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.

:* 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" /><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" /><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" />

 

=== Data flow ===

=== Data flow ===

=== 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>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_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>

: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_canada_data_strategy.pdf Statistics Canada Data Strategy (statcan.gc.ca)]</ref><ref name=":1">Statistics Canada (2021a). ''Enterprise Information and Data Management Glossary'' [PDF]. Unpublished internal departmental document.  </ref>

=== Data management ===

=== 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>

: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 (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>Statistics Canada (2020a). ''Data Literacy Competencies''. Statistics Canada. https://www.statcan.gc.ca/en/wtc/data-literacy/compentencies </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>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 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. https://doi.org/10.1080/07421222.1996.11518099</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>

::* 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. 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 name=":4">Statistics Canada (2021b). ''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. 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>

=== Data security ===

=== Data security ===