Modeling Unstructured Data: An Independent Perspective by Steve Hoberman, Data Modeling Expert by Sybase, Inc.

This white paper explains structured, semi-structured, and unstructured data, while increasing your awareness of the complex content of the requirements environment.

Structured data is data whose structure can be understood by some external mechanism by exclusively looking at the meta data. Each structured data element must have an atomic data type and therefore have a name that ends in any class word except for ‘Object' and ‘Text'. For example, Gross Sales Amount ends in the atomic class word ‘Amount' and is therefore structured. It is important to note that structured data has nothing to do with whether the data can be physically stored in a database.
Semi-structured data is data whose structure can be understood by some external mechanism by looking at the data. As with structured data, it also must be one of the atomic data types. A column heading in Excel for example, is typically a semi-structured data element. The only difference between structured and semi-structured is that with semi-structured the values can only be understood by examining the contents instead of just the meta data. Semi-structured data is one small step away from structured data, and has the same inherent characteristics of structured data.
Unstructured data is data whose structure cannot be understood by some external mechanism by looking at the meta data or content. Examples of unstructured data are documents, music, and images. There is substantially more unstructured data than structured data. The two class words ‘Object' and ‘Text' are unstructured data types.