A concept model organizes the business vocabulary needed to communicate consistently and thoroughly about the know-how of a problem domain.

A concept model starts with a glossary of business terms and definitions.  It puts a very high premium on high-quality, design-independent definitions, free of data or implementation biases.  It also emphasizes rich vocabulary.

A concept model is always about identifying the correct choice of terms to use in communications, including statements of business rules and requirements, especially where high precision and subtle distinctions need to be made.  The core concepts of a business problem domain are typically quite stable over time.^[1]

Concept models are can be especially effective where: 

• The organization seeks to organize, retain, build on, manage, and communicate core knowledge or know-how.


• The project or initiative needs to capture 100s or 1,000s of business rules.


• There is significant push-back from business stakeholders about the perceived technical nature of data models, class diagrams, or data element nomenclature and definition.


• Outside-the-box solutions are sought when reengineering business processes or other aspects of business capability.


• The organization faces regulatory or compliance challenges.

The standard for concept models is the OMG standard Semantics of Business Vocabulary and Business Rules (SBVR).^[2]

Concept Model vs. Data Model

A concept model differs from a data model in important ways.  The goal of a concept model is to support the expression of natural-language statements, and supply their semantics — not unify, codify (and sometimes simplify) data.  Therefore the vocabulary included in a concept model is far richer, as suits knowledge-intensive problem domains.  In short, concept models are concept-centric; data models are thing-entity-or-class-centric. 

Data models can usually be rather easily derived from concept models; the reverse is much harder (or impossible).  Like data models, concept models are often rendered graphically, but free of such distractions to business stakeholders as cardinalities.

The Components of Concept Models^[3]

Noun Concepts.  The most basic concepts in a concept model are the noun concepts of the problem domain, which are simply 'givens' for the problem space. 

• For the IIBA's Business Analysis Core Concepts Model (BACCM) these basic noun concepts are:  need, stakeholder, value, change, context, and solution. 


• In finance, basic noun concepts might include financial institution, real-estate property, party, mortgage application, lien, asset, loan, etc. 

Verb Concepts.  Verb concepts provide basic structural connections between noun concepts.  These verb concepts are given standard wordings, so they can be referenced unambiguously. 

• In BACCM some basic wordings for verb concepts include:  Value is measured relative to Context, Change is made to implement Solution, Stakeholder has Need. 


• In a financial business, some basic wordings for verb concepts include:  Lien is held against Real Estate Property, Party requests Loan, Asset is included in Mortgage Application.

Note that these wordings are not sentences per se; they are the building blocks of sentences (such as business rule statements).  Sometimes verb concepts are derived, inferred, or computed by definitional rules.  This is how new knowledge or information is built up from more basic facts.

Other Connections.  Since concept models must support rich meaning (semantics), other types of standard connections are used besides verb concepts.  These include but are not limited to:

• Categorizations — e.g., Person and Organization are two categories of Party.
• Classifications — e.g., 'Toronto Dominion Bank' is an instance of Financial Institution.
• Partitive (Whole-Part) Connections — e.g., Dwelling and Land are two Parts of a Real Estate Property.
• Roles — e.g., Applicant is the role that Party plays in the verb concept Party requests Loan. 

Strengths

A concept model:

• Provides a business-friendly way to communicate with stakeholders about precise meanings and subtle distinctions.


• Is independent of data design biases and the often limited business vocabulary coverage of data models.


• Proves highly useful for white-collar, knowledge-rich, decision-laden business processes.


• Helps ensure that large numbers of business rules and complex decision tables are free of ambiguity and fit together cohesively.

Limitations

A concept model:

• May set expectations too high about how much integration based on business semantics can be achieved on relatively-short notice.


• Requires a specialized skill set based on the ability to think abstractly and non-procedurally about know-how and knowledge.


• Involves a knowledge-and-rule focus that may be foreign to stakeholders.


• Requires tooling to actively support real-time use of standard business terminology in writing business rules, requirements, and other forms of business communication.

  For more information about Concept Models, refer to Business Rule Concepts 4th ed., by Ronald G. Ross, 2013, Part 2 ("Business Vocabularies and Concept Models"); also visit BRSolutions.com      

References

[1]  Ronald G. Ross, "How Long Will Your Fact Model Last? — The Power of Structured Business Vocabularies," Business Rules Journal, Vol. 12, No. 5 (May 2011), URL:  http://www.BRCommunity.com/a2011/b594.html   

[2]  This discussion is consistent with that standard, but explains concept models from a business point of view.  See the SBVR Insider section of BRCommunity.com for more information about SBVR.  

[3]  The first set of examples in each of the following two subsections is from the Business Analysis Core Concepts Model (BACCM), a part of the IIBA's Business Analysis Body of Knowledge (BABOK), version 3.  

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