Changes

; 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 .<ref>Data Governance Institute. (n.d.). ''Governance and Decision Making''. Data Governance Institute. https://datagovernance.com/governance-and-decision-making/  </ref><ref name=":2">Organization for Economic Co-operation and Development (2008). ''OECD Glossary of Statistical Terms'', OECD Publishing, Paris. https://doi.org/10.1787/9789264055087-en.</ref><ref>Organisation for Economic Co-operation and Development (2019). Data Governance in the Public Sector ''In'' ''The Path to Becoming a Data-Driven Public Sector'', OECD Digital Government Studies, OECD Publishing, Paris. https://doi.org/10.1787/059814a7-en.  </ref><ref name=":5">Plotkin, D. (2021). Data Stewardship: An Actionable Guide to Effective Data Management and Data Governance (2^nd Ed.). London, UK: Academic Press.</ref><ref name=":0">Statistics Canada. (2020b). Sta''tistics Canada Data Strategy: Delivering insight through data for a better''  ''Canada'' [PDF]. [https://www.statcan.gc.ca/eng/about/datastrategy/statistics_&#x20;canada_data_strategy.pdf Statistics Canada Data Strategy (statcan.gc.ca)]</ref><ref name=":1">Statistics Canada. (2021b). ''Enterprise Information and Data Management Glossary'' [PDF]. Unpublished internal departmental document.  </ref>

; 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 .<ref>Data Governance Institute. (n.d.). ''Governance and Decision Making''. Data Governance Institute. https://datagovernance.com/governance-and-decision-making/  </ref><ref name=":2">Organization for Economic Co-operation and Development (2008). ''OECD Glossary of Statistical Terms'', OECD Publishing, Paris. https://doi.org/10.1787/9789264055087-en.</ref><ref>Organisation for Economic Co-operation and Development (2019). Data Governance in the Public Sector ''In'' ''The Path to Becoming a Data-Driven Public Sector'', OECD Digital Government Studies, OECD Publishing, Paris. https://doi.org/10.1787/059814a7-en.  </ref><ref name=":5">Plotkin, D. (2021). Data Stewardship: An Actionable Guide to Effective Data Management and Data Governance (2^nd Ed.). London, UK: Academic Press.</ref><ref name=":0">Statistics Canada. (2020b). Sta''tistics Canada Data Strategy: Delivering insight through data for a better''  ''Canada'' [PDF]. [https://www.statcan.gc.ca/eng/about/datastrategy/statistics_&#x20;canada_data_strategy.pdf Statistics Canada Data Strategy (statcan.gc.ca)]</ref><ref name=":1">Statistics Canada. (2021b). ''Enterprise Information and Data Management Glossary'' [PDF]. Unpublished internal departmental document.  </ref>

; 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.<ref name=":0" /><ref name=":1" /><ref name=":3">Data Management Association (DAMA) (2017). DAMA-DMBOK: Data Management Body of Knowledge (2^nd Ed.). Basking Ridge, NJ: Technics Publications.</ref><ref name=":6">Government of Canada, Treasury Board Secretariat. (2019). ''Policy on Service and Digital''. Ottawa, ON: Her Majesty the Queen in Right of Canada. https://www.tbs-sct.canada.ca/pol/doc-eng.aspx?id=32603</ref><ref>Statistics Canada. (2020a). ''Data Literacy Competencies''. Statistics Canada. https://www.statcan.gc.ca/en/wtc/data-literacy/compentencies </ref>

; 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.<ref name=":0" /><ref name=":1" /><ref name=":3">Data Management Association (DAMA) (2017). DAMA-DMBOK: Data Management Body of Knowledge (2^nd Ed.). Basking Ridge, NJ: Technics Publications.</ref><ref name=":6">Government of Canada, Treasury Board Secretariat. (2019). ''Policy on Service and Digital''. Ottawa, ON: Her Majesty the Queen in Right of Canada. https://www.tbs-sct.canada.ca/pol/doc-eng.aspx?id=32603</ref><ref>Statistics Canada. (2020a). ''Data Literacy Competencies''. Statistics Canada. https://www.statcan.gc.ca/en/wtc/data-literacy/compentencies </ref>

;Data quality : The ‘quality’ of data refers to its fitness for purpose, often measured by such criteria offered in the sub 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.

;Data quality : The ‘quality’ of data refers to its fitness for purpose, often measured by such criteria offered 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.<ref>European Commission (2003). Eurostat. ''Assessment of quality in statistics - Definition of Quality in Statistics'', Working Group, Luxembourg, October 2003.</ref><ref>European Commission (2020). Eurostat. Quality assurance framework of the European statistical system: version 2.0, Publications Office, 2020. https://data.europa.eu/doi/10.2785/847733  </ref><ref>Government of Canada. (2022). ''GC Data Quality Framework''.[[GC Data Quality Framework#Background|https://wiki.gccollab.ca/GC_Data_Quality_Framework#Background]]</ref><ref>Organisation for Economic Co-operation and Development (2002). Measuring the Non-Observed Economy: A Handbook. Paris, France: OECD Publications.  </ref><ref>Statistics Canada (2002). ''Statistics Canada’s Quality Assurance Framework''. Ottawa, ON: Minister of Industry. https://www150.statcan.gc.ca/n1/en/pub/12-586-x/12-586-x2002001-eng.pdf?st=QDz6ld3y</ref><ref name=":4">Statistics Canada (2021a). ''Statistics Canada’s Approach to Data Stewardship'' [PDF]. Unpublished internal departmental document. </ref><ref>Wang, R.Y. and Strong, D.M. (1996) ''Beyond Accuracy: What Data Quality Means to Data Consumers''. Journal of Management Information Systems, 12, 5-33.</ref><ref>United Nations Departments of Economic and Social Affairs (2019). ''United Nations National Quality Assurance Frameworks Manual for Official Statistics'' [PDF]. https://desapublications.un.org/file/911/download</ref>

::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.<ref>European Commission (2003). Eurostat. ''Assessment of quality in statistics - Definition of Quality in Statistics'', Working Group, Luxembourg, October 2003.</ref><ref>European Commission (2020). Eurostat. Quality assurance framework of the European statistical system: version 2.0, Publications Office, 2020. https://data.europa.eu/doi/10.2785/847733  </ref><ref>Government of Canada. (2022). ''GC Data Quality Framework''.[[GC Data Quality Framework#Background|https://wiki.gccollab.ca/GC_Data_Quality_Framework#Background]]</ref><ref>Organisation for Economic Co-operation and Development (2002). Measuring the Non-Observed Economy: A Handbook. Paris, France: OECD Publications.  </ref><ref>Statistics Canada (2002). ''Statistics Canada’s Quality Assurance Framework''. Ottawa, ON: Minister of Industry. https://www150.statcan.gc.ca/n1/en/pub/12-586-x/12-586-x2002001-eng.pdf?st=QDz6ld3y</ref><ref name=":4">Statistics Canada (2021a). ''Statistics Canada’s Approach to Data Stewardship'' [PDF]. Unpublished internal departmental document. </ref><ref>Wang, R.Y. and Strong, D.M. (1996) ''Beyond Accuracy: What Data Quality Means to Data Consumers''. Journal of Management Information Systems, 12, 5-33.</ref><ref>United Nations Departments of Economic and Social Affairs (2019). ''United Nations National Quality Assurance Frameworks Manual for Official Statistics'' [PDF]. https://desapublications.un.org/file/911/download</ref>

; 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.<ref name=":2" /><ref name=":3" /><ref name=":4" /><ref>Economic Commission for Europe of the United Nations (UNECE). (2000). Terminology on Statistical Metadata in ''Conference of European Statisticians Statistical Standards and Studies''. (53), Geneva. </ref>

; 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.<ref name=":2" /><ref name=":3" /><ref name=":4" /><ref>Economic Commission for Europe of the United Nations (UNECE). (2000). Terminology on Statistical Metadata in ''Conference of European Statisticians Statistical Standards and Studies''. (53), Geneva. </ref>

; 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. <ref name=":2" /><ref name=":1" /><ref>International Organization for Standardization. (2016) Data quality — Part 61: Data quality management: Process reference model (ISO standard no. 8000-61:2016) https://www.iso.org/obp/ui/#iso:std:iso:8000:-61:ed-1:v1:en

; 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. <ref name=":2" /><ref name=":1" /><ref>International Organization for Standardization. (2016) Data quality — Part 61: Data quality management: Process reference model (ISO standard no. 8000-61:2016) https://www.iso.org/obp/ui/#iso:std:iso:8000:-61:ed-1:v1:en

; 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. <ref name=":5" /><ref name=":1" /><ref name=":7" />

; 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. <ref name=":5" /><ref name=":1" /><ref name=":7" />

; 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:

; 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

::F - Findable and easily searchable

*A - Accessible and easy to use

::A - Accessible and easy to use

*I - Interoperable and more easily interpretable

::I - Interoperable and more easily interpretable

*R - Re-usable data that is easy to share and use<ref>Wilkinson, M., Dumontier, M., Aalbersberg, I. ''et al''. (2016). The FAIR Guiding Principles for scientific data management and stewardship. ''Scientific Data 3'', 160018. https://www.nature.com/articles/sdata201618</ref>.

::R - Re-usable data that is easy to share and use<ref>Wilkinson, M., Dumontier, M., Aalbersberg, I. ''et al''. (2016). The FAIR Guiding Principles for scientific data management and stewardship. ''Scientific Data 3'', 160018. https://www.nature.com/articles/sdata201618</ref>.

; 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:

; 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:

#Legal interoperability is about ensuring that organizations operating under different policies and strategies are able to work together.

#Legal interoperability is about ensuring that organizations operating under different policies and strategies are able to work together.