Structured data is data that is divided into standardized pieces that are identifiable and accessible by both humans and computers. The granularity of these pieces can range from an individual data point, such as a number (e.g., revenues), date (e.g., the date of a transaction), or text (e.g., a name), to data that includes multiple individual data points (e.g., an entire section of narrative disclosure). Structured data can be created and communicated using data standards like XBRL, XML, and JSON, or generated with web and pdf forms.
Structured data offers numerous benefits. Widely available software can be used to easily analyze vast amounts of structured data without extensive and burdensome manual processing. This allows investors, analysts, and regulators to access and manipulate data in one disclosure, to compare disclosures across registrants, and to make comparisons against previous disclosures from the same registrant. For example, individual data points can be analyzed to observe trends, or can be combined to create ratios or other derived outcomes. Data structured at a high level of granularity can be useful for text analytics or manual comparisons of narrative disclosures, for instance comparisons to determine how different registrants are describing a particular issue. Software can also be used to enhance the readability of structured data by, for example, providing a standardized interface that links various sections of the disclosure. For these reasons, countries around the world are increasingly using structured data for business reporting, including, for example, the XBRL standard.
Modified: March 26, 2016