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, 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>

::* 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://unstats.un.org/unsd/methodology/dataquality/references/1902216-UNNQAFManual-WEB.pdf</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>