Annex A — Definitions
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 movement of data across business processes, locations, business roles, and technical components including databases, applications, platforms, and networks. Data flows are used to describe where data originates, where it is stored and used, and how it moves between processes and systems.
Figure 7: Graph breakdown
The data lifecycle includes planning for data needs, designing and enabling data collection and management, creating and obtaining data, storing and maintaining data, using data, enhancing data, and disposing of data that is no longer required.
- Plan: Identifying what data the business needs, and planning for its capture, storage, and use;
- Design and Enable: Designing the processes and systems to capture, manage, and govern data;
- Create and/or Obtain: Creating data through operational processes, or obtaining the data through data exchange or acquisition from another organization;
- Store and Maintain: Processing data (e.g. integration, warehousing, scrubbing) and storing data;
- Use: Using data to support the organization’s objectives;
- Enhance: Adding new data to existing data to support new requirements; and
- Dispose: Archiving data that is not currently being used, and purging data that is no longer required to minimize the consumption of resources.
Literacy broadly means having competency in a particular area. Data literacy includes the skills necessary to discover and access data, manipulate data, evaluate data quality, conduct analysis using data, interpret results of analyses, and understand the ethics of using data.
Data managementFootnote 2 is the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data assets throughout its lifecycle. This includes the following knowledge areas:
- Data architecture: identifying the data needs of the enterprise (regardless of structure), and designing and maintaining the master blueprints to meet those needs. It is used to align data investments with business strategy;
- Data modeling and design: discovering, analyzing, and scoping data requirements, and then representing and communicating these data requirements in a data model. Data models can include conceptual, logical, and physical models;
- Data storage and operations: designing, implementing, and supporting stored data to maximize its value;
- Data security: planning, developing, and executing security policies and procedures to provide proper authentication, authorization, access, and auditing of data assets. The goal is to protect data and information assets in alignment with privacy and confidentiality regulations, contractual agreements, and business requirements;
- Data integration and interoperability: moving and consolidating data within and between data stores, applications, and organizations (integration consolidates data into consistent forms, either physical or virtual; interoperability allows multiple systems to communicate);
- Document and content management: controlling the capture, storage, access, and use of data stored outside relational databases;
- Reference and master data management: managing shared data to meet organizational goals, reduce risks associated with data redundancy, ensure higher quality, and reduce the costs of data integration;
- Data warehousing and business intelligence: the planning, implementation, and control processes to provide decision support data and support knowledge workers engaged in reporting, query, and analysis;
- Metadata management: the planning, implementation and control activities to enable access to high quality, integrated metadata;
- Data quality management: the planning, implementation and control activities that apply quality management techniques to data to assure the data are fit for consumption and meets the needs of consumers; and
- Data governance: exercising authority and control (planning, monitoring, and enforcement) over the management of data assets.
Figure 8: Graph breakdown
The DAMA wheel demonstrates the knowledge areas of data management: data architecture, data modeling and design, data storage and operations, data security, data integration and interoperability, document and content management, reference and master data management, data warehousing and business intelligence, metadata management, data quality management, and data governance, which applies to all these areas through the exercise of authority and control over the management of data assets.
A value chain (sometimes known as a value stream) is the set of activities that an organization uses to create value for its stakeholders; at each stage, incremental value is created. For data, the value chain is linked to the business processes that create and use data. A data value chain takes into consideration how data are collected, disseminated, shared, used, and enhanced.
Information is defined as data in context. Data and information are intertwined, and the policies and processes that govern and manage them should be aligned.
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