Difference between revisions of "Data Strategy for the Federal Public Service - Annexes"

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== Glossary of Terms ==
 
== Glossary of Terms ==
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# '''Data flow''': The circulation or movement of computerised data and information through interoperable systems and across organisations or geopolitical regions.<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>
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The following definitions are intended to support a common understanding of key terminology when reading the 2023-2026 Data Strategy for the Federal Public Service. They are intended to be a source of collaboration and knowledge sharing and are not official policy definitions.
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# '''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:
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# '''Data flow''': The circulation or movement of computerised data and information through interoperable systems and across organisations, 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>
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# '''Data portability:''' The capacity of digital data and information to be transmitted or circulated through interoperable applications or systems and across organisations 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, p. 45.</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>
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# '''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>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>Plotkin, D. (2021). Data Stewardship: An Actionable Guide to Effective Data Management and Data Governance (2<sup>nd</sup> 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</ref><ref name=":1">Statistics Canada. (2021b). ''Enterprise Information and Data Management Glossary'' [PDF]. Unpublished internal departmental document.  </ref>
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# '''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>Data Management Association (DAMA) (2017). DAMA-DMBOK: Data Management Body of Knowledge (2<sup>nd</sup> Ed.). Basking Ridge, NJ: Technics Publications.</ref><ref>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><ref name=":0" /><ref name=":1" />
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#'''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
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#* '''A''' - Accessible and easy to use
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#*'''A''' - Accessible and easy to use
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#* '''I''' - Interoperable and more easily interpretable
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#*'''I''' - Interoperable and more easily interpretable
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#* '''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>.
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#*'''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>.
  
 
== Domain-Specific Strategies ==
 
== Domain-Specific Strategies ==

Revision as of 16:23, 28 February 2023

The following is an evergreen list of terms and complementary strategies.


Glossary of Terms

The following definitions are intended to support a common understanding of key terminology when reading the 2023-2026 Data Strategy for the Federal Public Service. They are intended to be a source of collaboration and knowledge sharing and are not official policy definitions.

  1. Data flow: The circulation or movement of computerised data and information through interoperable systems and across organisations, geopolitical regions or jurisdictions.[1]
  2. Data portability: The capacity of digital data and information to be transmitted or circulated through interoperable applications or systems and across organisations 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.[2][3]
  3. 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 .[4][5][6][7][8][9]
  4. 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.[10][11][12][8][9]
  5. 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
    • A - Accessible and easy to use
    • I - Interoperable and more easily interpretable
    • R - Re-usable data that is easy to share and use[13].

Domain-Specific Strategies

Pan-Canadian Health Data Strategy - the strategy aims to support the effective creation, exchange, and use of health data for the benefit of Canadians and the public health systems they rely on. A collaborative approach to develop and deliver the strategy is being taken - federal/provincial/territorial co-development of the strategy is informed by the latest research findings, public health and data experts, and an Expert Advisory Group to provide guidance as the work evolves.

References

  1. ↑ 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
  2. ↑ 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, p. 45.
  3. ↑ 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
  4. ↑ Data Governance Institute. (n.d.). Governance and Decision Making. Data Governance Institute. https://datagovernance.com/governance-and-decision-making/  
  5. ↑ Organization for Economic Co-operation and Development (2008). OECD Glossary of Statistical Terms, OECD Publishing, Paris. https://doi.org/10.1787/9789264055087-en.
  6. ↑ 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.  
  7. ↑ Plotkin, D. (2021). Data Stewardship: An Actionable Guide to Effective Data Management and Data Governance (2nd Ed.). London, UK: Academic Press.
  8. ↑ 8.0 8.1 Statistics Canada. (2020b). Statistics 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
  9. ↑ 9.0 9.1 Statistics Canada. (2021b). Enterprise Information and Data Management Glossary [PDF]. Unpublished internal departmental document.  
  10. ↑ Data Management Association (DAMA) (2017). DAMA-DMBOK: Data Management Body of Knowledge (2nd Ed.). Basking Ridge, NJ: Technics Publications.
  11. ↑ 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
  12. ↑ Statistics Canada. (2020a). Data Literacy Competencies. Statistics Canada. https://www.statcan.gc.ca/en/wtc/data-literacy/compentencies
  13. ↑ 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
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