Data modeling has always been a task that seems positioned in the middle of a white-water rapids with a paddle but no canoe. On one side of the data modeling rapids are the raging agilists who are demanding working software and decrying virtually all documentation. To this agilists’ group, data modeling is often seen as too simple to matter. But at the same time, their implementations will miss standardization in naming or data model patterns. And results may be so far off course that major rework is unavoidable. Sadly, far too many agile practices have been set up to place things under the technical debt umbrella, when in reality those practices never allow the re-factoring closet door to be opened. Poor data models are “overcome” by creating ever more complex logic around the data in order to get to a more proper result, as developers learn what really needs to be accomplished along the way, maybe. The results may work but can be a nightmare to maintain.
