During the information systems development process within an organization, data resource is typically analyzed in the form of a data model. During this data analysis phase, the data model is further refined so that it obeys certain rules of good behavior. Normalization is the process of grouping data into such well refined structures. Determining an appropriate normal form has not been clear to database systems analysts. This paper proposes an effective methodology for determining normal forms by employing a cost/benefit model coupled with a decision tree. Three primary variables that impact the benefits and costs of normalization are addressed. The resulting cost/benefit analysis enables database analysts to produce more cost-effective normalized databases.