In the second of two articles, the authors describe how to develop business patterns based on business functions, data, and business components, and show how these can be used to engineer software systems.

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

This paper presents an approach to enterprise data architecture that doesn’t require total buy-in by top management but can still deliver on the basic goal: improving the quality of shared data.

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

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.

XML is a perfectly good vehicle for describing data to be transmitted from one place to another. It is not so good for describing the semantics – the nature of – the underlying data. It cannot replace data modeling and sound database design. XML, data modeling, and database design are all ways to structure data. Each has its place. Unfortunately, our industry is somewhat confused as to what those places are. This article attempts to sort that out.

There has been much discussion about “atomic” data, but what about “molecular” data? This concept encompasses not only more concrete data specifications, but also more abstract function specifications, than conventional application development techniques.

This article explains the different types of keys, and how each type plays a different role within the database.

This article offers a step by step approach to transform a UML object-oriented class model in a purely relational database.

This is a proposal how to name and describe data models, where we relate to the various rows in Zachman Framework.