New data modelers often see things as black and white. But rather than being concrete flooring beneath our feet, knowledge is more like a gossamer web that builds up layer upon layer to provide the effect of a solid foundation. We may think we know facts, such as one plus one equals two, or Columbus discovered America in 1492. What we have come to know about our world comprises our own internal knowledge base. We gain much of this knowledge because it has been passed onto us by others, people with experience, parents, educators, clever friends, verbally or in print. But how do we know such items are real, actual, absolute bona fide beyond-a-shadow-of-a-doubt truths? What if those upon whom we have depended for this information are less than correct? Sadly, while they may be wrong at times we simply are not aware of it, and in truth, right or wrong is not really a cause for concern. Whether we admit it or not, these knowledge bases, rather than being dry and boring collections of facts, are in reality our beliefs. These knowledge bases serve to support our views regarding reality, even when those views may have frayed edges. Users and subject matter experts participating in a data modeling session are no different.
Within an organization, the knowledge of how the business works and what relationships exist between data and process are just as much of a belief system. If everything truly was guaranteed to be the same unswerving facts for one and all, there would be little need for quite so many data modelers. One data model would build on another and, sooner or later, the universe of collected industry knowledge would be covered, and a single model would effectively serve everyone. Database designers would all need to move onto other pursuits.
What we see as truth is what we choose to believe as truth and almost every entity has a few unique turns and twists within the alley ways of their convictions. Often we loose sight of this more fluid aspect to reality. Many problems arise over assumptions that there is one single truth in which everyone must participate. That is the nature of humanity. And while this may sound scary, things-even business things-are based on beliefs that are truth within their native contexts. While some beliefs may qualify as transcending across many entities, those certainties are few. Differing organizations, even within a single industry, have varying beliefs about themselves and the environment in which they do business. The full panorama of beliefs for any specific organization has been cobbled together by the efforts of all the past and present leaders and opinion makers. This organic variety is one of the many reasons that actual data models vary.
Database designers embrace the exploration of new and varied belief systems as they move from one organization into another. The standards used in developing a model are based on the belief system at hand. Consistency within beliefs is more important than a search for some quixotic absolute truth. In working through the process of building a specific data model, the designer must be vigilant in searching for areas of cognitive dissonance, where beliefs may contradict each other. For it is in these contrary areas that cracks may appear, which can diminish the effectiveness of the solution at hand. When consistency is weak, effective data modelers discuss with stakeholders the implications of these logical flaws and highlight their likely impacts. Focusing debate and discussion on these critical areas allows for exploring enhanced solutions. Success in negotiations of this nature leads directly to success when databases are implemented using these data models.