Foreword
When I first became involved in data modeling in the mid-1970s, I was taught a set of diagramming conventions, the rules of normalization, and a few principles of good design. It did not take me long to discover that my education had covered only the easy part. The real challenge, as any experienced modeler knows, lies in understanding business requirements and choosing an appropriate set of concepts and structures to support them. The traditional advice to “ask which things the enterprise needs to keep information about and how they are related” is a gross over-simplification of the often very difficult process of identifying entities and relationships.
Research in the last few years has supported what practitioners have known for a long time: rather than modeling from first principles, experienced data modelers re-use and adapt models and parts of models from their previous work. In fact, their “experience” may well reside more in their personal library of models–typically remembered rather than documented–than in greater facility with the basic techniques. The use of pre-existing templates also changes the nature of the dialog between the business experts and modelers: modelers will seek to discover which model or models from their repertoire may be appropriate to the situation, then to check the detail of those models. This is a far more proactive role for modelers than that traditionally described, and recognizes that both parties can contribute ideas and content ...