Changes

=== 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>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, Eurostat (2003). ''Assessment of quality in statistics - Definition of Quality in Statistics'', Working Group, Luxembourg, October 2003.</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.  </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 ===

=== 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>

: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>

: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" />

:(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>

=== 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=":7" />

:# System interoperability is about defining the infrastructure and communication protocols to be used during the exchange process.<ref name=":0" /><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=":0" /><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>

=== 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" />

: 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. (2019b). 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>

== Domain-Specific Strategies ==

== Domain-Specific Strategies ==