The data model is a valuable enterprise tool. In addition to helping enterprises understand information assets and analyze data requirements, data models support decision-making, enable information sharing, guide data quality initiatives and form the cornerstones of an enterprise’s data architecture. In fact, data models are the Holy Grail for the business and technical community.  A quality data architecture is the soul of an organization and must clearly depict how information flows through the enterprise and how it is used. The key component of a data architecture is the data model, which is typically a graphical representation of data structure.  Thousands of data models are being built these days. But for every good data model, there are a dozen bad ones. Existing efforts focus on completing the task at hand – building an adequate model – rather than spending time validating the effort. Imagine a mission-critical system deployed without testing. If we agree that a robust data architecture developed and maintained through data modeling efforts is the backbone of the information enterprise, then why aren’t we spending 25 to 30 percent of our effort in a quality assurance review? The impact of forgoing this step is grave.

Generally, quality is addressed relative to fitness for purpose. For quality data models, we talk about two concepts, completeness and correctness, from two perspectives, conceptual and technical. We document data models using what is essentially a language. Using its syntax correctly and making sure the model is an accurate and complete representation of the concept we are representing yields a quality result. These concepts and perspectives shape our quality framework:

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