What is Structured Data?
Structured data – clearly defined data with easily searchable patterns.
It refers to any data that resides in a fixed field within a record or file. This includes data contained in relational databases and spreadsheets.
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).
It 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.
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).
It 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.
SQL has been a standard of the American National Standards Institute since 1986. It is managed by the 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.