This chapter suggests that the ways to create a high-quality model while avoiding the stumbling blocks. A high-quality data model requires precision in the choice of such names for objects as entity, attributes, and relationships. An important element contributing to the data modeling process is how effectively the model reflects the business rules. A data model goes beyond an entity-relationship diagram, because it must encompass rigorous definition of data modeling objects-that is, entities, relationships, and attributes. The problems of redundant relationships are not as easy to identify because the modeler is generally reluctant to run the risk of missing significant relationships. The multiple supertype pitfalls is more noticeable when the modeler assigns key and non-key attributes to the entities. Normalization of data is a procedure used to ensure that a data model conforms to some useful standards. A modeler must be aware of when to make an attribute an entity in its own right.