Conceptually understanding data models{2}


by Jasmine C
In summary, the article I read reiterates the importance of having a well designed data model.  Without a good data model, then the information in the database based on that model is not going to correspond.  The data model is going to contain the information about the “entities , their associations and attributes within the intended business” (Huang).  As we all know, if the data model is messed-up, the the database that it is based off is not going to have a correct representation of reality.  Something that many people need to understand is that conceptually, data modeling is very easy to mess up.  This is true especially for student database designers.  Cognitively speaking, student designers do not yet contain the ability to effectively understand the reasoning behind data modeling techniques.  Domain knowledge and cognitive fit are two variables that student designers need to understand in order for them to progress to expert designers.  These two variables are essential for conceptual data modeling.

This article relates to class because it just stresses the importance of making sure the we as students understand the need for data models.  Data models can help us understand the business that we are researching and hence, create a better database.  In class, we keep learning about attributes and cardinalities and I believe this is one of the first steps that we as students need to understand in order for us to grasp a better understanding on creating a data model.  If we are able to correctly associates entities, then I think that everything else should come a littler easier.

I like this article because it helped me narrow down my focus. As a student, I should make sure I have the proper knowledge of the business I will use to create my data model and also cognitively, I should try to understand as much as I can about the business.

 

Huang, I. (2011). Identify students’ difficulties on learning conceptual data modeling. Allied Academies International Conference. Academy of Information and Management Sciences. Proceedings15(1), 3-8. Retrieved January 22, 2012, from http://search.proquest.com/docview/873720736/fulltextPDF?source=fedsrch&accountid=10357