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| | === Mission: === | | === Mission: === |
| | By providing employees with the data, skills and tools they need, we will achieve excellence in serving Canadians and Canadian businesses. | | By providing employees with the data, skills and tools they need, we will achieve excellence in serving Canadians and Canadian businesses. |
| | + | {| |
| | + | | |
| | + | ==== Business Drivers ==== |
| | + | * Enhanced service delivery |
| | + | * Evidence based policies, research and evaluation - |
| | + | * Strengthened reporting capacity and story telling |
| | + | * Enriched internal services |
| | + | |
| | + | * Improved regulation and enforcement |
| | + | | |
| | + | ==== '''Goals''' ==== |
| | + | * Canadians and Canadian businesses are better informed and served |
| | + | * ISED adopts a data culture where data are discoverable, accessible, secure and of high quality |
| | + | * ISED's talent base is enhanced with new skills and experimentation is promoted |
| | + | * Public trust is honoured by ensuring that data are handled ethically and securely |
| | + | |} |
| | | | |
| | + | ==== What are we doing? ==== |
| | + | There are six pillars of the ISED Data Strategy, each of which has high level initiatives in three phases of implementation; laying the foundation, building the momentum and adopting a data culture. The following table addresses each pillar by phase of implementation. |
| | {| | | {| |
| | ! | | ! |
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| | !Adopting a data culture | | !Adopting a data culture |
| | |- | | |- |
| − | | | + | |'''Data governance''' |
| − | |Establishing ISED data-related leadership, enabled by launching the Chief Data Office and data governance structure, and identifying key data stewards & champions and the planning processes to manage data. | + | _____________ |
| − | ________________________
| + | |'''Data-related leadership established''' |
| − | |Promoting a culture shift enabled by establishing data steward and champion networks and implementing and overseeing data processes.
| + | * Chief Data Office |
| | + | * Data Governance Structure |
| | + | * Identify key data stewards & champions |
| | + | * Identify processes to manage data at enterprise level (sharing & storing protocols) |
| | | | |
| | _______________________ | | _______________________ |
| − | |People value their data and treat it as an asset, enabled by monitoring the adoption of data processes and standards, and ensuring data access is facilitated by data stewards
| + | |'''Culture shift across ISED''' |
| − | ______________________
| + | * Data Steward and Champion network established |
| − | |-
| + | * Implement & oversee data processes |
| − | |'''Establish a Structure''' | |
| − | '''to Govern Data'''
| |
| − | | |
| − | _____________
| |
| − | |'''Formalize departmental data governance structure'''
| |
| − | * ISED data governance structure is created, with defined roles, responsibilities and accountabilities established and broadly communicated.
| |
| − | * Oversight for ethical use and management of data at all levels. Data stewards, individually and collectively, operationalize the use of data, data standards, and make data discoverable, with key issues raised to SDDOC for resolution and decision making; with strategic direction and guidance provided by DMC. | |
| − | * Chief Data Office champions overall implementation of ISED Data Strategy providing support to advance data and analytics capabilities.
| |
| | | | |
| | _______________________ | | _______________________ |
| − | |'''Democratize governance over data''' | + | |'''People value their data and treat it as an asset''' |
| − | * Strong understanding of the roles and accountabilities of operational, strategic and executive levels of governance, and solid methods for interacting between groups and the Chief Data Office. | + | * Monitor adoption of data processes & standards aligned with GoC |
| − | * Data strategy implementation undertaken with key partner collaboration (e.g., pillar and topic-specific working groups), building on the relationship between data and digital. Implementation is governed within the established departmental data governance structure.
| + | * Data Stewards facilitate access to data |
| − | * Performance indicators to measure success of strategy implementation defined and communicated.
| |
| − | _______________________
| |
| − | |'''Data is formally governed at ISED'''
| |
| − | * Data-related guidelines are embedded in departmental processes and structures, with associated data decisions taken at appropriate levels.
| |
| − | * Department wide management and governance of data aligns with ISED Data Strategy vision and drivers, and Federal initiatives. | |
| − | * Performance indicators tracked to demonstrate progress and value of investment.
| |
| | | | |
| | _______________________ | | _______________________ |
| | |- | | |- |
| − | | rowspan="2" |'''Operationalize'''
| + | |'''Data Access''' |
| − | '''Data Governance'''
| |
| − | | |
| − | '''via Data Stewardship'''
| |
| − | | |
| | _____________ | | _____________ |
| − | |'''Identify data stewards''' | + | |'''Data access challenges are well understood''' |
| − | * Chief Data Office identifies possible data stewardship models for ISED, acknowledging diversity of sectors. Model for departmental data stewardship developed and implemented. Collaborating with the CDO, sectors nominate data stewards based on identified data steward model. | + | * Inventory & valuation of data assets |
| − | * Data stewards made aware of their role and provided with targeted training, supported by the CDO. | + | * Inventory data sharing agreements |
| − | * CDO conducts initial identification of existing data-related guidelines and processes to manage data at the departmental level (i.e., data sharing and storing protocols, data standards, data lifecycle management and data quality). | + | * Assessment of legislative & policy framework for data sharing |
| | + | _______________________ |
| | |'''Form data steward network''' | | |'''Form data steward network''' |
| | * Establish data steward network for sharing knowledge, best practices and expertise related to data. | | * Establish data steward network for sharing knowledge, best practices and expertise related to data. |
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| | * Data Steward Network regularly meets and raises any implementation challenges with the CDO. | | * Data Steward Network regularly meets and raises any implementation challenges with the CDO. |
| | |- | | |- |
| − | |'''Identify departmental oversight''' '''over data''' | + | |'''Data Framework''' |
| − | * Chief Data Office works with key members of the ISED Data Governance Community to raise awareness of the departmental data governance structure, and the need for programs to consider data-related issues and concerns in projects and processes.
| + | _____________ |
| − | * CDO works across the department to determine data governance implementation required for various data-related processes and initiatives under the Data Access, Data Framework, Talent, Innovation, and Technology pillars of the ISED Data Strategy.
| + | | |
| − | | + | | |
| − | ______________________
| + | | |
| − | |'''Implement governance over departmental data''' | + | |- |
| − | * Employ the identified governance of departmental data initiatives, ensuring that decisions on data are made at the appropriate level through departmental data governance channels.
| + | |'''Talent''' |
| − | | + | _____________ |
| − | _________________________
| + | | |
| − | |'''Monitor adherence to data governance''' | + | | |
| − | * Data-related considerations are built into departmental initiatives, strategies and processes, and are governed appropriately at the right level through the right channels.
| + | | |
| − | * Data standards are adhered to and monitored regularly across ISED. Data are managed ethically and supported by strong data quality frameworks.
| + | |- |
| − | * ISED staff respect data processes and standards and are accountable through the formal governance structure. Skills and competencies related to data are developed and needed tools are available.
| + | |'''Innovation''' |
| − | ________________________
| + | _____________ |
| | + | | |
| | + | | |
| | + | | |
| | + | |- |
| | + | |'''Technology''' |
| | + | _____________ |
| | + | | |
| | + | | |
| | + | | |
| | |} | | |} |
| − | Business Drivers:
| |
| − | • Enhanced service delivery
| |
| − | • Evidence based policies, research and evaluation
| |
| − | • Strengthened reporting capacity and story telling
| |
| − | • Enriched internal services
| |
| − | • Improved regulation and enforcement
| |
| − | Goals:
| |
| − | • Canadians and Canadian businesses are better informed and served
| |
| − | • ISED adopts a data culture where data are discoverable, accessible, secure and of high quality
| |
| − | • ISED's talent base is enhanced with new skills and experimentation is promoted
| |
| − | • Public trust is honoured by ensuring that data are handled ethically and securely
| |
| − | What are we doing?
| |
| − | There are six pillars of the ISED Data Strategy, each of which has high level initiatives in three phases of implementation; laying the foundation, building the momentum and adopting a data culture. The following table addresses each pillar by phase of implementation.
| |
| − |
| |
| − | Laying the foundation Building the momentum Adopting a data culture
| |
| − | Data governance Data-related leadership established
| |
| − | • Chief Data Office
| |
| − | • Data Governance Structure
| |
| − | • Identify key data stewards & champions
| |
| − | • Identify processes to manage data at enterprise level (sharing & storing protocols) Culture shift across ISED
| |
| − | • Data Steward and Champion network established
| |
| − | • Implement & oversee data processes People value their data and treat it as an asset
| |
| − |
| |
| − | • Monitor adoption of data processes & standards aligned with GoC
| |
| − | • Data Stewards facilitate access to data
| |
| − | Data Access Data access challenges are well understood
| |
| − | • Inventory & valuation of data assets
| |
| − | • Inventory data sharing agreements
| |
| − | • Assessment of legislative & policy framework for data sharing Work on transformative data access initiatives
| |
| − | • Develop common consent statement for data sharing | Create roadmap for a data sharing hub
| |
| − | • Partner with Sectors to pilot data sharing and data integration Investigate data sharing opportunities across all levels of government ISED data are open by default
| |
| − | • Launch common consent statement for data sharing & monitor data sharing
| |
| − | • Deploy self-service data sharing hub
| |
| − | • Expand data sharing to all levels of government
| |
| − | Data Framework Data framework and models are developed
| |
| − | • Data standards, including Common Business Profile and dictionaries, developed and piloted Framework for ethical, secure use & storage of data developed
| |
| − | • Detailed data models for collection, acquisition, processing and storage conceptualized and piloted Put in place data framework and models
| |
| − | • Process to handle and use data are known
| |
| − | • Quality assurance standards developed Data standards launched
| |
| − | • Framework for ethical & secure data use implemented
| |
| − | • Data models in place across the department Protection of data via privacy by design
| |
| − | • Staff confidently conduct work with high quality data, with well-established data standards, definitions, and the privacy and security of Canada's data assured
| |
| − | Talent Baseline and identify skills gaps
| |
| − | • Identify business needs
| |
| − | • Assess data literacy
| |
| − | • Identify data-related learning and development Our workforce begins to transform
| |
| − | • Develop career path & data competencies
| |
| − | • Upskill and retrain new and existing staff
| |
| − | • Recruitment strategy based on required data skills We have trained people to reach our goals
| |
| − | • Ongoing recruitment & development
| |
| − | • Talent retention initiative
| |
| − | • Data as core competency for career development
| |
| − | Innovation Foundation for change is established
| |
| − | • Early opportunities identified
| |
| − | • First data analytics pilots undertaken
| |
| − | • Success stories shared Experimentation begins to yield results
| |
| − | • Roll-out successful pilots to other sectors
| |
| − | • Continue to communicate approaches and use cases
| |
| − | • Identify mechanism for making decisions on proposed innovative solutions Innovation becomes common business practice
| |
| − | • Initiate departmental analytics support
| |
| − | • Develop Free Agent data talent matching service (data-skilled talent pool for short-term work)
| |
| − | • Establish data science pipeline
| |
| − | Technology Higher organizational awareness of existing solutions
| |
| − | • Determine technology requirements
| |
| − | • Establish technology strategy
| |
| − | • Experiment with technology solutions for data (management, sharing, creation, data analytics)
| |
| − | • Roadmap for Client Relationship Management (CRM) New tools and processes put in place
| |
| − | • Implement technology strategy
| |
| − | • Business processes for use of new technologies
| |
| − | • On-site storage, common data software suite
| |
| − | • Business analytics tools available Internal technology keeps pace with innovation
| |
| − | • Integrated suite of IT tools for data and analytics
| |
| − | • Departmental Client Relationship Management with Common Business Profile
| |
| − | • Continual evaluation of Next Generation technology with new tech use on demand
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