Most organizations classify all data as either structured or unstructured. As the name suggests, structured data has the advantages of being organized and founded for quick queries via relatively simple search methods. Unstructured data has no inherent structure (although it may be “loosely structured”) and often defies attempts to provide easy search results.
Structured data is suitable for easy analysis due to their organization and homogeneous information. Examples include many spreadsheets and all relational databases, as both can be searched for type and thus easily and quickly present information to the user. All data is directly related to each other and relational database management systems (RDBMS) are optimized to answer user queries about the information.
Unstructured data contains little or no identifiable structure, usually due to the different data. The business community estimates that 80% of all useful business data rests in an unstructured state. An email provides an example. While email messages are sometimes organized in a database, the actual content of the message is not. It is possible to organize a series of emails by sender, data, etc., but it is not possible to query their content.
All unstructured data can be classified as either bitmap or text objects. Bitmap objects include all data that is not language-based, such as video, audio, and photos, while text objects are based on writing languages typically found in word processing files and emails, among others. To be fair, the term “unstructured data” may be something of an error number, since much of it may actually correspond to “semi-structured data”, which nonetheless does not readily cooperate with an RDBMS.
The challenge of extracting unstructured data lies both in its potential for size and their lack of identifiable structure. RDBMSs cannot present the data in any meaningful form, so the desire to make unstructured data usable led to platforms like Hadoop and Cloudera. “Big Data” and unstructured data are not synonymous terms, but Big Data is almost always unstructured. If a company like Google or Facebook needs a way to analyze users’ browsing habits or advertising information, they use a distributed database system (DDBMS) to do so. These DDBMSs can spread the extensive data over a network spanning thousands of computers; they can also distribute the workload arising from a query for this information across the same machines. It is possible to use other methods to analyze unstructured data; some of which include Google Refine, Firefox Firebug (for Flash sites) and PDF parsing for Ruby scripting.
As the world goes deeper into the information age, the amount of Big Data demand is likely to grow. As Big Data is unstructured or at best semi-structured, companies will continue to seek effective methods for collecting, storing and presenting meaningful analysis of data too large and too unfocused for traditional database management systems.