Changes

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.

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.

Data quality is also being prioritized within federal departments and agencies. Many departmental data strategies developed following the publication of the Data Strategy Roadmap identify data quality as an organizational priority and list planned or existing efforts aimed at managing it effectively. Further, the TB Directive on Service and Digital requires departmental CIOs and other designated officials to ensure that “information and data are managed to enable data interoperability, reuse and sharing to the greatest extent possible within and with other departments across the government to avoid duplication and maximize utility, while respecting security and privacy requirements” (subsection 4.3.1.3).

Data quality is also being prioritized within federal departments and agencies. Many departmental data strategies developed following the publication of the Data Strategy Roadmap identify data quality as an organizational priority and list planned or existing efforts aimed at managing it effectively. Further, the TB Directive on Service and Digital requires departmental CIOs and other designated officials to ensure that “information and data are managed to enable data interoperability, reuse and sharing to the greatest extent possible within and with other departments across the government to avoid duplication and maximize utility, while respecting security and privacy requirements” (subsection 4.3.1.3).

The concern with data quality also extends to automated decision systems, which rely on data to perform their functions. The TB Directive on Automated Decision-Making requires federal organizations to validate the quality of data collected for and used by automated decision systems (subsections 6.3.1, 6.3.3). The Algorithmic Impact Assessment, a risk assessment tool that supports the Directive by determining the impact level of an automated decision system, also accounts for this by asking users to identify processes for testing bias in data. Taken together, these measures are part of a broader move towards treating information and data as strategic assets “to support government operations, service delivery, analysis and decision-making” (Policy subsection 4.3.2.1).

The concern with data quality also extends to automated decision systems, which rely on data to perform their functions. The TB Directive on Automated Decision-Making requires federal organizations to validate the quality of data collected for and used by automated decision systems (subsections 6.3.1, 6.3.3). The Algorithmic Impact Assessment, a risk assessment tool that supports the Directive by determining the impact level of an automated decision system, also accounts for this by asking users to identify processes for testing bias in data. Taken together, these measures are part of a broader move towards treating information and data as strategic assets “to support government operations, service delivery, analysis and decision-making” (Policy subsection 4.3.2.1).