||Early in the development of information management technologies, two quality measures, performance and storage, emerged from the rest because of their dramatic affect on the cost over the life cycle of the database. Recent trends in hardware technologies have shown that these measures have become less important because their associated costs have dropped dramatically. However, the database is the pivotal artifact of the information system. Because of the complexity and size of these systems, the design phase of the database has become extremely important. It is important to get it correct, as a poorly designed data model can lead to a poor database and poor costs and performance for decades to come. Design is the most important aspect in the development of an information management system. Among the various characteristics that are used as quality measures for a data model, in our research, we explore the concept of semantic validity and the characteristics that are important for a good design and those characteristics that affect the semantic validity of a data model. Semantic validity of the data model is as important as structural validity. In this research work, we address factors affecting semantic validity of data modeling and discuss the design quality measures that have an impact on the usability and life cycle cost of the information management system. Although there are many factors affecting semantic validity, we believe Completeness, Correctness and Consistency of functional semantics are very important in the design of the database. This research work elaborates on the design issues related to these three key characteristics. We also address quality factors which leads the design to a high quality data model. We emphasize the fact that a good design is the one that satisfies all the key functional characteristics and strikes a balance between all the quality factors. The contribution of this research is to provide a set of guidelines which a designer can use to design and model a structurally and semantically valid data model. The important design issues in each of those concepts are elucidated and possible guidelines are presented to assist the readers in design development.