A database that is otherwise well-designed but does not perform well is useless. Data integrity is one of the keys to developing a quality database. Without accurate data, users will lose confidence in the database or make uninformed decisions. The identification, interpretation, and application of business rules, however, present a more difficult challenge for the developer. Rules and policies must be communicated and translated and much of the meaning and intent can be lost in this process.
Semantic quality is an important objective of database systems.
Information that represents a high proportionate match between the problem and solution domains should be the goal of a database with high semantic quality. Qualitative techniques address the ambiguous and subjective dimensions of conceptual database design. The interaction between people and information is one where human preference and constraints have a huge impact on the effectiveness of database design . The use of techniques such as affinity and pareto diagrams, semantic object models, nominal group, and interrelationship diagraphs help to define the aspects of business requirements which may be elusive. Quantitative techniques, such as entity-relationship diagrams, object models, data flow diagrams, and performance benchmarks, on the other hand, allow the results of the qualitative techniques to be described in a visual format and measured in a meaningful way.