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Big Data and the Coming Conceptual Model Revolution

Tuesday, April 17, 2012

Conceptual modeling, or semantic modeling if you like, is a rather nebulous area in data management. There seems to be a lot of agreement that it is needed, some disagreement about what it is, and little understanding of how to do it. Yet I believe we are now at a point where we will be forced to deal with it in a far more serious way than we have in the past.

Definitional Problems

I define a conceptual model as "a model of business information purely as information without any concern as to how it might be stored as data."

To me a conceptual model is not a data model in any sense because it is not part of any effort to design a data storage solution. It is a model that captures information used in a particular area of the business.

Other definitions of "conceptual model" exist. Confusingly, the ANSI/SPARC definition of "conceptual schema" is something that describes "... all the data items and relationships between them, together with integrity constraints (later). There is only one conceptual schema per database." This is essentially what is commonly called a "logical data model."

Then there is the "conceptual data model," defined by Tom Haughey as "a high level or coarse data model which is preliminary in structure, possibly abstract in content and sparse in attributes, that is intended to represent a business area. It is preliminary in structure because it may contain many-to-many relationships."

I do believe that a conceptual data model has a place in data management, as a preliminary to a logical data model. However, it lacks the detail I would expect of a real conceptual model and suffers from being oriented to a data storage design rather than a full description of a business reality.

Data Models and the Relational Paradigm

There is strong evidence that conceptual models are becoming more important today than they have ever been. Essentially, conceptual models are becoming divorced from traditional data models, and the divorce is likely to be a messy one because of the way that data models and data modelers have grown up since the 1970s.

Data modeling as we know it today is inextricably linked with the relational database paradigm - the way in which the columns of database tables are all "related" together. The relational paradigm is so ubiquitous that data modelers do not realize just how much data modeling presupposes it. And the relational paradigm has been enormously successful. It has been tempting to think, therefore, that a logical data model can truly represent the business - to think that a logical data model is the same as a conceptual model.

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