Unstructured data sources are data sources missing a logical data model. Whenever you find a data source where no particular logic and structure is defined to collect, store, and expose it, you are dealing with an unstructured data source. The best example we can provide of an unstructured data is a document full of words.

Within the document, you can actually find a lot of information. Nevertheless, that information is in some way disseminated within the whole document and there is no clear structure defining where each bit of information is stored.

As we will see in Chapter 12, Looking for the Culprit – Text Data Mining with R, there are some specific data modeling techniques that modelling extract valuable information from this kind of data, and even derive structured data from unstructured data. This kind of analysis is increasingly becoming more of interest, especially for companies, which are now able to analyze comments and feedback related to their products and derive synthetic statistics from them. This is the case with so-called social media listening, where companies catch different kinds of text on social media channels, subsequently analyzing them in order to get valuable information about their competitive position and the one of the competitors.