Database model basics: Naming conventions and structure{3}

by Bernard T
When it comes to designing databases it’s always a good idea to follow some sort of rigid naming convention. A little thought in the early design phases should save a lot of time in the long run and with the maintenance of the finished system. In the article I read about Data Modeling the author suggested that every IT professional should have a basic understanding of data modeling in order to gain some insight about the activities performed by agile databases. This understanding should also lead to an appreciation of what agile databases do and why they are such a critical part of any successful business. The author mentions that,  “data models can be used for a variety of purposes, from high-level conceptual models to physical data models”. He mentions that there are three basic types or styles of data modeling that people are most likely to encounter; Conceptual, Logical (LDMs) and Physical data models (PDMs). The article describes these three types in detail but basically says the each model has its own goal that it addresses and is a step driven system that must follow this convention. It emphasized that having a strict but logical data naming convention is key to having a successful Database model. The article emphasizes that in order to be successful we must make logical yet simple decisions in our naming conventions and data model structures, especially in the beginning stages. We have been learning this in class and after reading a few articles and journals about this, they are all in agreement that this one of the easiest yet important concepts that Database professionals should learn and is a solid foundation for anyone getting into database modeling and design. Naming conventions and structure logic is one first things we will learn and although one of the basics of Database design it is something that we will use and must perfect so that it comes second nature to us, this after all is something we will use all throughout our career.


Scott, W. Ambler. (2011). Data Modeling 101. Techniques for Successful Evolutionary/Agile Database Development. Retrieved 01/14/2012 from ml.