Seven common Data Modeling mistakes to watch out for

by Kevin Q
The article briefly describes the benefits of data modeling and how it helps a company and its projects. It then immediately jumps into the seven common mistakes that occur. The First mistake is thinking that the data model is a final structure. This kind of thinking is incorrect, since the data model should be thought of more as a version that can be updated due to new changes. The second mistake is have data models invisible. Data models should be easy to access, be clear and understandable and organized so that it can be used. Having an invisible model defeats the whole purpose of data modeling. The third mistake is assuming business users can read data models. Whether it is assumed by the data model creators or the employees, all that is needed is training to be able to understand the models. It’s important that everyone be able to read and understand the models so that they can make business decisions with helpful information. The fourth mistake is thinking that data models are only used for databases. Data models can go beyond databases to explain and show physical procedures that take place in a company. This can be really helpful in showing the nature of a certain area within the company, so that a well-informed decision can be made. The fith mistake thinking of the data model only as an early deliverable. The data model should be thorough and used as a guide when entering the implementation phase and not just a early deliverable. The sixth mistake creating and overall bland data model. By creating a more colorful and somewhat exciting data model with plenty of comments and guides to help the users, you can keep people engaged and impressed. The seventh mistake think of the data model as your own and not the company’s. They should be available and shared to everyone within the company for viewing, otherwise they will not help the company if only you or a small group of people are the only ones allowed to view it.

As we went over data modeling in class we were taught how it helped companies. This article was a good insight into how everyone in the company can really benefit from being able to view and understand a data model. Company projects will go a lot smoother if the data model is overall well done, clear, and available to everyone so that the implementation phase doesn’t get too hectic. This article broke down seven common mistakes that occur related to the creation, interpretation and use of data models. It was overall a good and interesting read.

Source:  Lopez, Karen & Nixon, Kamille. Enterprise Data Modeling: 7 Mistakes You Can’t Afford to Make. Information May 2, 2011. “”

3 thoughts on “Seven common Data Modeling mistakes to watch out for”

  1. I really enjoyed these tips and how they related to business practices in the industry. Looking at how to apply what we learn to the real world, this article really hits it spot on. This article gives us insight to how business can use data modeling to improve their companies and how it can even hurt their companies. Seeing these tips pushes me to want to make sure that I learn the proper uses for the skills we are taught in class. I can’t wait to see the opportunities presented to us in the future for this field.

  2. At first, I thought it was going to be just another Database model mistakes. But as I continued reading, I realized that I didn’t really have some of those tips in my mind. It made it more easier for me to understand what real data modeling is all about. Good article!

  3. Very useful tips, I think all of the mistakes listed come from limited thinking – for instance, the first mistake is a matter of limiting the data model to a static structure rather than a dynamic model for design, the third is assuming all people are fairly familiar at concepts they could plausibly have never seen before, and the sixth is limiting our view of data models to a very bland palette – however, all suffer from the same narrow thought process. Keeping a very broad system of thinking seems to be a strong indicator of success in every field, and this article reveals that it holds true for the CIS world as well.

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