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

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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 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<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 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 Unpublished internal departmental document.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>
 
; 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<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 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>
 
; 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<sup>nd</sup> 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>
 
; 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<sup>nd</sup> 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>
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;Data quality :The ‘quality’ of data refers to its fitness for purpose, often measured by such criteria offered 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.
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;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.
 
::* 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 (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>
 
; 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>
 
; 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>

Revision as of 10:00, 2 March 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.

Data flow
The circulation or movement of computerised data and information through interoperable systems and across organisations, geopolitical regions or jurisdictions.[1]
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]
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]
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.[8][9][10][11][12]
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.
  • 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.[13][14][15][16][17][18][19][20]
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.[5][10][18][21]
Data standards
Data standards are the rules and specifications by which data are described, defined and recorded. In order to share, exchange, and understand data, standardized formats and meanings are needed. Examples of data standards include data models, reference data, identifier schemas, and statistical standards. The use of data standards enables the integration of data over time and across different data sources, as well as reduces the resource requirements associated with many aspects of survey development and maintenance. [5][9][22][23]
Data steward
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. [5][9][24]
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. [7]
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. [7][9][24]
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[25].
Interoperability
Interoperability is the ability to access, process and exchange data from multiple sources, then integrate that data for mapping, visualization, and other forms of meaningful representation and analysis. This allows systems and organizations to work together (inter-operate) towards mutually beneficial goals by sharing information and exchanging data. In order to be interoperable, data should follow established data standards to ensure that it is easily compared over time, across jurisdictions, and within and between departments. There are five key layers of interoperability:
  1. Legal interoperability is about ensuring that organizations operating under different policies and strategies are able to work together.
  2. Operational interoperability is about ensuring how organizations align their business processes, responsibilities and expectations to achieve mutual beneficial outcomes.
  3. Semantic interoperability is about ensuring consistent meaning and optimal comparability of data with the use of conceptual models, vocabularies and ontologies.
  4. Syntactic interoperability is about format. It allows us to explicitly define the common representations and exchange models.
  5. System interoperability is about defining the infrastructure and communication protocols to be used during the exchange process.[8][11] [26][27][28]
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.[5][8][18]

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. ↑ 5.0 5.1 5.2 5.3 5.4 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. ↑ 7.0 7.1 7.2 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 8.2 8.3 Statistics Canada. (2020b). Statistics Canada Data Strategy: Delivering insight through data for a better  Canada [PDF]. Statistics Canada Data Strategy (statcan.gc.ca)
  9. ↑ 9.0 9.1 9.2 9.3 9.4 Statistics Canada. (2021b). Enterprise Information and Data Management Glossary [PDF]. Unpublished internal departmental document.  
  10. ↑ 10.0 10.1 Data Management Association (DAMA) (2017). DAMA-DMBOK: Data Management Body of Knowledge (2nd Ed.). Basking Ridge, NJ: Technics Publications.
  11. ↑ 11.0 11.1 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. ↑ European Commission (2003). Eurostat. Assessment of quality in statistics - Definition of Quality in Statistics, Working Group, Luxembourg, October 2003.
  14. ↑ 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  
  15. ↑ Government of Canada. (2022). GC Data Quality Framework.https://wiki.gccollab.ca/GC_Data_Quality_Framework#Background
  16. ↑ Organisation for Economic Co-operation and Development (2002). Measuring the Non-Observed Economy: A Handbook. Paris, France: OECD Publications.  
  17. ↑ 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
  18. ↑ 18.0 18.1 18.2 Statistics Canada (2021a). Statistics Canada’s Approach to Data Stewardship [PDF]. Unpublished internal departmental document.
  19. ↑ 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.
  20. ↑ 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
  21. ↑ 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.
  22. ↑ International Organization for Standardization. (2016) Data quality — Part 61: Data quality management: Process reference model (ISO standard no. 8000-61:2016) https://www.iso.org/obp/ui/#iso:std:iso:8000:-61:ed-1:v1:en
  23. ↑ Standards Council of Canada. (2020). What Are Standards? Standards Council of Canada. https://www.scc.ca/en/standards/what-are-standards
  24. ↑ 24.0 24.1 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
  25. ↑ 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
  26. ↑ 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
  27. ↑ 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
  28. ↑ Data Documentation Initiative Alliance (2021). DDI Alliance Glossary. DDI Alliance. https://ddialliance.org/resources/ddi-glossary
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