Best Practices - Datatype Selection while designing tables

• If your database is to support web-based application better to go for UNICODE (Unicode like nchar, nvarchar 2 bytes per char where as ASCII datatypes takes 1 bytes per char) datatypes because you may be going to support different types of clients.
• If your application is multi-lingual go for UNICODE.
• If you are planning to include CLRDatatype (SQL Server 2005) in the database go for UNICODE Datatypes instead of ASCII Datatypes, because, if CLRDatatype is going to consume the data then it must be in UNICODE.
• For numeric column, find the range that column is going to have and then choose the datatype. For eg. If you are sure that the column cannot have more than 255 like DepartmentID in a small organization may not go probably beyond 10 or 20. In such cases it is recommended to choose TinyINT datatype. Generally keeping all integer columns type INT without analyzing the range that going to support is not at all recommended from storage perspective.
• Description /Comments /Remarks sort of columns may or may not have data for all the rows. So it is better to go for Variable datatypes like Varchar ,Nvarchar.
• If you know the column is not nullable and it may contain more or less the same size of the data then for sure go for Fixed datatype like CHAR or NCHAR. Having said that it is important to know that, if you select fixed datatypes and if the column is nullable then if you donot have any data (null) then also the column will consume the space.
• If the size of the column is less than 10 char , use fixed width datatypes like NCHAR or CHAR.
• I have seen in many applications use Decimal to store currency kind of data though the application need less precision which can be supported by money. So, my point is, use Money datatype if you need only 4 precision.
• Use UniqueIdentitifier column as PK and CI or so only when it is unavoidable because UniqueIdentitifier takes 16 Bytes of the space.
Note : The point I want to make here is, if you do a proper analysis of the data and then select the datatype then you can control the row, page, table size and hence increase the performance.