The phrase "unstructured data" usually refers to information that doesn't reside in a traditional row-column database. As you might expect, it's the opposite of structured data -- the data stored in fields in a database.
Unstructured data files often include text and multimedia content. Examples include e-mail messages, word processing documents, videos, photos, audio files, presentations, webpages and many other kinds of business documents. Note that while these sorts of files may have an internal structure, they are still considered "unstructured" because the data they contain doesn't fit neatly in a database.
Experts estimate that 80 to 90 percent of the data in any organization is unstructured. And the amount of unstructured data in enterprises is growing significantly -- often many times faster than structured databases are growing.
Mining Unstructured Data
Many organizations believe that their unstructured data stores include information that could help them make better business decisions. Unfortunately, it's often very difficult to analyze unstructured data. To help with the problem, organizations have turned to a number of different software solutions designed to search unstructured data and extract important information. The primary benefit of these tools is the ability to glean actionable information that can help a business succeed in a competitive environment.
Because the volume of unstructured data is growing so rapidly, many enterprises also turn to technological solutions to help them better manage and store their unstructured data. These can include hardware or software solutions that enable them to make the most efficient use of their available storage space.
Unstructured Data and 'Big Data'
As mentioned above, unstructured data is the opposite of structured data. Structured data generally resides in a relational database, and as a result, it is sometimes called "relational data." This type of data can be easily mapped into pre-designed fields. For example, a database designer may set up fields for phone numbers, zip codes and credit card numbers that accept a certain number of digits. Structured data has been or can be placed in fields like these. By contrast, unstructured data is not relational and doesn't fit into these sorts of pre-defined data models.
In addition to structured and unstructured data, there's also a third category: semi-structured data. Semi-structured data is information that doesn't reside in a relational database but that does have some organizational properties that make it easier to analyze. Examples of semi-structured data might include XML documents and NoSQL databases.
The term "big data" is closely associated with unstructured data. "Big data" refers to extremely large datasets that are difficult to analyze with traditional tools. Big data can include both structured and unstructured data, but IDC estimates that 90 percent of big data is unstructured data. Many of the tools designed to analyze big data can handle unstructured data.
Implementing Unstructured Data Management
Organizations use of variety of different software tools to help them organize and manage unstructured data. These can include the following:
- Big data tools: Software like Hadoop can process stores of both unstructured and structured data that are extremely large, very complex and changing rapidly.
- Business intelligence software: Also known as BI, this is a broad category of analytics, data mining, dashboards and reporting tools that help companies make sense of their structured and unstructured data for the purpose of making better business decisions.
- Data integration tools: These tools combine data from disparate sources so that they can be viewed or analyzed from a single application. They sometimes include the capability to unify structured and unstructured data.
- Document management systems: Also called "enterprise content management systems," a DMS can track, store and share unstructured data that is saved in the form of document files.
- Information management solutions: This type of software tracks structured and unstructured enterprise data throughout its lifecycle.
- Search and indexing tools: These tools retrieve information from unstructured data files such as documents, Web pages and photos.
Unstructured Data Vendors
Numerous vendors offer products designed to help companies analyze and manage their unstructured data. They include the following:
- Hitachi Data Systems
- Parity Computing
- Provalis Research
The open source community has been particularly active in developing software that can manage unstructured data, and many vendors offer paid products and services related to these open source projects. Open source projects and vendors related to the storage, management and analysis of unstructured data include the following:
- Palo BI Suite/Jedox
Unstructured Data Technology
A group called the Organization for the Advancement of Structured Information Standards (OASIS) has published the Unstructured Information Management Architecture (UIMA) standard. The UIMA "defines platform-independent data representations and interfaces for software components or services called analytics, which analyze unstructured information and assign semantics to regions of that unstructured information."
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