Data modeling has been with us for several decades now and has been wildly successful. It has provided IT professionals with a set of tools, techniques and methodologies that have not only allowed us to implement databases, but which have also given us a way to communicate and discuss data architecture. So pervasive is this success that today data modeling is often taken for granted. It looks mature and rather static. Entry-level staff have to learn it, and experienced professionals may need to extend their skill sets into advanced areas, such as dimensional modeling. Yet even if there is a need for individual growth, there seems to be an unspoken assumption that the body of knowledge built up around data modeling is all that is needed to implement successful databases. Few people seem to question if this is really so. Is it possible to entertain the thought that data modeling does not completely encompass everything that is needed for successful database design? I would submit that there really are limits to what data modeling - at least in its present form - can achieve, and that in practice it cannot fully describe the architecture of any database.
Code Tables, Indicators and Nulls
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