Changes

[[File:DataGoviconn.jpg|left]]

[[File:DataGoviconn.jpg|left]]

Strong data governance is foundational to developing an ISED data culture that treats data as a strategic asset. Data governance means having data-related leadership, with clearly defined roles, responsibilities and accountabilities with decision-making authority within a structure to govern data. Data standards for quality and metadata, as well as data management processes throughout the data lifecycle are also critical components of data governance.

Strong data governance is foundational to developing an ISED data culture that treats data as a strategic asset. Data governance means having data-related leadership, with clearly defined roles, responsibilities and accountabilities with decision-making authority within a structure to govern data. Data standards for quality and metadata, as well as data management processes throughout the data lifecycle are also critical components of data governance.

=== [[Data Access Pillar|<big>Data Access</big>]] ===

=== [[Data Access Pillar|<big>Data Access</big>]] ===

[[File:Dataaccessicon.jpg|left]]

[[File:Dataaccessicon.jpg|left]]

Data access is the building block for leveraging existing information to improve decision-making and service delivery, as well as for accelerating partnerships within the department and between all levels of government. ISED data holdings must be discoverable, usable and shareable. This includes the need to develop a common consent statement for sharing to facilitate greater future access and interoperability across different data holdings.

Data access is the building block for leveraging existing information to improve decision-making and service delivery, as well as for accelerating partnerships within the department and between all levels of government. ISED data holdings must be discoverable, usable and shareable. This includes the need to develop a common consent statement for sharing to facilitate greater future access and interoperability across different data holdings.

[[File:DataFrameicon.jpg|left]]

[[File:DataFrameicon.jpg|left]]

Understanding how data flows within ISED, including upholding the ethical treatment of sensitive and private information is essential to how data is managed and leverage. Data models, aligned to departmental priorities, will ensure standardization of key concepts and will result in high quality data for use at the enterprise level.

Understanding how data flows within ISED, including upholding the ethical treatment of sensitive and private information is essential to how data is managed and leverage. Data models, aligned to departmental priorities, will ensure standardization of key concepts and will result in high quality data for use at the enterprise level.

[[File:Talenticonn.jpg|left]]

[[File:Talenticonn.jpg|left]]

To enable a data culture, ISED must have the talent and capacity it needs to manage, interpret, use and understand data. To start, knowing the current state of data literacy and skills gaps will help identify strategic training, recruitment and retention requirements to transform a workforce that has the data skills needed to reach ISED data goals.

To enable a data culture, ISED must have the talent and capacity it needs to manage, interpret, use and understand data. To start, knowing the current state of data literacy and skills gaps will help identify strategic training, recruitment and retention requirements to transform a workforce that has the data skills needed to reach ISED data goals.

[[File:Innovationicon.jpg|left]]

[[File:Innovationicon.jpg|left]]

Fostering innovation will help propel ISED to continue being a leader in helping Canadians and businesses be competitive in their current and future work. Time and resources dedicated and available for experimentation, with particular focus on those projects that are replicable and scalable, will ensure that data are leveraged to their greatest potential.

Fostering innovation will help propel ISED to continue being a leader in helping Canadians and businesses be competitive in their current and future work. Time and resources dedicated and available for experimentation, with particular focus on those projects that are replicable and scalable, will ensure that data are leveraged to their greatest potential.