Changes

:The representation of information, in a manner suitable for storage, communication, interpretation, or processing by human beings or by automatic means, and from which knowledge can be drawn, including structured or unstructured forms. Often a set of values of subjects with respect to qualitative or quantitative variables representing facts, statistics, or items of information in a formalized manner.   

:The representation of information, in a manner suitable for storage, communication, interpretation, or processing by human beings or by automatic means, and from which knowledge can be drawn, including structured or unstructured forms. Often a set of values of subjects with respect to qualitative or quantitative variables representing facts, statistics, or items of information in a formalized manner.   

:* Statistical data refers to data used to produce official statistics (often from a census, survey statistical register or administrative source) by government agencies or other entities working on behalf of the government.

:* Statistical data refers to data used to produce official statistics (often from a census, survey statistical register or administrative source) by government agencies or other entities working on behalf of the government.

:* Administrative data refers to data and information collected by organizations, government agencies or other public entities as a part of their ongoing operations. Examples include records of births and deaths, data collected by satellites, or records about the flow of goods and people across borders.<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 (2021). Recommendation of the Council on Enhancing Access to and Sharing of Data. OECD Legal Instruments.  https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0463  </ref><ref>Statistics Canada (2016). Statistics Canada Policy on the Use of Administrative Data Obtained under the Statistics Act. Ottawa, ON: Her Majesty the Queen in Right of Canada. https://www.statcan.gc.ca/en/about/policy/admin_data </ref><ref>Statistics Canada (2023). Administrative Data. Statistics Canada. https://www.statcan.gc.ca/en/our-data/where/administrative-data  </ref><ref name=":6">Government of Canada, Treasury Board Secretariat (2019a). ''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>United Nations, Economic Commission of Europe (2000). Terminology on Statistical Metadata In Conference of European Statisticians Statistical Standards and Studies (53). Geneva, Switzerland: United Nations.</ref><ref name=":8" />

:* Administrative data refers to data and information collected by organizations, government agencies or other public entities as a part of their ongoing operations. Examples include records of births and deaths, data collected by satellites, or records about the flow of goods and people across borders.<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 (2021). Recommendation of the Council on Enhancing Access to and Sharing of Data. OECD Legal Instruments.  https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0463  </ref><ref>Statistics Canada (2016). Statistics Canada Policy on the Use of Administrative Data Obtained under the Statistics Act. Ottawa, ON: Her Majesty the Queen in Right of Canada. https://www.statcan.gc.ca/en/about/policy/admin_data </ref><ref>Statistics Canada (2023). Administrative Data. Statistics Canada. https://www.statcan.gc.ca/en/our-data/where/administrative-data  </ref><ref name=":6">Government of Canada, Treasury Board Secretariat (2019a). ''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>United Nations, Economic Commission of Europe (2000). Terminology on Statistical Metadata In Conference of European Statisticians Statistical Standards and Studies (53). Geneva, Switzerland: United Nations.</ref><ref name=":8">United Nations Departments of Economic and Social Affairs (2019). ''United Nations National Quality Assurance Frameworks Manual for Official Statistics'' [PDF]. https://unstats.un.org/unsd/methodology/dataquality/references/1902216-UNNQAFManual-WEB.pdf</ref>

=== Aggregated data ===

=== Aggregated data ===

:Unit level data that has been combined and summarized, often from multiple sources, into a collective form, often for the purposes of statistical analysis. Aggregate data allows for greater analysis and insight about particular groups based on specific variables, such as age or gender.<ref name=":9">National Collaborating Centre for Indigenous Health (2010). The Importance of Disaggregated Data. https://www.nccih.ca/docs/context/FS-ImportanceDisaggregatedData-EN.pdf </ref><ref>Strategic Data and Metadata eXchange (2020). SDMX Glossary Version 2.1. https://sdmx.org/wp-content/uploads/SDMX_Glossary_version_2_1-Final-2.docx  </ref></blockquote>

:Unit level data that has been combined and summarized, often from multiple sources, into a collective form, often for the purposes of statistical analysis. Aggregate data allows for greater analysis and insight about particular groups based on specific variables, such as age or gender.<ref name=":9">National Collaborating Centre for Indigenous Health (2010). The Importance of Disaggregated Data. https://www.nccih.ca/docs/context/FS-ImportanceDisaggregatedData-EN.pdf </ref><ref>Strategic Data and Metadata eXchange (2020). SDMX Glossary Version 2.1. https://sdmx.org/wp-content/uploads/SDMX_Glossary_version_2_1-Final-2.docx  </ref>

=== Disaggregated data ===

=== Disaggregated data ===

:Compiled or aggregate data that has been separated or broken down into smaller information units for the purposes of analysis. Disaggregated data allows for detailed analysis and insight about various subsets or outcomes within a larger data set. Data can be disaggregated by variables such as income or socio-cultural background.<ref name=":9" /></blockquote>

:Compiled or aggregate data that has been separated or broken down into smaller information units for the purposes of analysis. Disaggregated data allows for detailed analysis and insight about various subsets or outcomes within a larger data set. Data can be disaggregated by variables such as income or socio-cultural background.<ref name=":9" />

=== Data flow ===

=== Data flow ===

:The circulation or movement of computerised data and information through interoperable systems and across organizations, geopolitical regions or jurisdictions.<ref>Organisation for Economic Co-operation and Development (1985). ''Declaration on Transborder Data Flows''. OECD: Better Policies for Better Lives. https://www.oecd.org/sti/ieconomy/declarationontransborderdataflows.htm</ref>

:The circulation or movement of computerised data and information through interoperable systems and across organizations, geopolitical regions or jurisdictions.<ref>Organisation for Economic Co-operation and Development (1985). ''Declaration on Transborder Data Flows''. OECD: Better Policies for Better Lives. https://www.oecd.org/sti/ieconomy/declarationontransborderdataflows.htm</ref>

=== Data governance ===

=== 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 name=":2" /><ref>Data Governance Institute (n.d.). ''Governance and Decision Making''. Data Governance Institute. https://datagovernance.com/governance-and-decision-making/  </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=":1">Statistics Canada (2021a). ''Enterprise Information and Data Management Glossary'' [PDF]. Unpublished internal departmental document.  </ref><ref name=":10">Statistics Canada (2019). Statistics Canada Data Strategy: Delivering insight through data for a better Canada https://www.statcan.gc.ca/en/about/datastrategy</ref><ref name=":4">Statistics Canada (2021b). ''Statistics Canada’s Approach to Data Stewardship'' [PDF]. Unpublished internal departmental document. </ref>

: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 name=":2" /><ref>Data Governance Institute (n.d.). ''Governance and Decision Making''. Data Governance Institute. https://datagovernance.com/governance-and-decision-making/  </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=":1">Statistics Canada (2021). ''Enterprise Information and Data Management Glossary'' [PDF]. Unpublished internal departmental document.  </ref><ref name=":10">Statistics Canada (2019). Statistics Canada Data Strategy: Delivering insight through data for a better Canada https://www.statcan.gc.ca/en/about/datastrategy</ref><ref name=":4">Statistics Canada (2021). ''Statistics Canada’s Approach to Data Stewardship'' [PDF]. Unpublished internal departmental document. </ref>

=== Data management ===

=== Data management ===

=== Data portability ===

=== 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.<ref>Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) [2016] Official Journal of the European Union, Legislation Series 119. https://eur-lex.europa.eu/eli/reg/2016/679/oj</ref><ref>Government of Canada, Innovation, Science and Economic Development Canada (2019). ''Canada’s Digital Charter in Action: A Plan by Canadians, for Canadians.'' Ottawa, ON: Her Majesty the Queen in Right of Canada. https://ised-isde.canada.ca/site/innovation-better-canada/en/canadas-digital-charter/canadas-digital-and-data-strategy</ref></blockquote>

: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.<ref>Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) [2016] Official Journal of the European Union, Legislation Series 119. https://eur-lex.europa.eu/eli/reg/2016/679/oj</ref><ref>Government of Canada, Innovation, Science and Economic Development Canada (2019). ''Canada’s Digital Charter in Action: A Plan by Canadians, for Canadians.'' Ottawa, ON: Her Majesty the Queen in Right of Canada. https://ised-isde.canada.ca/site/innovation-better-canada/en/canadas-digital-charter/canadas-digital-and-data-strategy</ref>

=== Data quality ===

=== 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.

: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.<ref name=":1" /><ref>European Commission, Eurostat (2003). ''Assessment of quality in statistics - Definition of Quality in Statistics'', Working Group, Luxembourg, October 2003. https://ec.europa.eu/eurostat/documents/64157/4373735/02-ESS-quality-definition.pdf</ref><ref>European Commission, Eurostat (2020). 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. https://www.oecd.org/sdd/na/measuringthenon-observedeconomy-ahandbook.htm</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>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. https://doi.org/10.1080/07421222.1996.11518099</ref><ref name=":8">United Nations Departments of Economic and Social Affairs (2019). ''United Nations National Quality Assurance Frameworks Manual for Official Statistics'' [PDF]. https://unstats.un.org/unsd/methodology/dataquality/references/1902216-UNNQAFManual-WEB.pdf</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 name=":8" /><ref name=":4" /><ref>European Commission, Eurostat (2003). ''Assessment of quality in statistics - Definition of Quality in Statistics'', Working Group, Luxembourg, October 2003. https://ec.europa.eu/eurostat/documents/64157/4373735/02-ESS-quality-definition.pdf</ref><ref>European Commission, Eurostat (2020). 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. https://www.oecd.org/sdd/na/measuringthenon-observedeconomy-ahandbook.htm</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>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. https://doi.org/10.1080/07421222.1996.11518099</ref>

=== Data security ===

=== 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. https://digitallibrary.un.org/record/442455</ref>

: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=":4" /><ref name=":3" /><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. https://digitallibrary.un.org/record/442455</ref>

=== Data standards ===

=== Data standards ===

: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. <ref name=":2" /><ref name=":1" /><ref name=":7">Organisation for Economic Co-operation and Development (2018). Governing open data for sustainable results, in Open Government Data Report: Enhancing Policy Maturity for Sustainable Impact, OECD Publishing, Paris. https://read.oecd-ilibrary.org/governance/open-government-data-report/governing-open-data-for-sustainable-results_9789264305847-4-en#page1 </ref>

: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. <ref name=":2" /><ref name=":1" /><ref name=":7">Organisation for Economic Co-operation and Development (2018). Governing open data for sustainable results, in Open Government Data Report: Enhancing Policy Maturity for Sustainable Impact, OECD Publishing, Paris. https://read.oecd-ilibrary.org/governance/open-government-data-report/governing-open-data-for-sustainable-results_9789264305847-4-en#page1 </ref>

=== Domain steward ===  

=== 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. <ref name=":5" /><ref>Marco, D.P. (n.d.). Data Stewardship Roles: A Complete Guide. DataManagementU. https://www.ewsolutions.com/data-stewardship-roles-a-complete-guide/ </ref><ref>Seiner, R.S. (2007). The Data Stewardship Approach to Data Governance: Chapter 7. The Data Administration Newsletter. https://tdan.com/the-data-stewardship-approach-to-data-governance-chapter-7/6173. </ref>

:(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. <ref name=":5" /><ref>Marco, D.P. (n.d.). Data Stewardship Roles: A Complete Guide. DataManagementU. https://www.ewsolutions.com/data-stewardship-roles-a-complete-guide/ </ref><ref>Seiner, R.S. (2007). The Data Stewardship Approach to Data Governance: Chapter 7. The Data Administration Newsletter. https://tdan.com/the-data-stewardship-approach-to-data-governance-chapter-7/6173. </ref><ref>Loshin, D. (2001). Enterprise Knowledge Management: The Data Quality Approach. The Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann Publishers.   </ref><ref>Strengholt, P. (2021). Data Domains and Data Products. Towards Data Science. https://towardsdatascience.com/data-domains-and-data-products-64cc9d28283e </ref><ref>University of Washington (2023). Data Stewardship – UW’s Data Domains and Councils. University of Washington data Governance. https://datagov.uw.edu/data-stewardship/ </ref>

=== Data stewardship ===

=== 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 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=":10" /><ref name=":7" />

=== FAIR Data Principles ===

=== 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:

: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:

:# Semantic interoperability is about ensuring consistent meaning and optimal comparability of data with the use of conceptual models, vocabularies and ontologies.

:# Semantic interoperability is about ensuring consistent meaning and optimal comparability of data with the use of conceptual models, vocabularies and ontologies.

:# Syntactic interoperability is about format. It allows us to explicitly define the common representations and exchange models.

:# Syntactic interoperability is about format. It allows us to explicitly define the common representations and exchange models.

:# System interoperability is about defining the infrastructure and communication protocols to be used during the exchange process.<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_canada_data_strategy.pdf Statistics Canada Data Strategy (statcan.gc.ca)]</ref><ref name=":6" /> <ref>European Commission (2017a). European Political Strategy Centre, Enter the data economy: EU policies for a thriving data ecosystem. Publications Office 21:11. https://data.europa.eu/doi/10.2872/33746 </ref><ref>European Commission (2017b). European Interoperability Framework. Luxembourg: Publications Office of the European Union. https://ec.europa.eu/isa2/sites/default/files/eif_brochure_final.pdf</ref><ref>Data Documentation Initiative Alliance (2021). ''DDI Alliance Glossary''. DDI Alliance. https://ddialliance.org/resources/ddi-glossary </ref>

:# System interoperability is about defining the infrastructure and communication protocols to be used during the exchange process.<ref name=":6" /><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_canada_data_strategy.pdf Statistics Canada Data Strategy (statcan.gc.ca)]</ref><ref>European Commission (2017a). European Political Strategy Centre, Enter the data economy: EU policies for a thriving data ecosystem. Publications Office 21:11. https://data.europa.eu/doi/10.2872/33746 </ref><ref>European Commission (2017b). European Interoperability Framework. Luxembourg: Publications Office of the European Union. https://ec.europa.eu/isa2/sites/default/files/eif_brochure_final.pdf</ref><ref>Data Documentation Initiative Alliance (2021). ''DDI Alliance Glossary''. DDI Alliance. https://ddialliance.org/resources/ddi-glossary </ref><ref>Chapurlat, V., Daclin N. (2012). System interoperability: definition and proposition of interface model in MBSE Context. IFAC Proceedings Volumes, 45(6), 1523-1528. https://www.sciencedirect.com/science/article/pii/S1474667016333675 </ref>

=== Privacy ===

=== 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.<ref name=":2" /><ref name=":0" /><ref name=":4" /><ref>Government of Canada, Treasury Board Secretariat (2019). Directive on Privacy Practices. Ottawa, ON: Her Majesty the Queen in Right of Canada. https://www.tbs-sct.canada.ca/pol/doc-eng.aspx?id=18309 </ref>

: 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.<ref name=":2" /><ref name=":0" /><ref name=":4" /><ref>Government of Canada, Treasury Board Secretariat (2019). Directive on Privacy Practices. Ottawa, ON: Her Majesty the Queen in Right of Canada. https://www.tbs-sct.canada.ca/pol/doc-eng.aspx?id=18309 </ref>