Common Database Design Errors

Before we begin today’s blog post, wherein I explain some of the more common mistakes that rookies and non-database folks make (heck, even some database folks make mistakes), I first want to unequivocally state that your organization should have a data architecture team that is responsible for logical and conceptual modeling… and your DBA team should work in tandem with the data architects to ensure well-designe databses.

OK, so what if that isn’t your experience? Frankly, it is common for novices to be designing databases these days, so you aren’t alone. But that doesn’t really make things all that much better, does it?

The best advice I can give you is to be aware of design failures that can result in a hostile database. A hostile database is difficult to understand, hard to query, and takes an enormous amount of effort to change.

So with all of that in mind, let’s just dig in and look at some advice on things not to do when you are designing your databases.

Assigning inappropriate table and column names is a common design error made by novices. Database names that are used to store data should be as descriptive as possible to allow the tables and columns to self-document themselves, at least to some extent. Application programmers are notorious for creating database naming problems, such as using screen variable names for columns or coded jumbles of letters and numbers for table names. Use descriptive names!

When pressed for time, some DBAs resort to designing the database with output in mind. This can lead to flaws such as storing numbers in character columns because leading zeroes need to be displayed on reports. This is usually a bad idea with a relational database. It is better to let the database system perform the edit-checking to ensure that only numbers are stored in the column.

If the column is created as a character column, then the developer will need to program edit-checks to validate that only numeric data is stored in the column. It is better in terms of integrity and efficiency to store the data based on its domain. Users and programmers can format the data for display instead of forcing the data into display mode for storage in the database.

Another common database design problem is overstuffing columns. This actually is a normalization issue. Sometimes a single column is used for convenience to store what should be two or three columns. Such design flaws are introduced when the DBA does not analyze the data for patterns and relationships. An example of overstuffing would be storing a person’s name in a single column instead of capturing first name, middle initial, and last name as individual columns.

Poorly designed keys can wreck the usability of a database. A primary key should be nonvolatile because changing the value of the primary key can be very expensive. When you change a primary key value you have to ripple through foreign keys to cascade the changes into the child table.

A common design flaw is using Social Security number for the primary key of a personnel or customer table. This is a flaw for several reasons, two of which are: 1) a social security number is not necessarily unique and 2) if your business expands outside the USA, no one will have a social security number to use, so then what do you store as the primary key?

Actually, failing to account for international issues can have greater repercussions. For example, when storing addresses, how do you define zip code? Zip code is USA code but many countries have similar codes, though they are not necessarily numeric. And state is a USA concept, too.

Of course, some other countries have states or similar concepts (Canadian provinces). So just how do you create all of the address columns to assure that you capture all of the information for every person to be stored in the table regardless of country? The answer, of course, is to conduct proper data modeling and database design.

Denormalization of the physical database is a design option but it can only be done if the design was first normalized. How do you denormalize something that was not first normalized? Actually, a more fundamental problem with database design is improper normalization. By focusing on normalization, data modeling and database design, you can avoid creating a hostile database.

Without proper upfront analysis and design, the database is unlikely to be flexible enough to easily support the changing requirements of the user. With sufficient preparation, flexibility can be designed into the database to support the user’s anticipated changes. Of course, if you don’t take the time during the design phase to ask the users about their anticipated future needs, you cannot create the database with those needs in mind.


Of course, these are just a few of the more common database design mistakes. Can you name more? If so, please discuss your thoughts and experiences in the comments section.


I'm a strategist, researcher, and consultant with nearly three decades of experience in all facets of database systems development.
This entry was posted in data, data modeling, database design, DBA and tagged . Bookmark the permalink.

2 Responses to Common Database Design Errors

  1. doug1943 says:

    As a mistake, how about using Autoincrement or its equivalent, when there is a perfectly good Primary Key? With a proper Primary Key, you cannot enter contradictory records for the same entity. You lose that protection when you make Autoincrement the Primary Key, unless you designate your ‘real’ Primary Key as Unique.

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