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Data is foundational to digital government. The Government of Canada (GC) increasingly relies on data to support the design and delivery of programs and services. Data is also a critical building block of evidence-informed policymaking, enabling the government to make measured and timely decisions that benefit all Canadians. It also supports the government’s commitment to openness and transparency, helping build public trust in digital government. Data also plays a role in advancing international cooperation and helping Canada meet its international obligations.
Data is foundational to digital government. The Government of Canada (GC) increasingly relies on data to support the design and delivery of programs and services. Data is also a critical building block of evidence-informed policymaking, enabling the government to make measured and timely decisions that benefit all Canadians. It also supports the government’s commitment to openness and transparency, helping build public trust in digital government. Data also plays a role in advancing international cooperation and helping Canada meet its international obligations.
For data to be effective and trustworthy, it needs to be fit-for-purpose. Fitness-for-purpose is an indicator of data being both usable and relevant to user needs and goals. The quality of data has a significant impact on its value to users. It influences whether data is discoverable and available to users when they need it, and the ways in which they can use or reuse data within and across organizations and jurisdictions. The prominent role of data in government operations and decision-making also highlights the importance of high-quality data not only to the government’s mandate, but also to public trust. Inaccurate or incomplete data, for example, can lead to misguided policies or biased decisions with adverse impacts on individuals, communities, or businesses. Managing the quality of data throughout the lifecycle – from acquisition to disposition or archiving – can help ensure it is fit-for-purpose, allowing users to appropriately harness its value to support their objectives. This draws on multiple roles in an organization: for example, data providers and custodians ensure data is managed to be usable, while data stewards and consumers determine its relevance within a specific use-context.
For data to be effective and trustworthy, it needs to be fit-for-purpose. Fitness-for-purpose is an indicator of data being both usable and relevant to user needs and goals.[1] The quality of data has a significant impact on its value to users. It influences whether data is discoverable and available to users when they need it, and the ways in which they can use or reuse data within and across organizations and jurisdictions. The prominent role of data in government operations and decision-making also highlights the importance of high-quality data not only to the government’s mandate, but also to public trust. Inaccurate or incomplete data, for example, can lead to misguided policies or biased decisions with adverse impacts on individuals, communities, or businesses. Managing the quality of data throughout the lifecycle – from acquisition to disposition or archiving – can help ensure it is fit-for-purpose, allowing users to appropriately harness its value to support their objectives. This draws on multiple roles in an organization: for example, data providers and custodians ensure data is managed to be usable, while data stewards and consumers determine its relevance within a specific use-context.
There is a need for a common understanding of data quality in the federal government. The current landscape includes a wide range of approaches to data quality, each developed to suit a specific type of data or organizational context. While such focused approaches serve a unique function, a shared framework with broad applicability can strengthen government-wide data governance capabilities by establishing a common vocabulary, improving coherence in data quality rules, facilitating interdepartmental data sharing and reuse, and fostering trusted data flows and ethical practices.
There is a need for a common understanding of data quality in the federal government. The current landscape includes a wide range of approaches to data quality, each developed to suit a specific type of data or organizational context. While such focused approaches serve a unique function, a shared framework with broad applicability can strengthen government-wide data governance capabilities by establishing a common vocabulary, improving coherence in data quality rules, facilitating interdepartmental data sharing and reuse, and fostering trusted data flows and ethical practices.
United Nations National Quality Assurance Frameworks Manual for Official Statistics: https://unstats.un.org/unsd/methodology/dataquality/references/1902216-UNNQAFManual-WEB.pdf
United Nations National Quality Assurance Frameworks Manual for Official Statistics: https://unstats.un.org/unsd/methodology/dataquality/references/1902216-UNNQAFManual-WEB.pdf
----[1] In this document, the term ‘user’ generally refers to a data consumer who needs high-quality data to support a policy, program, service, or other initiative in the federal government. The data can be used for the purpose for which it was initially obtained or reused for a consistent or other purpose, as permitted under privacy, security, and other applicable legislation. Users leverage the Government Data Quality Framework to identify, communicate, assess, report on, and help address issues of data quality in consultation with the appropriate stakeholders (e.g., data providers, data custodians, data policymakers, data stewards, data architects, subject matter experts, security and privacy officials).
