Structured data refers to any data that resides in a fixed field within a record or file. This includes data contained in relational databases and spreadsheets.
Characteristics of Structured Data
Structured data first depends on creating a data model – a model of the types of business data that will be recorded and how they will be stored, processed and accessed. This includes defining what fields of data will be stored and how that data will be stored: data type (numeric, currency, alphabetic, name, date, address) and any restrictions on the data input (number of characters; restricted to certain terms such as Mr., Ms. or Dr.; M or F).
Structured data has the advantage of being easily entered, stored, queried and analyzed. At one time, because of the high cost and performance limitations of storage, memory and processing, relational databases and spreadsheets using structured data were the only way to effectively manage data. Anything that couldn’t fit into a tightly organized structure would have to be stored on paper in a filing cabinet.
Managing Structured Data
Structured data is often managed using Structured Query Language (SQL) – a programming language created for managing and querying data in relational database management systems. Originally developed by IBM in the early 1970s and later developed commercially by Relational Software, Inc. (now Oracle Corporation).
Structured data was a huge improvement over strictly paper-based unstructured systems, but life doesn’t always fit into neat little boxes. As a result, the structured data always had to be supplemented by paper or microfilm storage. As technology performance has continued to improve, and prices have dropped, it was possible to bring into computing systems unstructured and semi-structured data.
Unstructured and Semi-Structured Data
Unstructured data is all those things that can’t be so readily classified and fit into a neat box: photos and graphic images, videos, streaming instrument data, webpages, PDF files, PowerPoint presentations, emails, blog entries, wikis and word processing documents.
Semi-structured data is a cross between the two. It is a type of structured data, but lacks the strict data model structure. With semi-structured data, tags or other types of markers are used to identify certain elements within the data, but the data doesn’t have a rigid structure. For example, word processing software now can include metadata showing the author’s name and the date created, with the bulk of the document just being unstructured text. Emails have the sender, recipient, date, time and other fixed fields added to the unstructured data of the email message content and any attachments. Photos or other graphics can be tagged with keywords such as the creator, date, location and keywords, making it possible to organize and locate graphics. XML and other markup languages are often used to manage semi-structured data.
Structured Data Technology Standards
SQL has been a standard of the American National Standards Institute since 1986. It is managed by InterNational Committee for Information Technology Standards (INCITS) Technical Committee DM 32 – Data Management and Interchange. The committee has two task groups, one for databases and the other for metadata. HP, CA, IBM, Microsoft, Oracle, Sybase (SAP) and Teradata all participate, as well as several federal government agencies. Both of the committee project documents have links to further information on each project. SQL became an International Organization for Standards (ISO) standard in 1987. The published standards are available for purchase from the ANSI eStandards Store, under the INCITS/ISO/IEC 9075 classification.
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.
Examples of Unstructured Data
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.
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, business intelligence 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 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.”
Many industry watchers say that Hadoop has become the de facto industry standard for managing Big Data. This open source project is managed by the Apache Software Foundation.