Data

The representation of facts as text, numbers, graphics, images, sound, or video (The Department of National Defence and Canadian Armed Forces Data Strategy, 2019)

Data: An object, variable, or piece of information that has the perceived capacity to be collected, stored, and identifiable. It comes largely in two forms: structured and unstructured. Structured data are essentially answers to questions asked by the collector of data, are generally easy to organize and identify and have a strict hierarchy that is not easily manipulated (i.e. responses to a survey organized in a table format and information about people’s years of education and income in a chart). Unstructured data are not readily amenable to automated analysis and often are used in ways that differ from the intended purpose when collected (such as photos, videos, tweets), and do not need to follow a hierarchical method of identification. Data is also used as a policy concept and social phenomena (e.g. “data is changing the world”), or as a shortcut for data ecosystems, Big Data, etc. (Bhargava, et.al., 2015)

As mentioned earlier, the definition of data used in this document is "the representation of facts as text, numbers, graphics, images, sound, or video". This definition of data can be further categorized and typed to include:

Structured data: Data that uses a data model to define relationships between data fields. Within DND/CAF, DRMIS, HRMS are examples of systems that create and use structured data.
Unstructured/semi-structured data: Data that is stored without a data model to understand how it is organized, or its content, such as email, web pages, social media, reports, pictures, and audio. Examples of unstructured or semi-structured data within DND/CAF include "combat camera" images, or Defence Team news videos.
Master data: Data that provides context for the business in the form of common and abstract concepts. For DND/CAF, master data might include employees, materiel, assets, vendors / suppliers, G/L, and cost centres.
Reference data: Data that is used to relate data beyond the boundaries of an organization, or to characterize other data. Examples include province codes, postal codes, and status codes.
Metadata: Often described as data about data, it can include data rules, constraints, concepts, relationships with other data, and many other things. The last updated field, or author field, are common examples of metadata.
Big data: Semi-structured and unstructured data in a wide variety of formats, in large volumes, and produced at high speed. "Big" data, by virtue of their volume, velocity, or variety cannot be easily stored or analyzed with traditional methods. Things like sensors, Internet of Things (IoT) devices, and social media all create "big" data.
Open data: Data that can be freely used, shared and built-on by anyone, anywhere, for any purpose.
Dark data: Data that has been collected, created, processed, and stored but that isn't used to make decisions or derive insight.
Operational data: Data used in an operational setting to address an operational objectiveFootnote 1 .
Corporate data: Data used in an administrative setting to address a legislative, regulated requirement, or as part of an internal process.
Transactional data: Data that describes an event, or a change to an entity.

(The Department of National Defence and Canadian Armed Forces Data Strategy, 2019)