This article explains why making common sense of the real world first involves abstracting or grouping actual things into kinds under a concept that defines them.

In this article, the authors analyze enterprise’s data architecture and obtain intelligent insights to find the best performing solution for a given set of requirements.

Identifiers aren't locators, and they aren't pointers or links either. They are a logical concept in a relational database, and, unlike the more traditional methods of accessing data, don't derive from the way that data gets stored. Identifiers uniquely identify members of the set, and it should be possible to validate and verify them. Celko somehow involves watches and taxi cabs to illustrate the point.

This article provides an introduction to proven strategies that IT managers can find useful for large-scale data modeling projects.

For application developers focusing on the needs of their code, rather than worrying about the complexities of data representation, the Entity Framework’s abstractions are essential. But eventually a database needs to be created, and this article shows how it’s done.

This article presents how a Strategic Model is developed for rapid delivery of Enterprise Architecture. It also shows how project plans and associated project maps can be derived from a data model to manage the rapid delivery of priority business activities and processes into production as systems. These project plan and project map derivation concepts have not previously been used in data modeling.

Bill Lewis describes the benefits of an application development environment in which all data dependencies begin and remain clearly, precisely and explicitly defined.

This article explores the ways hierarchical structures can be used to significantly increase data value and its utilization.

This article discusses the different types and uses of data models what they are good for, what the differences are between them. The author then analyzes the needs of an integration architecture and the special requirements it puts on a data model.

Data models and data profiling are complementary techniques. Although data models do not tell us the whole truth, profiling the database does not provide the whole truth either. In fact, both may be misleading. However, used together, they can provide better insight into the data.