I think two common mistakes are made in modeling data & processes that can lead to unintended complexity. The first is conflating two models that should be separate, and the second is oversimplifying models.Conflated model: when what should be two separate models are intermingled, resulting in confusing (forced) associations. If you have two elements in a hierarchy and you’re not sure which is higher, it could be a sign of conflated models.
Inadequate model: when the model is too simple, resulting in multiple aspects being crammed into one element — making comparison and isolation difficult. If an element has too vague a meaning or has muliple contexts, it may be better modeled in separate elements.
Contexts are a cood indicator of domain areas. Requiring context to understand an element may hint at two or more models.