Data Strategy for the Federal Public Service - Annexes

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The following is an evergreen list of terms and complementary strategies for the 2023-2026 Data Strategy for the Federal Public Service.

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.

Data flow

The circulation or movement of computerised data and information through interoperable systems and across organizations, geopolitical regions or jurisdictions.

Data portability

The capacity of digital data and information to be transmitted or circulated through interoperable applications or systems and across organizations 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.

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 .

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.

Data quality

The ‘quality’ of data refers to its fitness for purpose, often measured by such criteria offered in the bullet below. 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.

• 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.

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.

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.

Data steward

The role(s) accountable for the management of data assets and resources from a strategic perspective. Data stewards are responsible for ensuring that the data acquisition, entry, quality, interoperability, and overall management supports organization's needs, while also ensuring adherence to social license, legislative, and regulatory requirements. They work with stakeholders and other deliberative or advisory bodies to develop definitions, standards and data controls, and perform key functions in the ideation and implementation of data policies that are scalable, sustainable, and significant.

Domain steward

(Also called domain lead, subject area steward, data domain steward, or business data steward) A role within a data stewardship program, which is accountable for a particular data domain. The domain steward is the leader of the domain’s stewardship team, will represent their domain on various data stewardship committees or data governance councils, and will help define, implement, and enforce data management policies and procedures within their specific Data Domain. Domain stewards are essential to a successful data governance program. Employing domain stewardship and domain data stewards is a way to govern data across functional areas of the enterprise.

Data stewardship

Data stewardship is a discipline that directs and supports the ethical and responsible creation, collection, management, use, and reuse of data, and is applicable at all scales – from the national or data system level, to the organization or enterprise level, or to the individual or dataset. Data stewardship programs and processes are formalized through repeatable and automated business processes, established roles and accountabilities, and the use of metrics and audits in order to continuously improve data quality. Data stewardship operations influence proactive and responsible data practice to help deliver data strategies, maintain trust, and promote accountability, and it is enabled though good data governance and data management, which provide oversight of data assets throughout their lifecycle to ensure their proper care.

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.

Interoperability

Interoperability is the ability to access, process and exchange data from multiple sources, then integrate that data for mapping, visualization, and other forms of meaningful representation and analysis. This allows systems and organizations to work together (inter-operate) towards mutually beneficial goals by sharing information and exchanging data. In order to be interoperable, data should follow established data standards to ensure that it is easily compared over time, across jurisdictions, and within and between departments. There are five key layers of interoperability:

1. Legal interoperability is about ensuring that organizations operating under different policies and strategies are able to work together.
2. Operational interoperability is about ensuring how organizations align their business processes, responsibilities and expectations to achieve mutual beneficial outcomes.
3. Semantic interoperability is about ensuring consistent meaning and optimal comparability of data with the use of conceptual models, vocabularies and ontologies.
4. Syntactic interoperability is about format. It allows us to explicitly define the common representations and exchange models.
5. System interoperability is about defining the infrastructure and communication protocols to be used during the exchange process.

Privacy

Privacy describes the degree of protection and confidentiality that personal information and data will be accorded. For Canadian federal institutions, privacy requirements regulate the creation, collection, use, disclosure, protection, retention and disposal of personal information. Privacy can include guiding principles such as accountability, transparency, security, openness, and the rights to redress and to access one’s own personal information.

Domain-Specific Strategies

The following is a list of domain-specific strategies that complement the renewed Data Strategy for the federal public service. Additional strategies will be added as they are published.

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 through federal/provincial/territorial co-development of the strategy, which is informed by the latest research findings, public health and data experts, and an Expert Advisory Group to provide guidance as the work evolves.