfaction. Entity groups include AI Model, AI Application, IoT Device Use, Data Lineage, Blockchain, Survey, Loyalty, and Journal.

Notice how the subject areas span both operational concerns (Sell, Fulfill, Service) and analytical ones (Analyze, Finance). That breadth is the point: a shared model is most valuable when it can describe the same customer, product, or order consistently whether the data lives in a transactional system or a warehouse.

Choosing Between CIM and Other Standards

CIM is not the only shared model in the enterprise. Several established standards overlap with parts of its scope, and a mature architecture often uses more than one. The decision is less about picking a winner and more about matching the model’s breadth and governance to your problem.

Standard	Primary focus	Typical strength	Where CIM differs
CIM	Cross-domain business concepts	Broad, application-agnostic coverage of CRM/ERP/marketing/service	Designed as a shared umbrella across domains
OMG Common Core Ontologies / UML-based models	Conceptual modeling notation and upper ontologies	Rigorous formal semantics	CIM is more directly business-oriented
Industry-specific models (e.g., retail, healthcare, finance verticals)	Deep coverage of one sector	Precision within the vertical	CIM trades depth for breadth
Vendor data models (CRM/ERP platforms)	One product’s objects	Tight integration with that product	CIM is vendor-neutral by design

A practical rule of thumb:

If you need a shared vocabulary across many systems and vendors, a broad model like CIM is a strong fit.
If you need deep, regulated, industry-specific semantics, a vertical standard will usually be more precise, and you can map it to CIM at the boundaries.
If you are integrating within a single vendor’s ecosystem, that vendor’s own model may be sufficient — but it will not help you connect to the next vendor.

The most common real-world pattern is a hub-and-spoke approach: CIM (or another canonical model) sits in the middle, and each source system maps to it. This is the same principle behind canonical data models in master data management and behind the “conformed dimension” idea popularized in dimensional modeling by Ralph Kimball.

Practical Guidance for Adopting CIM

Adopting a shared model is as much an organizational exercise as a technical one. A few decisions determine whether the effort pays off.

Start with a bounded scope. Do not attempt to map every system to every subject area at once. Pick one high-value domain — Party and Sell are common starting points because customer and opportunity data are widely duplicated — and prove the mapping end to end.

Decide your extension policy early. CIM is designed to grow with the consortium and contributions, but your organization will inevitably need attributes the model does not yet define. Establish a convention for local extensions (for example, a namespaced prefix) so that custom attributes are clearly distinguishable from standard ones and can be reconciled later.

Treat versioning as a first-class concern. The subject areas carry version markers such as v1.0 and v0.1.1, which signals that the model evolves. Pin the version you build against, track changes, and plan for migration. This is the same discipline you would apply to any dependency.

Map, don’t copy. CIM is a conceptual and logical model. Resist the temptation to generate physical schemas directly from it without considering performance, indexing, and the access patterns of the systems that will consume the data. Use the model to align meaning, then design physical storage for your workload.

Govern the mapping. The mapping between a source system and CIM is itself an asset. Version it, review it, and assign ownership. Tools in the data integration space — ETL and ELT platforms, data catalogs, and lineage tools — can help you track where each attribute originates and how it flows, which is exactly the kind of metadata the Analyze subject area anticipates with entities like Data Lineage.

Engage the community. Because CIM is open source and consortium-driven, gaps you find are often gaps others have found too. Contributing a proposed entity or attribute back to the project is both good citizenship and a way to reduce your long-term maintenance burden.

Formats, Diagrams, and Consumption

The CIM designs are available in multiple formats for each domain, including example diagrams. This matters because different audiences consume a data model differently:

Architects want diagrams and relationship views to reason about structure.
Engineers want machine-readable definitions they can feed into code generation, schema validation, or mapping tools.
Analysts and stewards want documentation that explains what each entity and attribute means in business terms.

Publishing in multiple formats is a deliberate design choice that lowers the barrier to adoption. When evaluating any shared model, check that it ships in formats your toolchain can actually ingest — a model that only exists as a PDF is far less useful than one with structured definitions.

Frequently Asked Questions

What is the Cloud Information Model (CIM)?

The Cloud Information Model is an open, application-agnostic data model that defines shared business concepts — such as Party, Account, and Sales Order — so that different cloud and on-premises systems can exchange data using a common vocabulary. It is organized into subject areas, entity groups, entities, and attributes, and is stewarded as an open-source project under The Linux Foundation.

What is the difference between a supertype and a subtype in CIM?

A supertype is an entity that is extended by subtype entities and defines the attributes common to similar concepts. A subtype extends another entity and inherits the attributes of its supertype. For example, Party can act as a supertype with Person and Organization as subtypes, so shared attributes are defined once and specialized attributes live in the subtype.

How does CIM relate to a database schema?

CIM is a conceptual and logical model, not a physical schema. Its entities are analogous to database tables and its attributes to fields, which makes translation intuitive, but you should still design physical storage — indexing, partitioning, denormalization — around your own query patterns rather than copying the model literally.

Is CIM a replacement for industry-specific data standards?

No. CIM is broad and cross-domain, while vertical standards are deep and sector-specific. Many organizations use a hub-and-spoke approach in which CIM serves as the canonical model in the center and industry standards or vendor models map to it at the edges.

Why do CIM subject areas have version numbers?

Version markers such as v1.0 and v0.1.1 indicate that the model evolves and that some subject areas are more mature than others. Pinning a version, tracking changes, and planning migrations is the same dependency-management discipline you would apply to any shared library or schema.

How do I handle attributes that CIM does not define?

Establish a documented extension convention, such as a namespaced prefix, so custom attributes are clearly distinguishable from standard ones. Then consider contributing the gap back to the project, since the model is designed to grow with consortium and community contributions.

P.S. A few readers have asked which enterprise ipaas we actually reach for — it's Boomi AtomSphere; if you want the current details.

Frequently asked questions