Data Strategy for the Federal Public Service - Annexes

The following is an evergreen list of terms and complementary strategies.

Glossary of Terms

The following definitions are intended to support a common understanding of key terminology when reading the 2023-2026 Data Strategy for the Federal Public Service. They are intended to be a source of collaboration and knowledge sharing and are not official policy definitions.

1. Data flow: The circulation or movement of computerised data and information through interoperable systems and across organisations, geopolitical regions or jurisdictions.
2. Data portability: The capacity of digital data and information to be transmitted or circulated through interoperable applications or systems and across organisations or geopolitical regions. Data portability enables data subjects to have clear and manageable access to their personal data, which they have provided to a controller in a structured, commonly used, machine-readable and interoperable format, and are free to transfer it to another controller without undue burden.
3. Data governance: A system of decision rights and accountabilities, responsibilities and rules for the management of the availability, usability, integrity and security of the data and information to enable coherent implementation and co-ordination of data stewardship activities as well as increase the capacity (technical or otherwise) to better control the data value chain, and the resulting regulations, policies and frameworks that provide enforcement. This includes the systems within an enterprise, organization or government that define who has authority and control over data assets and how those data assets may be used, as well as the people, processes, tools and technologies required to manage and protect data assets .
4. Data management: A discipline that directs and supports effective and efficient management of information and data in an organization or public administration, from planning and systems development to disposal or long-term preservation. Data management involves the development, execution, and supervision of plans, policies, practices, concepts, programs, and the accompanying range of systems that contribute to the organizational or governmental mandates and to public good, as well as the maintenance of data processes to meet ongoing information lifecycle needs. It enables the delivery, control, protection, and enhancement of the value of data and information assets through integrated, user-based approaches. Key components of data lifecycle management include a searchable data inventory, reference and master data management, and a quality assessment framework.
5. Data quality: The ‘quality’ of data refers to its fitness for purpose, often measured by such criteria offered below (5a). Data quality assurance measures are used to assess and improve the quality of data. Quality assurance measures planning, implementation, and control of activities that apply quality management techniques to data (whether statistical, administrative, or otherwise) and the statistical production process, to assure data is fit for purpose, which means that it is both usable and relevant in a primary or other use-context, and meets the needs of data users. Different users may have different needs that must be balanced. a) Many organizations – within Canada and internationally – have a set of criteria defining data quality. These often include concepts such as: relevance, reliability, consistency, credibility, completeness, accuracy, timeliness, accessibility, comparability, interpretability, coherence, and proportionality, which all contribute to the data and information’s overall quality and value.
6. Data security: The definition, planning, development, and execution of security policies and procedures used to provide proper authentication, authorization, access, and auditing of data and information assets. Data security enables the protection of privacy, confidentiality, and integrity, as well as the maintenance of trust and social license to operate.
7. Data standards: Data standards are the rules and specifications by which data are described, defined and recorded. In order to share, exchange, and understand data, standardized formats and meanings are needed. Examples of data standards include data models, reference data, identifier schemas, and statistical standards. The use of data standards enables the integration of data over time and across different data sources, as well as reduces the resource requirements associated with many aspects of survey development and maintenance.

1. FAIR Data Principles: Set of data principles, which define characteristics that modern data resources, tools, vocabularies and infrastructures should demonstrate to facilitate the discovery and reuse of data by other parties. FAIR stands for:
   • F - Findable and easily searchable
   • A - Accessible and easy to use
   • I - Interoperable and more easily interpretable
   • R - Re-usable data that is easy to share and use.

Domain-Specific Strategies

Pan-Canadian Health Data Strategy - the strategy aims to support the effective creation, exchange, and use of health data for the benefit of Canadians and the public health systems they rely on. A collaborative approach to develop and deliver the strategy is being taken - federal/provincial/territorial co-development of the strategy is informed by the latest research findings, public health and data experts, and an Expert Advisory Group to provide guidance as the work evolves.