Watson, Richard T. “The Essential Skills of Data Modeling.” Journal of Information Systems Education 2006 (17) 1. 39-42.

  • The authors notes that after 20 years teaching data modeling, the essential issue “isn’t how to represent entities and relationships. The essential skill is learning to identify entities and the correct relationships among them” (39). In essence, the relatively transparent skill of modeling isn’t difficult to teach; rather, the rhetorical skills of knowing what entities and relationships are important are at the core of successful data modeling practices. As the author notes, “Data modeling is a higher-level skill than drawing a diagram” (ibid.).
  • Some typical errors in non-functioning models include: 1) not recognizing that an attribute is an entity; 2) failing to generalize several entities as a single entity; 3) not reading a relationship both ways and thus making a cardinality mistake; 4) ignoring exceptions that result in a failure to represent reality (39). [1. It is important to note here that the author identifies all four of these issues as a failure to understand domain knowledge and interpret domain knowledge appropriately. In other words, this is a rhetorical problem inasmuch as there’s slippage between the representations of reality through language in the model form. That being said, the author disagrees, noting that “reality is far more important than representation . . . . Even if there were a single best representation . . . it is worthless if the resulting data model does not capture the domain of interest’s reality.”]
  • On the rhetorical nature of data modeling, “I don’t believe that one can argue that one data modeling dialect is superior to another without considering the context in which it is used or the tools available” (39).

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